add kick test script

.gitignore

@@ -15,7 +15,6 @@ dist/
 *.npz
 *.xml
 *.json
-*.yaml
 *.iml
 *.pyc
 .TXT

agent/agent.py

@@ -6,7 +6,6 @@ import numpy as np
 from utils.math_ops import MathOps
 from world.commons.field import FIFAField, HLAdultField, Soccer7vs7Field
 from world.commons.play_mode import PlayModeEnum, PlayModeGroupEnum
-from agent.path_planner import PathPlanner
 
 
 logger = logging.getLogger()
@@ -50,6 +49,9 @@ class Agent:
         }
     }
 
+    TEAMMATE_INFO_MAX_AGE: float = 0.8
+    SUPPORT_DISTANCE_FROM_BALL: float = 1.2
+
     def __init__(self, agent):
         """
         Creates a new DecisionMaker linked to the given agent.
@@ -61,7 +63,10 @@ class Agent:
 
         self.agent: Base_Agent = agent
         self.is_getting_up: bool = False
-        self.path_planner = PathPlanner(agent.world)
+        self.is_kicking: bool = False
+        self.current_shot_target_point: np.ndarray | None = None
+        self.target_point: np.ndarray = np.array([0.0, 0.0])
+        self.my_pos: np.ndarray = self.agent.world.global_position[:2]
 
     def update_current_behavior(self) -> None:
         """
@@ -87,11 +92,19 @@ class Agent:
             self.is_getting_up = not self.agent.skills_manager.execute(skill_name="GetUp")
 
         elif self.agent.world.playmode is PlayModeEnum.PLAY_ON:
-            self.carry_ball()
+            active_player_number = self.get_active_player_number()
+            if active_player_number == self.agent.world.number:
+                # self.carry_ball()
+                self.target_point = self.my_pos + np.array([4.0, 0.0])
+                self.kick_ball()
+            else:
+                # self.execute_support_behavior()
+                self.agent.skills_manager.execute("Neutral")
         elif self.agent.world.playmode in (PlayModeEnum.BEFORE_KICK_OFF, PlayModeEnum.THEIR_GOAL, PlayModeEnum.OUR_GOAL):
             self.agent.skills_manager.execute("Neutral")
         else:
-            self.carry_ball()
+            # self.execute_support_behavior()
+            self.kick_ball()
 
         self.agent.robot.commit_motor_targets_pd()
 
@@ -99,19 +112,21 @@ class Agent:
         """
         Basic example of a behavior: moves the robot toward the goal while handling the ball.
         """
-        their_goal_pos = np.array(self.agent.world.field.get_their_goal_position()[:2])
+        target_point = self.target_point
         ball_pos = self.agent.world.ball_pos[:2]
         my_pos = self.agent.world.global_position[:2]
-        next_target = None
 
-        ball_to_goal = their_goal_pos - ball_pos
-        bg_norm = np.linalg.norm(ball_to_goal)
-        if bg_norm == 0:
+        if self.kick_ball(target_point=target_point):
             return
-        ball_to_goal_dir = ball_to_goal / bg_norm
+
+        ball_to_target = target_point - ball_pos
+        bt_norm = np.linalg.norm(ball_to_target)
+        if bt_norm == 0:
+            return
+        ball_to_target_dir = ball_to_target / bt_norm
 
         dist_from_ball_to_start_carrying = 0.30
-        carry_ball_pos = ball_pos - ball_to_goal_dir * dist_from_ball_to_start_carrying
+        carry_ball_pos = ball_pos - ball_to_target_dir * dist_from_ball_to_start_carrying
 
         my_to_ball = ball_pos - my_pos
         my_to_ball_norm = np.linalg.norm(my_to_ball)
@@ -120,22 +135,17 @@ class Agent:
         else:
             my_to_ball_dir = my_to_ball / my_to_ball_norm
 
-        cosang = np.dot(my_to_ball_dir, ball_to_goal_dir)
+        cosang = np.dot(my_to_ball_dir, ball_to_target_dir)
         cosang = np.clip(cosang, -1.0, 1.0)
         angle_diff = np.arccos(cosang)
 
         ANGLE_TOL = np.deg2rad(7.5)
         aligned = (my_to_ball_norm > 1e-6) and (angle_diff <= ANGLE_TOL)
 
-        behind_ball = np.dot(my_pos - ball_pos, ball_to_goal_dir) < 0
-        desired_orientation = MathOps.vector_angle(ball_to_goal)
+        behind_ball = np.dot(my_pos - ball_pos, ball_to_target_dir) < 0
+        desired_orientation = MathOps.vector_angle(ball_to_target)
 
         if not aligned or not behind_ball:
-            self.path_planner.set_target(carry_ball_pos)
-            current_time = self.agent.world.server_time
-            next_target = self.path_planner.update(my_pos, current_time=current_time)
-            if next_target is None:
-                next_target = carry_ball_pos
             self.agent.skills_manager.execute(
                 "Walk",
                 target_2d=carry_ball_pos,
@@ -143,15 +153,142 @@ class Agent:
                 orientation=None if np.linalg.norm(my_pos - carry_ball_pos) > 2 else desired_orientation
             )
         else:
-            self.path_planner.set_target(their_goal_pos)
-            current_time = self.agent.world.server_time
-            next_target = self.path_planner.update(my_pos, current_time=current_time)
-            if next_target is None:
-                next_target = their_goal_pos
             self.agent.skills_manager.execute(
                 "Walk",
-                target_2d=their_goal_pos,
+                target_2d=target_point,
                 is_target_absolute=True,
                 orientation=desired_orientation
             )
 
+    def kick_ball(self, target_point=None):
+        if target_point is None:
+            target_point = self.target_point
+
+        target_point = np.asarray(target_point, dtype=float)[:2]
+        ball_pos = self.agent.world.ball_pos[:2]
+        my_pos = self.agent.world.global_position[:2]
+
+        ball_to_target = target_point - ball_pos
+        ball_to_target_dist = np.linalg.norm(ball_to_target)
+        if ball_to_target_dist <= 1e-6:
+            return False
+        if self.is_kicking:
+            if self.current_shot_target_point is None:
+                self.current_shot_target_point = target_point
+
+            active_target = self.current_shot_target_point
+            active_distance = np.linalg.norm(active_target - ball_pos)
+            self.is_kicking = not self.agent.skills_manager.execute(
+                "Kick", distance=active_distance
+            )
+            if not self.is_kicking:
+                self.current_shot_target_point = None
+            return True
+
+        my_to_ball = ball_pos - my_pos
+        my_to_ball_dist = np.linalg.norm(my_to_ball)
+
+        ball_to_target_dir = ball_to_target / ball_to_target_dist
+        behind_ball = np.dot(my_pos - ball_pos, ball_to_target_dir) < 0
+        me_to_ball_dir = ball_pos - my_pos
+        me_to_ball_dir_norm = np.linalg.norm(me_to_ball_dir)
+
+        desired_orientation = MathOps.vector_angle(me_to_ball_dir)
+        current_orientation = self.agent.robot.global_orientation_euler[2]
+        orientation_error = abs(
+            MathOps.normalize_deg(desired_orientation - current_orientation)
+        )
+
+        lateral_error_to_shot_line, _ = MathOps.distance_point_to_line(
+            p=my_pos,
+            a=ball_pos,
+            b=target_point,
+        )
+
+        kick_offset = MathOps.rotate_2d_vec(np.array([0.0, -0.1]), desired_orientation)
+
+        close_to_ball = my_to_ball_dist <= 0.24
+        good_orientation = orientation_error <= 8.0
+        good_lateral_offset = lateral_error_to_shot_line <= 0.16
+
+        can_shoot = (
+            close_to_ball
+            and behind_ball
+            and good_orientation
+            and good_lateral_offset
+        )
+
+        if not can_shoot:
+            prepare_offset = 0.1
+            prepare_pos = ball_pos - ball_to_target_dir * prepare_offset + kick_offset
+
+            self.agent.skills_manager.execute(
+                "Walk",
+                target_2d=prepare_pos,
+                is_target_absolute=True,
+                orientation=desired_orientation,
+            )
+            return True
+
+        self.current_shot_target_point = target_point
+        self.is_kicking = not self.agent.skills_manager.execute(
+            "Kick", distance=ball_to_target_dist
+        )
+        if not self.is_kicking:
+            self.current_shot_target_point = None
+        return True
+
+    def get_active_player_number(self) -> int:
+        world = self.agent.world
+        ball_pos = world.ball_pos[:2]
+        server_time = world.server_time
+
+        my_num = world.number
+        my_pos = world.global_position[:2]
+        best_player_number = my_num
+        best_distance = np.linalg.norm(my_pos - ball_pos)
+
+        for teammate_number, teammate in enumerate(world.our_team_players, start=1):
+            if teammate_number == my_num:
+                continue
+
+            if teammate.last_seen_time is None or server_time is None:
+                continue
+
+            info_age = server_time - teammate.last_seen_time
+            if info_age > self.TEAMMATE_INFO_MAX_AGE:
+                continue
+
+            teammate_pos = teammate.position[:2]
+            teammate_distance = np.linalg.norm(teammate_pos - ball_pos)
+
+            if teammate_distance + 1e-6 < best_distance:
+                best_distance = teammate_distance
+                best_player_number = teammate_number
+            elif abs(teammate_distance - best_distance) <= 1e-6:
+                best_player_number = min(best_player_number, teammate_number)
+
+        return best_player_number
+
+    def execute_support_behavior(self) -> None:
+        world = self.agent.world
+        my_pos = world.global_position[:2]
+        ball_pos = world.ball_pos[:2]
+        target_point = self.target_point
+
+        ball_to_target = target_point - ball_pos
+        bt_norm = np.linalg.norm(ball_to_target)
+        if bt_norm <= 1e-6:
+            ball_to_target_dir = np.array([1.0, 0.0])
+        else:
+            ball_to_target_dir = ball_to_target / bt_norm
+
+        support_pos = ball_pos - ball_to_target_dir * self.SUPPORT_DISTANCE_FROM_BALL
+        support_orientation = MathOps.vector_angle(ball_pos - my_pos)
+
+        self.agent.skills_manager.execute(
+            "Walk",
+            target_2d=support_pos,
+            is_target_absolute=True,
+            orientation=support_orientation,
+        )

agent/path_planner.py

@@ -1,520 +0,0 @@
-import numpy as np
-from heapq import heappush, heappop
-from typing import List, Tuple, Optional
-from world.world import World  # 假设你的世界类
-from world.commons.field_landmarks import FieldLandmarks  # 假设你的地标类
-import time
-
-
-class PathPlanner:
-    """
-    足球机器人全局路径规划器（A* + 动态避障）
-    - 静态地图：边界硬墙，球门内部可通行，门柱硬墙
-    - 动态障碍物：对手球员（扩大半径 + 缓冲代价）
-    - 支持多目标点队列，自动切换
-    - 不预测对手运动，仅使用当前帧位置
-    """
-
-    def __init__(self, world, grid_resolution: float = 0.2):
-        """
-        :param world: 世界对象，需包含 field_landmarks 和 global_position 属性
-        :param grid_resolution: 栅格分辨率（米/格）
-        """
-        self.world = world
-        self.res = grid_resolution
-
-        # 球场参数（基于 Sim3D7vs7SoccerField）
-        self.field_half_len = 27.5      # 球场半长（不含球门深度）
-        self.field_half_width = 18.0    # 球场半宽
-        self.goal_width = 3.66          # 球门宽度
-        self.goal_depth = 1.0           # 球门深度
-        self.goal_half_width = self.goal_width / 2.0
-        self.post_radius = 0.05         # 门柱半径
-
-        # 机器人物理参数
-        self.robot_radius = 0.2         # 机器人半径（米）
-        self.safety_margin = 0.2        # 避障安全余量（代替预测）
-
-        # 栅格尺寸
-        # x 范围扩展以包含球门内部：[-field_half_len - goal_depth, field_half_len + goal_depth]
-        self.x_min = -self.field_half_len - self.goal_depth
-        self.x_max = self.field_half_len + self.goal_depth
-        self.y_min = -self.field_half_width
-        self.y_max = self.field_half_width
-        self.nx = int((self.x_max - self.x_min) / self.res) + 1
-        self.ny = int((self.y_max - self.y_min) / self.res) + 1
-
-        # 静态代价地图（初始化后不再改变）
-        self.static_cost_map = np.zeros((self.nx, self.ny), dtype=np.float32)
-        self._init_static_map()
-
-        # 多目标点管理
-        self.waypoints: List[np.ndarray] = []   # 目标点列表
-        self.current_wp_idx: int = 0
-        self.current_path: List[np.ndarray] = []   # 当前规划的完整路径（世界坐标）
-        self.replan_interval: float = 1.0          # 重新规划频率（Hz）
-        self.last_replan_time: float = 0.0
-        self.arrival_threshold: float = 0.3        # 到达目标的距离阈值（米）
-
-    # ---------- 坐标转换辅助 ----------
-    def _world_to_grid(self, x: float, y: float) -> Tuple[int, int]:
-        """世界坐标 -> 栅格坐标（边界裁剪）"""
-        ix = int((x - self.x_min) / self.res)
-        iy = int((y - self.y_min) / self.res)
-        ix = max(0, min(ix, self.nx - 1))
-        iy = max(0, min(iy, self.ny - 1))
-        return ix, iy
-
-    def _grid_to_world(self, ix: int, iy: int) -> Tuple[float, float]:
-        """栅格坐标 -> 世界坐标（中心点）"""
-        x = ix * self.res + self.x_min
-        y = iy * self.res + self.y_min
-        return x, y
-
-    def _world_to_grid_x(self, x: float) -> int:
-        return int((x - self.x_min) / self.res)
-
-    def _world_to_grid_y(self, y: float) -> int:
-        return int((y - self.y_min) / self.res)
-
-    def _grid_to_world_x(self, ix: int) -> float:
-        return ix * self.res + self.x_min
-
-    def _grid_to_world_y(self, iy: int) -> float:
-        return iy * self.res + self.y_min
-
-    # ---------- 静态地图生成 ----------
-    def _init_static_map(self):
-        """根据 Sim3D7vs7SoccerField 参数生成静态代价地图"""
-        # 球场参数
-        field_half_len = 27.5      # 球场半长（不含球门深度）
-        field_half_width = 18.0
-        goal_width = 3.66
-        goal_depth = 1.0
-        goal_half_width = goal_width / 2.0
-        post_radius = 0.05
-
-        # 1. 初始化全地图为 0（自由空间）
-        self.static_cost_map.fill(0.0)
-
-        # 2. 边界硬墙
-        # 左侧边界：x < -field_half_len 的区域，但保留球门开口（|y| <= goal_half_width 时球门内部可通行）
-        for i in range(self._world_to_grid_x(-field_half_len - 0.001), -1, -1):
-            for j in range(self.ny):
-                y = self._grid_to_world_y(j)
-                if abs(y) > goal_half_width:
-                    self.static_cost_map[i, j] = -3
-        # 右侧边界：x > field_half_len 的区域，保留球门开口
-        for i in range(self._world_to_grid_x(field_half_len + 0.001), self.nx):
-            for j in range(self.ny):
-                y = self._grid_to_world_y(j)
-                if abs(y) > goal_half_width:
-                    self.static_cost_map[i, j] = -3
-        # 上下边界
-        for i in range(self.nx):
-            for j in [0, self.ny-1]:
-                self.static_cost_map[i, j] = -3
-        # 可选：如果需要在边界内留出线宽，可额外处理
-
-        # 4. 门柱（作为硬墙）
-        goal_post_positions = [
-            
-            (field_half_len,  goal_half_width),  # 右侧上柱
-            (field_half_len,  -goal_half_width),  # 右侧下柱
-            (-field_half_len,  goal_half_width),  # 左侧上柱
-            (-field_half_len,  -goal_half_width),  # 左侧下柱
-        ]
-        for px, py in goal_post_positions:
-            ix, iy = self._world_to_grid(px, py)
-            rad_cells = int(post_radius / self.res) + 1
-            for dx in range(-rad_cells, rad_cells+1):
-                for dy in range(-rad_cells, rad_cells+1):
-                    nx, ny = ix + dx, iy + dy
-                    if not (0 <= nx < self.nx and 0 <= ny < self.ny):
-                        continue
-                    dist = np.hypot(dx * self.res, dy * self.res)
-                    if dist <= post_radius:
-                        self.static_cost_map[nx, ny] = -3
-
-    # ---------- 获取动态障碍物（对手球员） ----------
-    def _get_opponent_positions(self) -> List[np.ndarray]:
-        """从 FieldLandmarks 获取所有对手球员的全局位置"""
-        opponents = []
-        for player in self.world.their_team_players:
-            if player.last_seen_time is not None and (self.world.server_time - player.last_seen_time) < 1.0:
-                opponents.append(player.position[:2])  # 只使用 x, y
-            else:
-                # 长时间未看到的球员不添加到避障列表中
-                continue  # 跳过未看到的球员
-        return opponents
-
-    def _get_teammate_positions(self) -> List[np.ndarray]:
-        """从 FieldLandmarks 获取所有队友球员的全局位置"""
-        teammates = []
-        for player in self.world.our_team_players:
-            if player.last_seen_time is not None and (self.world.server_time - player.last_seen_time) < 1.0:
-                teammates.append(player.position[:2])  # 只使用 x, y
-            else:
-                # 长时间未看到的球员不添加到避障列表中
-                continue  # 跳过未看到的球员
-        return teammates
-    # ---------- 动态障碍物添加到代价地图 ----------
-    def _apply_opponents_to_map(self, cost_map: np.ndarray, opponents: List[np.ndarray]):
-        """
-        在动态代价地图上添加对手障碍物：
-        - 硬半径内：-3（不可通行）
-        - 缓冲区内：正代价（鼓励远离）
-        """
-        effective_radius = self.robot_radius + self.safety_margin
-        radius_cells = int(effective_radius / self.res) + 1
-        buffer_width = 0.2   # 缓冲区宽度（米）
-
-        for opp in opponents:
-            if opp is None:
-                continue
-            ox, oy = opp[0], opp[1]
-            ix, iy = self._world_to_grid(ox, oy)
-            for dx in range(-radius_cells, radius_cells + 1):
-                for dy in range(-radius_cells, radius_cells + 1):
-                    nx, ny = ix + dx, iy + dy
-                    if not (0 <= nx < self.nx and 0 <= ny < self.ny):
-                        continue
-                    dist = np.hypot(dx * self.res, dy * self.res)
-                    if dist <= effective_radius:
-                        cost_map[nx, ny] = -3   # 硬墙
-                    elif dist <= effective_radius + buffer_width:
-                        # 缓冲区内增加代价（线性衰减）
-                        if cost_map[nx, ny] >= 0:
-                            cost_map[nx, ny] += 10.0 * (1.0 - (dist - effective_radius) / buffer_width)
-
-    def _apply_teammate_to_map(self, cost_map, teammates):
-        soft_radius = self.robot_radius + 0.2   # 允许稍微接近
-        for tm in teammates:
-            ix, iy = self._world_to_grid(tm[0], tm[1])
-            rad_cells = int(soft_radius / self.res) + 1
-            for dx in range(-rad_cells, rad_cells+1):
-                for dy in range(-rad_cells, rad_cells+1):
-                    nx, ny = ix+dx, iy+dy
-                    if not (0 <= nx < self.nx and 0 <= ny < self.ny):
-                        continue
-                    dist = np.hypot(dx*self.res, dy*self.res)
-                    if dist <= soft_radius:
-                        # 不设为硬墙，只加较小代价
-                        if cost_map[nx, ny] >= 0:
-                            cost_map[nx, ny] += 5.0   # 代价比对手小
-
-    # ---------- 启发函数 ----------
-    def _heuristic(self, ix1: int, iy1: int, ix2: int, iy2: int) -> float:
-        """对角线距离（允许8方向移动）"""
-        dx = abs(ix1 - ix2)
-        dy = abs(iy1 - iy2)
-        return (dx + dy) - 0.585786 * min(dx, dy)   # sqrt(2)-1 ≈ 0.414
-
-    # ---------- 直线检测 ----------
-    def _line_is_free(self, start: np.ndarray, end: np.ndarray, opponents: List[np.ndarray]) -> bool:
-        """检查线段是否与任何对手（扩大后）相交"""
-        effective_radius = self.robot_radius + self.safety_margin
-        ax, ay = start
-        bx, by = end
-        abx = bx - ax
-        aby = by - ay
-        len_sq = abx * abx + aby * aby
-        if len_sq == 0:
-            return True
-
-        for opp in opponents:
-            if opp is None:
-                continue
-            ox, oy = opp[0], opp[1]
-            # 计算投影参数 t
-            t = ((ox - ax) * abx + (oy - ay) * aby) / len_sq
-            if t < 0:
-                t = 0
-            elif t > 1:
-                t = 1
-            closest_x = ax + t * abx
-            closest_y = ay + t * aby
-            dist = np.hypot(closest_x - ox, closest_y - oy)
-            if dist <= effective_radius:
-                return False
-        return True
-
-    # ---------- 路径重构 ----------
-    def _reconstruct_path(self, node: Tuple[int, int], parent: dict, start: np.ndarray) -> List[np.ndarray]:
-        """从父字典重构路径（世界坐标）"""
-        path = []
-        cur = node
-        while cur in parent:
-            x, y = self._grid_to_world(cur[0], cur[1])
-            path.append(np.array([x, y]))
-            cur = parent[cur]
-        path.append(start)
-        path.reverse()
-        return path
-
-    # ---------- A* 规划 ----------
-    def plan(self, start: np.ndarray, target: Optional[np.ndarray] = None,
-         go_to_goal: bool = False, timeout_ms: float = 10.0) -> List[np.ndarray]:
-        """
-        A* 路径规划
-        :param start: 起点 (x, y)
-        :param target: 目标点（当 go_to_goal=False 时使用）
-        :param go_to_goal: 是否前往对方球门
-        :param timeout_ms: 超时时间（毫秒）
-        :return: 路径点列表（世界坐标），若失败返回空列表
-        """
-        # 1. 获取队友并构建动态代价地图
-        teammates = self._get_teammate_positions()
-        cost_map = self.static_cost_map.copy()
-        self._apply_teammate_to_map(cost_map, teammates)
-        opponents = self._get_opponent_positions()
-        self._apply_opponents_to_map(cost_map, opponents)
-
-        # 2. 转换坐标
-        sx, sy = self._world_to_grid(start[0], start[1])
-        if go_to_goal:
-            # 目标点为球门线上 y=0 附近的格子
-            goal_x = self.field_half_len
-            goal_y = 0.0
-            tx, ty = self._world_to_grid(goal_x, goal_y)
-        else:
-            if target is None:
-                raise ValueError("target must be provided when go_to_goal=False")
-            tx, ty = self._world_to_grid(target[0], target[1])
-
-        # 3. 【关键修改】强制将目标点格子设为 -1（覆盖障碍物）
-        if go_to_goal:
-            # 球门线上所有格子都设为 -1（增加容错）
-            goal_line_cell = self._world_to_grid_x(self.field_half_len)
-            y_min_cell = self._world_to_grid_y(-self.goal_half_width)
-            y_max_cell = self._world_to_grid_y(self.goal_half_width)
-            for j in range(y_min_cell, y_max_cell + 1):
-                if 0 <= goal_line_cell < self.nx and 0 <= j < self.ny:
-                    cost_map[goal_line_cell, j] = -1
-        else:
-            if 0 <= tx < self.nx and 0 <= ty < self.ny:
-                cost_map[tx, ty] = -1
-
-        # 4. 快速直线检测（可选，可提高效率）
-        if go_to_goal:
-            end_point = np.array([goal_x, goal_y])
-        else:
-            end_point = target
-        if self._line_is_free(start, end_point, opponents):
-            # 直线无碰撞，直接返回
-            return [start, end_point]
-
-        # 5. A* 初始化
-        open_set = []
-        closed = np.zeros((self.nx, self.ny), dtype=bool)
-        g = np.full((self.nx, self.ny), np.inf)
-        f = np.full((self.nx, self.ny), np.inf)
-        parent = {}   # (ix, iy) -> (pix, piy)
-
-        g[sx, sy] = 0.0
-        f[sx, sy] = self._heuristic(sx, sy, tx, ty)
-        heappush(open_set, (f[sx, sy], sx, sy))
-
-        # 记录最佳节点（用于超时/不可达回退）
-        best_node = (sx, sy)
-        best_h = self._heuristic(sx, sy, tx, ty)
-
-        start_time = time.time()
-        iterations = 0
-
-        # 邻居方向（8方向）及其移动代价
-        dirs = [(-1, -1, 1.414), (-1, 0, 1.0), (-1, 1, 1.414),
-                (0, -1, 1.0),                 (0, 1, 1.0),
-                (1, -1, 1.414),  (1, 0, 1.0), (1, 1, 1.414)]
-
-        while open_set:
-            iterations += 1
-            # 超时检查
-            if iterations % 32 == 0 and (time.time() - start_time) * 1000 > timeout_ms:
-                # 超时，返回最佳节点路径（如果有）
-                if best_node != (sx, sy):
-                    return self._reconstruct_path(best_node, parent, start)
-                else:
-                    return []
-
-            _, cx, cy = heappop(open_set)
-            if closed[cx, cy]:
-                continue
-            closed[cx, cy] = True
-
-            # 更新最佳节点（基于启发式距离）
-            h = self._heuristic(cx, cy, tx, ty)
-            if h < best_h:
-                best_h = h
-                best_node = (cx, cy)
-
-            # 到达目标
-            if (cx, cy) == (tx, ty) or (go_to_goal and cost_map[cx, cy] == -1):
-                return self._reconstruct_path((cx, cy), parent, start)
-
-            # 扩展邻居
-            for dx, dy, step_cost in dirs:
-                nx, ny = cx + dx, cy + dy
-                if not (0 <= nx < self.nx and 0 <= ny < self.ny):
-                    continue
-                if closed[nx, ny]:
-                    continue
-
-                cell_cost = cost_map[nx, ny]
-                if cell_cost == -3:   # 硬墙
-                    continue
-
-                move_cost = step_cost + max(0.0, cell_cost)
-                tentative_g = g[cx, cy] + move_cost
-
-                if tentative_g < g[nx, ny]:
-                    parent[(nx, ny)] = (cx, cy)
-                    g[nx, ny] = tentative_g
-                    f[nx, ny] = tentative_g + self._heuristic(nx, ny, tx, ty)
-                    heappush(open_set, (f[nx, ny], nx, ny))
-
-        # open 集为空，不可达
-        if best_node != (sx, sy):
-            return self._reconstruct_path(best_node, parent, start)
-        else:
-            return []
-
-    # ---------- 多目标点管理 ----------
-    def set_waypoints(self, waypoints: List[np.ndarray]):
-        """
-        设置目标点序列（世界坐标）。如果某个元素为 None，表示前往对方球门。
-        """
-        self.waypoints = [np.array(wp) if wp is not None else None for wp in waypoints]
-        self.current_wp_idx = 0
-        self.current_path = []
-        self.last_replan_time = 0.0
-
-    def get_next_target(self) -> Optional[np.ndarray]:
-        """返回当前需要前往的目标点（世界坐标）"""
-        if self.current_wp_idx >= len(self.waypoints):
-            return None
-        wp = self.waypoints[self.current_wp_idx]
-        if wp is None:
-            # 前往球门
-            return np.array([self.field_half_len, 0.0])
-        return wp
-
-    def advance_to_next_target(self):
-        """标记当前目标已完成，切换到下一个"""
-        if self.current_wp_idx < len(self.waypoints):
-            self.current_wp_idx += 1
-            self.current_path = []   # 清空旧路径
-
-    def update(self, current_pos: np.ndarray, current_time: float) -> Optional[np.ndarray]:
-        """
-        更新路径规划，返回下一个需要前往的路径点。
-        :param current_pos: 机器人当前世界坐标 (x, y)
-        :param current_time: 当前时间（秒），用于可选的重规划频率控制
-        :return: 下一个路径点（世界坐标），若无有效目标则返回 None
-        """
-        # 1. 获取当前需要前往的目标点
-        target = self.get_next_target()
-        if target is None:
-            # 没有剩余目标，停止移动
-            return None
-
-        # 2. 到达检测：如果已有路径且路径终点距离机器人很近，则认为已到达当前目标
-        if len(self.current_path) >= 2:
-            last_point = self.current_path[-1]
-            if np.linalg.norm(last_point - current_pos) < self.arrival_threshold:
-                # 当前目标已完成，切换到下一个目标
-                self.advance_to_next_target()
-                target = self.get_next_target()
-                if target is None:
-                    return None
-                # 清空旧路径，强制下次重规划到新目标
-                self.current_path = []
-
-        # 3. 路径有效性检查（仅当存在有效路径时）
-        path_valid = (len(self.current_path) >= 2) and self._is_path_still_valid(current_pos)
-
-        # 4. 判断是否需要重新规划
-        #   条件1：当前路径为空（包括刚切换目标后）
-        #   条件2：当前路径被障碍物阻塞
-        need_replan = (len(self.current_path) < 2) or not path_valid
-
-        if need_replan:
-            # 重新规划到当前目标
-            new_path = self.plan(current_pos,
-                                target=target if target is not None else None,
-                                go_to_goal=(target is None),
-                                timeout_ms=10.0)
-            if new_path and len(new_path) > 1:
-                self.current_path = new_path
-                self.last_replan_time = current_time
-            else:
-                # 当前目标不可达（规划失败），跳过它，尝试下一个
-                self.advance_to_next_target()
-                # 递归调用，继续处理下一个目标（避免深度过大，但目标数量有限）
-                return self.update(current_pos, current_time)
-
-        # 5. 返回下一个路径点（路径的第二个点，第一个点为机器人当前位置的近似）
-        if len(self.current_path) >= 2:
-            return self.current_path[1]
-        else:
-            return None
-        
-    def _is_path_still_valid(self, current_pos: np.ndarray, lookahead_dist: float = 3.0) -> bool:
-        """
-        检查从机器人当前位置开始的剩余路径是否仍无碰撞（仅检查前方 lookahead_dist 米内）。
-        :param current_pos: 机器人当前世界坐标 (x, y)
-        :param lookahead_dist: 检查的前向距离（米），默认 3.0
-        :return: 如果路径在前方范围内无碰撞返回 True，否则 False
-        """
-        if len(self.current_path) < 2:
-            return False
-
-        # 获取对手最新位置
-        opponents = self._get_opponent_positions()
-
-        # 累积距离
-        accumulated_dist = 0.0
-        # 从机器人当前位置到路径第二个点的第一段
-        start = current_pos
-        end = self.current_path[1]
-        seg_dist = np.linalg.norm(end - start)
-        if not self._line_is_free(start, end, opponents):
-            return False
-        accumulated_dist += seg_dist
-        # 如果第一段就已经超过阈值，直接返回 True（已检查第一段无碰撞）
-        if accumulated_dist >= lookahead_dist:
-            return True
-
-        # 继续检查后续路径段，直到累积距离超过阈值
-        for i in range(1, len(self.current_path) - 1):
-            start = self.current_path[i]
-            end = self.current_path[i+1]
-            seg_dist = np.linalg.norm(end - start)
-            if not self._line_is_free(start, end, opponents):
-                return False
-            accumulated_dist += seg_dist
-            if accumulated_dist >= lookahead_dist:
-                break
-
-        return True
-    
-    def set_target(self, target: np.ndarray, force: bool = False):
-        """
-        设置单目标点（世界坐标）。
-        :param target: 新目标点
-        :param force: 是否强制更新（即使目标相同或距离很近）
-        """
-        # 获取当前有效目标（如果存在）
-        current_target = self.get_next_target()
-        if current_target is not None and not force:
-            # 计算新目标与当前目标的欧氏距离
-            dist = np.linalg.norm(target - current_target)
-            if dist < 0.2:   # 阈值可调（例如 0.2 米）
-                # 目标没有显著变化，不更新
-                return
-
-        # 目标变化显著，或强制更新
-        self.waypoints = [target]
-        self.current_wp_idx = 0
-        self.current_path = []          # 清空旧路径，触发重规划
-        self.last_replan_time = 0.0

behaviors/behavior_manager.py

@@ -1,4 +1,5 @@
 from behaviors.custom.keyframe.get_up.get_up import GetUp
+from behaviors.custom.keyframe.kick.kick import Kick
 from behaviors.custom.keyframe.keyframe import KeyframeSkill
 from behaviors.custom.keyframe.poses.neutral.neutral import Neutral
 from behaviors.behavior import Behavior
@@ -22,7 +23,7 @@ class BehaviorManager:
         Each skill is indexed by its class name.
         """
 
-        classes: list[type[Behavior]] = [Walk, Neutral, GetUp]
+        classes: list[type[Behavior]] = [Walk, Neutral, GetUp, Kick]
 
         # instantiate each Skill and store in the skills dictionary
         self.skills = {cls.__name__: cls(agent=self.agent) for cls in classes}

behaviors/custom/keyframe/kick/kick.py

@@ -0,0 +1,91 @@
+import os
+from numbers import Real
+
+from behaviors.behavior import Behavior
+from behaviors.custom.keyframe.keyframe import KeyframeSkill
+
+
+class Kick(Behavior):
+    SHORT_MAX_DISTANCE: float = 1.8
+    MID_MAX_DISTANCE: float = 4.5
+    TEN_METER_MIN_DISTANCE: float = 8.0
+    DEFAULT_DISTANCE: float = 3.0
+
+    def __init__(self, agent):
+        super().__init__(agent)
+
+        base_dir = os.path.dirname(__file__)
+        self.short_kick = KeyframeSkill(
+            agent=agent,
+            file=os.path.join(base_dir, "kick_short.yaml"),
+        )
+        self.mid_kick = KeyframeSkill(
+            agent=agent,
+            file=os.path.join(base_dir, "kick_mid.yaml"),
+        )
+        self.long_kick = KeyframeSkill(
+            agent=agent,
+            file=os.path.join(base_dir, "kick_long.yaml"),
+        )
+        self.ten_meter_kick = KeyframeSkill(
+            agent=agent,
+            file=os.path.join(base_dir, "kick_10m.yaml"),
+        )
+
+        self.current_kick: KeyframeSkill | None = None
+        self.should_reset_kick: bool = True
+        self.should_execute_neutral: bool = True
+
+    def execute(self, reset: bool, *args, **kwargs) -> bool:
+        if reset:
+            kick_distance = self._extract_kick_distance(*args, **kwargs)
+            self.current_kick = self._choose_kick_from_distance(kick_distance)
+            self.should_reset_kick = True
+            self.should_execute_neutral = True
+
+        if self.current_kick is None:
+            self.current_kick = self.mid_kick
+
+        if self.should_execute_neutral:
+            neutral_finished = self.agent.skills_manager.execute(
+                "Neutral",
+            )
+            self.should_reset_kick = False
+            if not neutral_finished:
+                return False
+            self.should_execute_neutral = False
+            self.should_reset_kick = True
+
+        has_finished = self.current_kick.execute(reset=self.should_reset_kick)
+        self.should_reset_kick = False
+        return has_finished
+
+    def _extract_kick_distance(self, *args, **kwargs) -> float:
+        distance_candidates = (
+            kwargs.get("distance"),
+            kwargs.get("kick_distance"),
+            kwargs.get("target_distance"),
+            kwargs.get("ball_to_target_distance"),
+            args[0] if args else None,
+        )
+
+        for candidate in distance_candidates:
+            if isinstance(candidate, Real):
+                return float(max(0.0, candidate))
+
+        return self.DEFAULT_DISTANCE
+
+    def _choose_kick_from_distance(self, distance: float) -> KeyframeSkill:
+        if distance <= self.SHORT_MAX_DISTANCE:
+            return self.short_kick
+
+        if distance <= self.MID_MAX_DISTANCE:
+            return self.mid_kick
+
+        if distance >= self.TEN_METER_MIN_DISTANCE:
+            return self.ten_meter_kick
+
+        return self.long_kick
+
+    def is_ready(self, *args) -> bool:
+        return True

behaviors/custom/keyframe/kick/kick_10m.yaml

@@ -0,0 +1,187 @@
+symmetry: false
+kp: 175
+kd: 1.4
+keyframes:
+  - delta: 0.00
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 68.000
+      Left_Shoulder_Roll: -76.000
+      Left_Elbow_Pitch: -62.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 52.000
+      Right_Elbow_Pitch: -72.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -26.000
+      Left_Hip_Roll: 8.000
+      Left_Hip_Yaw: 0.000
+      Left_Knee_Pitch: 62.000
+      Left_Ankle_Pitch: 12.000
+      Left_Ankle_Roll: 3.000
+      Right_Hip_Pitch: 14.000
+      Right_Hip_Roll: -10.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -10.000
+      Right_Ankle_Pitch: 6.000
+      Right_Ankle_Roll: -5.000
+
+  - delta: 0.23
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 70.000
+      Left_Shoulder_Roll: -80.000
+      Left_Elbow_Pitch: -70.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 50.000
+      Right_Elbow_Pitch: -72.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -40.000
+      Left_Hip_Roll: 10.000
+      Left_Hip_Yaw: 2.500
+      Left_Knee_Pitch: 64.000
+      Left_Ankle_Pitch: 10.000
+      Left_Ankle_Roll: 4.500
+      Right_Hip_Pitch: 14.000
+      Right_Hip_Roll: -10.000
+      Right_Hip_Yaw: -3.000
+      Right_Knee_Pitch: -10.000
+      Right_Ankle_Pitch: 6.000
+      Right_Ankle_Roll: -6.000
+
+  - delta: 0.26
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 72.000
+      Left_Shoulder_Roll: -82.000
+      Left_Elbow_Pitch: -74.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 48.000
+      Right_Elbow_Pitch: -68.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -56.000
+      Left_Hip_Roll: 12.000
+      Left_Hip_Yaw: 5.000
+      Left_Knee_Pitch: 34.000
+      Left_Ankle_Pitch: 6.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 14.000
+      Right_Hip_Roll: -10.000
+      Right_Hip_Yaw: -6.000
+      Right_Knee_Pitch: -8.000
+      Right_Ankle_Pitch: 5.000
+      Right_Ankle_Roll: -7.000
+
+  - delta: 0.10
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 74.000
+      Left_Shoulder_Roll: -86.000
+      Left_Elbow_Pitch: -76.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 44.000
+      Right_Elbow_Pitch: -64.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -66.000
+      Left_Hip_Roll: 13.000
+      Left_Hip_Yaw: 5.500
+      Left_Knee_Pitch: -12.000
+      Left_Ankle_Pitch: 4.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 12.000
+      Right_Hip_Roll: -9.000
+      Right_Hip_Yaw: -2.500
+      Right_Knee_Pitch: -6.000
+      Right_Ankle_Pitch: 4.000
+      Right_Ankle_Roll: -6.000
+    kp: 210
+
+  - delta: 0.23
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 72.000
+      Left_Shoulder_Roll: -82.000
+      Left_Elbow_Pitch: -72.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 46.000
+      Right_Elbow_Pitch: -66.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -24.000
+      Left_Hip_Roll: 10.000
+      Left_Hip_Yaw: 3.000
+      Left_Knee_Pitch: 46.000
+      Left_Ankle_Pitch: 12.500
+      Left_Ankle_Roll: 4.500
+      Right_Hip_Pitch: 14.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -10.000
+      Right_Ankle_Pitch: 6.000
+      Right_Ankle_Roll: -4.000
+
+  - delta: 0.23
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 70.000
+      Left_Shoulder_Roll: -80.000
+      Left_Elbow_Pitch: -68.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 48.000
+      Right_Elbow_Pitch: -68.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -18.000
+      Left_Hip_Roll: 10.000
+      Left_Hip_Yaw: 1.000
+      Left_Knee_Pitch: 38.000
+      Left_Ankle_Pitch: 10.000
+      Left_Ankle_Roll: 3.000
+      Right_Hip_Pitch: 12.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -6.000
+      Right_Ankle_Pitch: 4.000
+      Right_Ankle_Roll: -4.000
+
+  - delta: 0.34
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 64.000
+      Left_Shoulder_Roll: -72.000
+      Left_Elbow_Pitch: -58.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 68.000
+      Right_Shoulder_Roll: 45.000
+      Right_Elbow_Pitch: -64.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: 0.000
+      Left_Hip_Roll: 0.000
+      Left_Hip_Yaw: 0.000
+      Left_Knee_Pitch: 0.000
+      Left_Ankle_Pitch: 0.000
+      Left_Ankle_Roll: 0.000
+      Right_Hip_Pitch: 0.000
+      Right_Hip_Roll: 0.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: 0.000
+      Right_Ankle_Pitch: 0.000
+      Right_Ankle_Roll: 0.000
+    kp: 120

behaviors/custom/keyframe/kick/kick_long.yaml

@@ -0,0 +1,213 @@
+symmetry: false
+kp: 160
+kd: 1.3
+keyframes:
+  - delta: 0.00
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 68.000
+      Left_Shoulder_Roll: -76.000
+      Left_Elbow_Pitch: -62.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 52.000
+      Right_Elbow_Pitch: -72.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -18.000
+      Left_Hip_Roll: 8.000
+      Left_Hip_Yaw: 0.000
+      Left_Knee_Pitch: 38.000
+      Left_Ankle_Pitch: 12.000
+      Left_Ankle_Roll: 3.000
+      Right_Hip_Pitch: 24.000
+      Right_Hip_Roll: -10.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -38.000
+      Right_Ankle_Pitch: 10.000
+      Right_Ankle_Roll: -5.000
+
+  - delta: 0.20
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 70.000
+      Left_Shoulder_Roll: -80.000
+      Left_Elbow_Pitch: -70.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 50.000
+      Right_Elbow_Pitch: -72.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -30.000
+      Left_Hip_Roll: 10.000
+      Left_Hip_Yaw: 2.000
+      Left_Knee_Pitch: 24.000
+      Left_Ankle_Pitch: 10.000
+      Left_Ankle_Roll: 4.500
+      Right_Hip_Pitch: 26.000
+      Right_Hip_Roll: -10.000
+      Right_Hip_Yaw: -3.000
+      Right_Knee_Pitch: -40.000
+      Right_Ankle_Pitch: 10.000
+      Right_Ankle_Roll: -6.000
+
+  - delta: 0.21
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 72.000
+      Left_Shoulder_Roll: -82.000
+      Left_Elbow_Pitch: -74.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 48.000
+      Right_Elbow_Pitch: -68.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -48.000
+      Left_Hip_Roll: 12.000
+      Left_Hip_Yaw: 3.000
+      Left_Knee_Pitch: 4.000
+      Left_Ankle_Pitch: 4.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 24.000
+      Right_Hip_Roll: -10.000
+      Right_Hip_Yaw: -4.000
+      Right_Knee_Pitch: -36.000
+      Right_Ankle_Pitch: 8.000
+      Right_Ankle_Roll: -7.000
+
+  - delta: 0.12
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 74.000
+      Left_Shoulder_Roll: -86.000
+      Left_Elbow_Pitch: -76.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 44.000
+      Right_Elbow_Pitch: -64.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -56.000
+      Left_Hip_Roll: 13.000
+      Left_Hip_Yaw: 4.000
+      Left_Knee_Pitch: -4.000
+      Left_Ankle_Pitch: 2.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 22.000
+      Right_Hip_Roll: -9.000
+      Right_Hip_Yaw: -1.500
+      Right_Knee_Pitch: -28.000
+      Right_Ankle_Pitch: 6.000
+      Right_Ankle_Roll: -6.000
+
+  - delta: 0.12
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 76.000
+      Left_Shoulder_Roll: -88.000
+      Left_Elbow_Pitch: -78.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 42.000
+      Right_Elbow_Pitch: -62.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -60.000
+      Left_Hip_Roll: 14.000
+      Left_Hip_Yaw: 5.000
+      Left_Knee_Pitch: -8.000
+      Left_Ankle_Pitch: 0.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 20.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: -2.000
+      Right_Knee_Pitch: -24.000
+      Right_Ankle_Pitch: 4.000
+      Right_Ankle_Roll: -5.000
+    kp: 190
+
+  - delta: 0.21
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 72.000
+      Left_Shoulder_Roll: -82.000
+      Left_Elbow_Pitch: -72.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 46.000
+      Right_Elbow_Pitch: -66.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -22.000
+      Left_Hip_Roll: 10.000
+      Left_Hip_Yaw: 3.000
+      Left_Knee_Pitch: 16.000
+      Left_Ankle_Pitch: 11.500
+      Left_Ankle_Roll: 4.500
+      Right_Hip_Pitch: 24.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -34.000
+      Right_Ankle_Pitch: 8.000
+      Right_Ankle_Roll: -4.000
+
+  - delta: 0.21
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 70.000
+      Left_Shoulder_Roll: -80.000
+      Left_Elbow_Pitch: -68.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 48.000
+      Right_Elbow_Pitch: -68.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -18.000
+      Left_Hip_Roll: 10.000
+      Left_Hip_Yaw: 1.000
+      Left_Knee_Pitch: 12.000
+      Left_Ankle_Pitch: 10.000
+      Left_Ankle_Roll: 3.000
+      Right_Hip_Pitch: 24.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -34.000
+      Right_Ankle_Pitch: 8.000
+      Right_Ankle_Roll: -4.000
+
+  - delta: 0.31
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 64.000
+      Left_Shoulder_Roll: -72.000
+      Left_Elbow_Pitch: -58.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 68.000
+      Right_Shoulder_Roll: 45.000
+      Right_Elbow_Pitch: -64.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: 0.000
+      Left_Hip_Roll: 0.000
+      Left_Hip_Yaw: 0.000
+      Left_Knee_Pitch: 0.000
+      Left_Ankle_Pitch: 0.000
+      Left_Ankle_Roll: 0.000
+      Right_Hip_Pitch: 20.000
+      Right_Hip_Roll: -6.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -26.000
+      Right_Ankle_Pitch: 6.000
+      Right_Ankle_Roll: 0.000
+    kp: 120

behaviors/custom/keyframe/kick/kick_mid.yaml

@@ -0,0 +1,213 @@
+symmetry: false
+kp: 150
+kd: 1.2
+keyframes:
+  - delta: 0.00
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 68.000
+      Left_Shoulder_Roll: -76.000
+      Left_Elbow_Pitch: -62.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 52.000
+      Right_Elbow_Pitch: -72.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -16.000
+      Left_Hip_Roll: 8.000
+      Left_Hip_Yaw: 0.000
+      Left_Knee_Pitch: 36.000
+      Left_Ankle_Pitch: 12.000
+      Left_Ankle_Roll: 4.000
+      Right_Hip_Pitch: 24.000
+      Right_Hip_Roll: -10.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -36.000
+      Right_Ankle_Pitch: 9.000
+      Right_Ankle_Roll: -5.000
+
+  - delta: 0.17
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 70.000
+      Left_Shoulder_Roll: -80.000
+      Left_Elbow_Pitch: -70.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 50.000
+      Right_Elbow_Pitch: -72.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -28.000
+      Left_Hip_Roll: 10.000
+      Left_Hip_Yaw: 1.500
+      Left_Knee_Pitch: 24.000
+      Left_Ankle_Pitch: 10.000
+      Left_Ankle_Roll: 4.500
+      Right_Hip_Pitch: 26.000
+      Right_Hip_Roll: -10.000
+      Right_Hip_Yaw: -2.000
+      Right_Knee_Pitch: -38.000
+      Right_Ankle_Pitch: 10.000
+      Right_Ankle_Roll: -6.000
+
+  - delta: 0.18
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 72.000
+      Left_Shoulder_Roll: -82.000
+      Left_Elbow_Pitch: -74.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 48.000
+      Right_Elbow_Pitch: -68.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -44.000
+      Left_Hip_Roll: 12.000
+      Left_Hip_Yaw: 3.000
+      Left_Knee_Pitch: 6.000
+      Left_Ankle_Pitch: 5.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 24.000
+      Right_Hip_Roll: -10.000
+      Right_Hip_Yaw: -4.000
+      Right_Knee_Pitch: -36.000
+      Right_Ankle_Pitch: 8.000
+      Right_Ankle_Roll: -7.000
+
+  - delta: 0.12
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 74.000
+      Left_Shoulder_Roll: -84.000
+      Left_Elbow_Pitch: -76.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 46.000
+      Right_Elbow_Pitch: -66.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -52.000
+      Left_Hip_Roll: 13.000
+      Left_Hip_Yaw: 3.500
+      Left_Knee_Pitch: -2.000
+      Left_Ankle_Pitch: 2.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 24.000
+      Right_Hip_Roll: -9.000
+      Right_Hip_Yaw: -1.000
+      Right_Knee_Pitch: -30.000
+      Right_Ankle_Pitch: 6.000
+      Right_Ankle_Roll: -5.000
+
+  - delta: 0.12
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 76.000
+      Left_Shoulder_Roll: -86.000
+      Left_Elbow_Pitch: -78.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 44.000
+      Right_Elbow_Pitch: -64.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -58.000
+      Left_Hip_Roll: 14.000
+      Left_Hip_Yaw: 4.000
+      Left_Knee_Pitch: -6.000
+      Left_Ankle_Pitch: 0.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 22.000
+      Right_Hip_Roll: -9.000
+      Right_Hip_Yaw: -1.000
+      Right_Knee_Pitch: -26.000
+      Right_Ankle_Pitch: 4.000
+      Right_Ankle_Roll: -6.000
+    kp: 180
+
+  - delta: 0.18
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 72.000
+      Left_Shoulder_Roll: -82.000
+      Left_Elbow_Pitch: -72.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 46.000
+      Right_Elbow_Pitch: -66.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -20.000
+      Left_Hip_Roll: 10.500
+      Left_Hip_Yaw: 2.000
+      Left_Knee_Pitch: 20.000
+      Left_Ankle_Pitch: 11.000
+      Left_Ankle_Roll: 4.500
+      Right_Hip_Pitch: 24.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -34.000
+      Right_Ankle_Pitch: 8.000
+      Right_Ankle_Roll: -4.000
+
+  - delta: 0.18
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 70.000
+      Left_Shoulder_Roll: -80.000
+      Left_Elbow_Pitch: -68.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 72.000
+      Right_Shoulder_Roll: 48.000
+      Right_Elbow_Pitch: -68.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -16.000
+      Left_Hip_Roll: 10.000
+      Left_Hip_Yaw: 1.000
+      Left_Knee_Pitch: 14.000
+      Left_Ankle_Pitch: 10.000
+      Left_Ankle_Roll: 3.000
+      Right_Hip_Pitch: 24.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -34.000
+      Right_Ankle_Pitch: 8.000
+      Right_Ankle_Roll: -4.000
+
+  - delta: 0.26
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 64.000
+      Left_Shoulder_Roll: -72.000
+      Left_Elbow_Pitch: -58.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: 68.000
+      Right_Shoulder_Roll: 45.000
+      Right_Elbow_Pitch: -64.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: 0.000
+      Left_Hip_Roll: 0.000
+      Left_Hip_Yaw: 0.000
+      Left_Knee_Pitch: 0.000
+      Left_Ankle_Pitch: 0.000
+      Left_Ankle_Roll: 0.000
+      Right_Hip_Pitch: 20.000
+      Right_Hip_Roll: -6.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -26.000
+      Right_Ankle_Pitch: 6.000
+      Right_Ankle_Roll: 0.000
+    kp: 120

behaviors/custom/keyframe/kick/kick_short.yaml

@@ -0,0 +1,213 @@
+symmetry: false
+kp: 140
+kd: 1.2
+keyframes:
+  - delta: 0.00
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 68.000
+      Left_Shoulder_Roll: -76.000
+      Left_Elbow_Pitch: -62.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: -72.000
+      Right_Shoulder_Roll: 52.000
+      Right_Elbow_Pitch: -70.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -12.000
+      Left_Hip_Roll: 8.000
+      Left_Hip_Yaw: 0.000
+      Left_Knee_Pitch: 35.000
+      Left_Ankle_Pitch: -18.000
+      Left_Ankle_Roll: 3.000
+      Right_Hip_Pitch: 14.000
+      Right_Hip_Roll: -9.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -10.000
+      Right_Ankle_Pitch: 6.000
+      Right_Ankle_Roll: -5.000
+
+  - delta: 0.16
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 70.000
+      Left_Shoulder_Roll: -80.000
+      Left_Elbow_Pitch: -70.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: -72.000
+      Right_Shoulder_Roll: 50.000
+      Right_Elbow_Pitch: -70.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -25.000
+      Left_Hip_Roll: 10.000
+      Left_Hip_Yaw: 1.000
+      Left_Knee_Pitch: 45.000
+      Left_Ankle_Pitch: -20.000
+      Left_Ankle_Roll: 4.500
+      Right_Hip_Pitch: 14.000
+      Right_Hip_Roll: -9.000
+      Right_Hip_Yaw: -1.000
+      Right_Knee_Pitch: -10.000
+      Right_Ankle_Pitch: 6.000
+      Right_Ankle_Roll: -5.500
+
+  - delta: 0.17
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 72.000
+      Left_Shoulder_Roll: -82.000
+      Left_Elbow_Pitch: -74.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: -72.000
+      Right_Shoulder_Roll: 48.000
+      Right_Elbow_Pitch: -68.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -40.000
+      Left_Hip_Roll: 12.000
+      Left_Hip_Yaw: 2.000
+      Left_Knee_Pitch: 18.000
+      Left_Ankle_Pitch: -24.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 14.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: -2.000
+      Right_Knee_Pitch: -8.000
+      Right_Ankle_Pitch: 5.000
+      Right_Ankle_Roll: -7.000
+
+  - delta: 0.16
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 73.500
+      Left_Shoulder_Roll: -84.000
+      Left_Elbow_Pitch: -76.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: -72.000
+      Right_Shoulder_Roll: 46.000
+      Right_Elbow_Pitch: -66.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -48.000
+      Left_Hip_Roll: 13.000
+      Left_Hip_Yaw: 3.000
+      Left_Knee_Pitch: -8.000
+      Left_Ankle_Pitch: -18.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 12.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: -1.000
+      Right_Knee_Pitch: -6.000
+      Right_Ankle_Pitch: 4.000
+      Right_Ankle_Roll: -5.000
+
+  - delta: 0.16
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 75.000
+      Left_Shoulder_Roll: -86.000
+      Left_Elbow_Pitch: -72.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: -70.000
+      Right_Shoulder_Roll: 44.000
+      Right_Elbow_Pitch: -64.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -50.000
+      Left_Hip_Roll: 13.000
+      Left_Hip_Yaw: 3.000
+      Left_Knee_Pitch: 5.000
+      Left_Ankle_Pitch: -22.000
+      Left_Ankle_Roll: 5.000
+      Right_Hip_Pitch: 12.000
+      Right_Hip_Roll: -8.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -6.000
+      Right_Ankle_Pitch: 4.000
+      Right_Ankle_Roll: -5.000
+    kp: 170
+
+  - delta: 0.18
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 73.500
+      Left_Shoulder_Roll: -82.000
+      Left_Elbow_Pitch: -69.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: -72.000
+      Right_Shoulder_Roll: 48.000
+      Right_Elbow_Pitch: -68.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -8.000
+      Left_Hip_Roll: 9.000
+      Left_Hip_Yaw: 1.000
+      Left_Knee_Pitch: 28.000
+      Left_Ankle_Pitch: -22.000
+      Left_Ankle_Roll: 3.000
+      Right_Hip_Pitch: 8.000
+      Right_Hip_Roll: -5.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -8.000
+      Right_Ankle_Pitch: 5.000
+      Right_Ankle_Roll: -3.000
+
+  - delta: 0.18
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 72.000
+      Left_Shoulder_Roll: -82.000
+      Left_Elbow_Pitch: -68.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: -68.000
+      Right_Shoulder_Roll: 48.000
+      Right_Elbow_Pitch: -62.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: -2.000
+      Left_Hip_Roll: 9.000
+      Left_Hip_Yaw: 1.000
+      Left_Knee_Pitch: 28.000
+      Left_Ankle_Pitch: -18.000
+      Left_Ankle_Roll: 3.000
+      Right_Hip_Pitch: 8.000
+      Right_Hip_Roll: -5.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: -6.000
+      Right_Ankle_Pitch: 4.000
+      Right_Ankle_Roll: -3.000
+
+  - delta: 0.24
+    motor_positions:
+      Head_yaw: 0.000
+      Head_pitch: 0.000
+      Left_Shoulder_Pitch: 65.000
+      Left_Shoulder_Roll: -75.000
+      Left_Elbow_Pitch: -60.000
+      Left_Elbow_Yaw: 0.000
+      Right_Shoulder_Pitch: -65.000
+      Right_Shoulder_Roll: 45.000
+      Right_Elbow_Pitch: -60.000
+      Right_Elbow_Yaw: 0.000
+      Waist: 0.000
+      Left_Hip_Pitch: 0.000
+      Left_Hip_Roll: 0.000
+      Left_Hip_Yaw: 0.000
+      Left_Knee_Pitch: 0.000
+      Left_Ankle_Pitch: 0.000
+      Left_Ankle_Roll: 0.000
+      Right_Hip_Pitch: 0.000
+      Right_Hip_Roll: 0.000
+      Right_Hip_Yaw: 0.000
+      Right_Knee_Pitch: 0.000
+      Right_Ankle_Pitch: 0.000
+      Right_Ankle_Roll: 0.000
+    kp: 120

kick_test.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+export OMP_NUM_THREADS=1
+
+host=${1:-localhost}
+port=${2:-60000}
+
+python3 run_player.py --host $host --port $port -n 1 -t SE &

world/world.py

@@ -34,7 +34,7 @@ class World:
         self.playmode_group: PlayModeGroupEnum = PlayModeGroupEnum.NOT_INITIALIZED
         self.is_left_team: bool = None
         self.game_time: float = None
-        self.server_time: float = None  # 服务器时间，单位：秒
+        self.server_time: float = None
         self.score_left: int = None
         self.score_right: int = None
         self.their_team_name: str = None