面向多智能消防机器人的编队避障控制方法

A Formation Obstacle-avoidance Control Method for Multiple Intelligent Firefighting Robots

  • 摘要: 为了提高多智能消防机器人系统在复杂火场环境下协同运动的安全性和及时性,提出一种基于动态权重的虚拟领航—领航—跟随法与改进人工势场法相结合的编队避障控制方法。首先,设计一种圆周运动控制律与方位角定位控制律相耦合的协同编队控制器,使各机器人收敛到以虚拟领航机器人为原点形成的圆上的期望位置。然后,对传统人工势场法进行改进,利用一种对数障碍函数建立道路两侧的危险膨胀区域来保障编队行驶在安全区域内,接着通过调整障碍物和道路边界共同作用在机器人上的合斥力方向,使得合斥力方向变为与目标作用的引力方向垂直且远离障碍物的方向,以解决局部极小值和目标不可达的问题。最后,引入动态权重因子对编队控制器和避障控制器进行自适应比值调整。为验证本文控制器的有效性,与传统人工势场法、固定权重的改进人工势场法进行仿真实验对比,结果表明所设计的控制器在收敛速度、跟踪误差和避障效果方面都表现更优。为进一步验证本文控制器的实用性,采用3台智能消防机器人进行物理实验,实验结果表明本文控制器既可以将编队限制在道路可行驶区域内,又可以较好地完成编队避障。

     

    Abstract: To improve the safety and timeliness of cooperative motion of multiple intelligent firefighting robot systems in complex fire environments, a formation obstacle-avoidance control method is proposed, which combines the virtual leader-leader-follower method and an improved artificial potential field method using a dynamic weight. Firstly, a cooperative formation controller combining a circular motion control law and a bearing angle based positioning control law is designed, to make each robot converge to its desired position along a circle with the virtual leader as the center. Then, the traditional artificial potential method is improved, and a logarithmic obstacle function is used to establish the dangerous expansion areas on both sides of the road to ensure that the formation drive in a safe area. The direction of the resultant repulsive force from the obstacle and the road boundary is adjusted to be perpendicular to the direction of the attractive force from the target and far away from the obstacle, so as to solve the problems of local minimum and unreachable target. Finally, a dynamic weight factor is introduced to adaptively adjust the ratio of the formation controller to the obstacle avoidance controller. To validate the effectiveness of the proposed controller, it is compared with the traditional artificial potential field method and an improved artificial potential field method with fixed weight by simulation experiments. The results show that the designed controller outperforms the other methods regarding convergence speed, tracking error, and obstacle avoidance effect. Further, 3 intelligent firefighting robots are used in a physical experiment to validate the practicality of the controller. The result shows that the controller can effectively restrict the formation within the feasible road area and complete the obstacle avoidance.

     

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