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提出了一种基于局部区域内的机器人和目标密度的多机器人分布式控制算法。运用这种算法,多机器人在跟踪多目标过程中,每个机器人能在每次调整速度和方向之前,根据最新的通讯信息和感知信息,计算通讯范围内相邻机器人和感知范围内移动目标的数量及位置坐标,然后按照所受人工势场(APF)力的合力决定下一次移动的新位置坐标,这个合力乘以一个基于机器人和目标密度的可变加权系数,以适应不同状态,然后按照动力学和运动学方程计算速度及方向。通过仿真实验对该算法进行了验证,实验结果表明,多机器人可以根据多目标的移动进行自主协作跟踪观测,算法的性能优于无可变加权系数的APF法和静态感知节点(SN)法。此方法对于实时监测和目标跟踪具有实际应用意义。
A multi-robot distributed control algorithm based on local robot and target density is proposed. Using this algorithm, each robot in the process of tracking multiple targets, each robot can calculate the distance between the adjacent robot within the communication range and the moving target within the range of perception based on the latest communication information and perception information before each adjustment of the speed and direction Quantity and location coordinates, and then determine the new location coordinate of the next move according to the resultant APF forces multiplied by a variable weighting coefficient based on the robot and the target density to adapt to different states, and then follow the Kinetic and kinematic equations calculate velocity and direction. The algorithm is validated through simulation experiments. The experimental results show that the multi-robot can cooperatively track and observe according to the multi-objective movement. The performance of the algorithm is better than the APF method and the static sensing node (SN) method without variable weighting coefficients. This method has practical application for real-time monitoring and target tracking.