Obstacle avoidance for multi-robot navigation with polytopic shapes is challenging. Existing works simplify the system dynamics or consider it as a convex or non-convex optimization problem with positive distance constraints between robots, which limits real-time performance and scalability. Additionally, generating collision-free behavior for polytopic-shaped robots is harder due to implicit and non-differentiable distance functions between polytopes. In this paper, we extend the concept of velocity obstacle (VO) principle for polytopic-shaped robots and propose a novel approach to construct the VO in the function of vertex coordinates and other robot's states. Compared with existing work about obstacle avoidance between polytopic-shaped robots, our approach is much more computationally efficient as the proposed approach for construction of VO between polytopes is optimization-free. Based on VO representation for polytopic shapes, we later propose a navigation approach for distributed multi-robot systems. We validate our proposed VO representation and navigation approach in multiple challenging scenarios including large-scale randomized tests, and our approach outperforms the state of art in many evaluation metrics, including completion rate, deadlock rate, and the average travel distance.
翻译:Abstract: 多边形形状的机器人避障是具有挑战性的问题。现有的研究要么简化了系统动力学,要么将其视为带有机器人之间正距离约束的凸或非凸优化问题,这限制了实时性能和可伸缩性。此外,由于多边形之间的距离函数是隐式和不可微分的,因此针对多边形形状的机器人,生成无碰撞的行为更加困难。在本文中,我们将速度障碍(VO)原理扩展到多边形形状的机器人,并提出了一种构建基于顶点坐标和其他机器人状态的VO的新方法。与现有研究关于多边形形状机器人之间的避障相比,我们的方法更加计算高效,因为构建多边形之间VO的方法是无需优化的。基于VO表示多边形形状,我们随后提出了一种分布式多机器人系统的导航方法。我们在多个具有挑战性的场景中验证了我们提出的VO表示和导航方法,并且我们的方法在很多评估指标上优于现有的研究,包括完成率、死锁率和平均行驶距离。