论文标题

基于强大的词典任务分配避免碰撞

Collision Avoidance Based on Robust Lexicographic Task Assignment

论文作者

Wood, Tony A., Khoo, Mitchell, Michael, Elad, Manzie, Chris, Shames, Iman

论文摘要

多代理运动控制的传统任务分配方法没有考虑到碰撞的可能性。这可能导致对路径计划的挑战性要求。我们得出了一种分配方法,该方法不仅可以最大程度地减少代理商及其分配的目的地之间的最大距离,还可以为保证避免碰撞的局部约束提供局部约束。为此,我们引入了一个顺序的瓶颈优化问题,并定义了优化分配的鲁棒性概念,以对单个分配成本的变化进行更改。在与代理的大小相关的足够鲁棒性的条件下,我们为每个单独的代理构建了随时间变化的位置界限。这些本地约束是分配过程的直接副产品,仅取决于初始代理位置,要访问的目的地和正时参数。我们证明,如果所有代理人都满足其本地位置约束,则没有指派迁移到目标位置之一的代理人碰撞。我们在说明性案例研究中演示了该方法。

Traditional task assignment approaches for multi-agent motion control do not take the possibility of collisions into account. This can lead to challenging requirements for path planning. We derive an assignment method that not only minimises the largest distance between an agent and its assigned destination but also provides local constraints for guaranteed collision avoidance. To this end, we introduce a sequential bottleneck optimisation problem and define a notion of robustness of an optimising assignment to changes of individual assignment costs. Conditioned on a sufficient level of robustness in relation to the size of the agents, we construct time-varying position bounds for every individual agent. These local constraints are a direct byproduct of the assignment procedure and only depend on the initial agent positions, the destinations that are to be visited, and a timing parameter. We prove that no agent that is assigned to move to one of the target locations collides with any other agent if all agents satisfy their local position constraints. We demonstrate the method in a illustrative case study.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源