对弈人工智能！myCobot 280开源六轴机械臂Connect 4 四子棋对弈下篇

● 获取信息：用摄像头获取到棋盘上的信息，进行对弈


            ● 处理信息：处理获取到的信息识别出棋子的位置，通过对弈算法，计算出下一步棋子应该在哪里下


            ● 机械臂的轨迹：设计机械臂如何抓取棋子，设计放置棋子的路径


            ● 功能的整合：将上面三个功能结合在一起，实现AI机械臂下棋。

dictionary = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_6X6_250)
        parameters = cv2.aruco.DetectorParameters()
        detector = cv2.aruco.ArucoDetector(dictionary, parameters)

        corners, ids, rejectedCandidates = detector.detectMarkers(bgr_data)
        rvec, tvec, _ = cv2.aruco.estimatePoseSingleMarkers(corners, 0.05, self.mtx, self.dist)

        if rvec is None or len(corners) != 4:
            return None

# debug
        if DEBUG:
            debug_img = bgr_data.copy()
            for i in range(rvec.shape[0]):
                cv2.drawFrameAxes(debug_img, self.mtx, self.dist, rvec[i, :, :, ], tvec[i, :, :, ],
                                  0.03)
# Draw a square around the marker.
                cv2.aruco.drawDetectedMarkers(debug_img, corners)
            cv2.imshow("debug1", debug_img)

# Sort the detected QR code corner points in the following order: top left, top right, bottom left, bottom right.
        corners = np.mean(corners, axis=2)
        corners = (np.ceil(corners)).astype(int)
        corners = corners.reshape((4, 2))
        cx, cy = (np.mean(corners[:, 0]), np.mean(corners[:, 1]))
        res: list = [None for _ in range(4)]
        for x, y in corners:
            if x < cx and y < cy:
                res[0] = (x, y)
            elif x > cx and y < cy:
                res[1] = (x, y)
            elif x < cx and y > cy:
                res[2] = (x, y)
            else:
                res[3] = (x, y)
        res = np.array(res)

## debug code
        if DEBUG:
            debug_img = bgr_data.copy()
            for p in res:
                cv2.circle(debug_img, p, 3, BGR_GREEN, -1)
            cv2.imshow("aruco", debug_img)

        return res

functionmodel_predict(state, available_actions):
# 将available_actions转换为numpy数组
    available_actions = np.array(available_actions)
# 对state进行扩展，以适应ONNX模型的输入要求
    state = np.expand_dims(np.expand_dims(np.array(state, dtype=np.float32), axis=0), axis=0)
# 构建ONNX模型的输入
    ort_inputs ={self.policy_net.get_inputs()[0].name: state}
# 进行模型预测，获取每个可用位置的预测值
    r_actions =self.policy_net.run(None, ort_inputs)[0][0,:]
# 根据预测值选择最优的落子位置
    state_action_values = np.array(
[r_actions[action]for action in available_actions])
    argmax_action = np.argmax(state_action_values)
    greedy_action = available_actions[argmax_action]
return greedy_action

# 初始化定义几个坐标点
# 设定一个长度为7的列表
self.chess_table = [None for _ in range(7)]
self.chess_table[0]-[6] = [J1,J2,J3,J4,J5,J6] # 七个棋格的位置

self.angle_table = {
    "recovery": [0, 0, 0, 0, 0, 0], #初始位置
    "observe": [-2.54, 135.0, -122.95, -32.34, 2.46, -90.35],#观察位置
    "stack-hover-1": [-47.19, -37.96, -58.53, 1.05, -1.93, -1.84],#吸取棋子的位置
        }

#Sending the angle to the robotic arm.
self.send_angles(self.chess_table[n], ARM_SPEED)

#Sending the Cartesian coordinates to the robotic arm.
self.send_coords(coord_list,ARM_SPEED)