CIAO $^\star$：可预测动态环境中基于MPC的安全运动规划
CIAO$^\star$: MPC-based Safe Motion Planning in Predictable Dynamic Environments
摘要：
几十年来，机器人一直在动态环境中运行并共享机器人。然而，大多数基于优化的运动规划方法不考虑其他代理（例如，人类或其他机器人）的移动，因此在这种情况下不能保证避免碰撞。本文基于凸内近似（CIAO）方法，提出了一种运动规划算法，保证在可预测的动态环境中避免碰撞。此外，它推广了CIAO的自由区域的概念，以任意的规范，并提出了一个成本函数近似的时间最优运动规划。所提出的方法，CIAO $^\star$，发现kinodynamically可行的和无碰撞的轨迹约束的单体机器人使用模型预测控制（MPC）。它优化一个代理的运动，并考虑周围代理和障碍物的预测运动。实验评估表明，CIAO $^\star$达到接近时间最优的行为。
英文摘要：
Robots have been operating in dynamic environments and shared workspaces for decades. Most optimization based motion planning methods, however, do not consider the movement of other agents, e.g. humans or other robots, and therefore do not guarantee collision avoidance in such scenarios. This paper builds upon the Convex Inner ApprOximation (CIAO) method and proposes a motion planning algorithm that guarantees collision avoidance in predictable dynamic environments. Furthermore, it generalizes CIAO's free region concept to arbitrary norms and proposes a cost function to approximate time optimal motion planning. The proposed method, CIAO$^\star$, finds kinodynamically feasible and collision free trajectories for constrained single body robots using model predictive control (MPC). It optimizes the motion of one agent and accounts for the predicted movement of surrounding agents and obstacles. The experimental evaluation shows that CIAO$^\star$ reaches close to time optimal behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
