具有速度、加速度约束的机器人编队避障控制
Obstacle avoidance control for robots formation with speed and acceleration constraints
摘要点击 69  全文点击 53  投稿时间:2019-05-07  修订日期:2019-11-21
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DOI编号  10.7641/CTA.2019.90325
  2020,37(6):1388-1396
中文关键词  编队避障控制  “航标”引导  速度、加速度约束  惩罚因子
英文关键词  formation obstacle avoidance control  navigation’s beacon guidance  velocity and acceleration constraints  penalty factor
基金项目  国家自然科学基金项目(11672187)资助.
作者单位E-mail
张志伟 沈阳航空航天大学 zzw2590@163.com 
滕英元 沈阳航空航天大学 yyteng2005@aliyun.com 
杨慧欣 沈阳航空航天大学  
倪智宇 沈阳航空航天大学  
中文摘要
      针对轮式移动机器人的编队避障问题, 提出了一种改进的控制算法, 能够有效实现避免碰撞和规避障碍. 首先, 建立多机器人不同控制增益的全员“航标”引导的非线性循环追踪控制编队, 当编队过程某成员监测执行功 能失效(但仍能运行)时, 可对非失效机器人进行系统降级重组, 并继续执行任务且避免机器人之间发生碰撞; 再通 过引入速度和加速度约束, 以满足轮式移动机器人控制接口及保护电机的需要, 从而保证控制算法的稳定与收敛; 最后, 通过引入惩罚因子对该控制算法进行改进, 使编队成功规避障碍物并进行最短路径规划. 结果表明, 改进的 控制算法增加了多机器人编队的鲁棒性、提高了抗干扰能力及编队恢复执行任务的可靠性, 更有效地实现了编队 避障控制.
英文摘要
      In this paper, an improved control algorithm for formation obstacle avoidance of wheeled mobile robots is presented, which can effectively avoid collision and obstacles. Firstly, the control formation of nonlinear cyclic pursuit in “navigation’s beacon” to all agents for multi-robot with different control gain is established. When a member of the formation process fails to monitor the execution function (but it can still run), the non-failure robot can be degraded and reorganized, then the tasks can be continued and the collision between the robots can be avoided. Then the speed and acceleration constraints are introduced to meet the requirements of control interface and motor protection of wheeled mobile robot so as to ensure the stability and convergence of the control algorithm. Finally, the penalty factor is introduced to improve the control algorithm, so that the formation can avoid obstacles successfully and plan the shortest path. The results show that the improved control algorithm increases the robustness of multi-robot formation, improves the antijamming ability and the reliability of the formation recovery to perform tasks, and realizes the control of formation obstacle avoidance more effectively.