Navigation functions with moving destinations and obstacles
Navigation functions with moving destinations and obstacles
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DOI：
10.1007/s10514-023-10088-7
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发表时间：
2023-02
影响因子：
3.5
通讯作者：
Cong Wei;Chuchu Chen;H. Tanner
中科院分区：
文献类型：
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作者：
Cong Wei;Chuchu Chen;H. Tanner
动态环境挑战现有的机器人导航方法，并激励工作空间变化或放弃碰撞避免和收敛保证的严格假设。本文表明，后者可以保存，即使在没有知识的环境如何演变，通过适用于移动障碍物和机器人目的地的球形世界的导航功能方法。假设机器人目的地和障碍物的速度上的界限，以及足够高的最大机器人速度，可以使用导航函数梯度来产生保证避障的机器人反馈律，以及当目标最终停止移动时有界跟踪误差和渐近收敛到目标的理论保证。基于梯度的反馈控制器来自新的导航功能的建设的功效证明了在数值模拟以及实验。
Dynamic environments challenge existing robot navigation methods, and motivate either stringent assumptions on workspace variation or relinquishing of collision avoidance and convergence guarantees. This paper shows that the latter can be preserved even in the absence of knowledge of how the environment evolves, through a navigation function methodology applicable to sphere-worlds with moving obstacles and robot destinations. Assuming bounds on speeds of robot destination and obstacles, and sufficiently higher maximum robot speed, the navigation function gradient can be used produce robot feedback laws that guarantee obstacle avoidance, and theoretical guarantees of bounded tracking errors and asymptotic convergence to the target when the latter eventually stops moving. The efficacy of the gradient-based feedback controller derived from the new navigation function construction is demonstrated both in numerical simulations as well as experimentally.