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.

中文翻译：

---

CIAO$^\star$：可预测动态环境中基于 MPC 的安全运动规划

几十年来，机器人一直在动态环境和共享工作空间中运行。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）的运动，因此不能保证在这种情况下避免碰撞。本文建立在凸内逼近 (CIAO) 方法的基础上，并提出了一种运动规划算法，可确保在可预测的动态环境中避免碰撞。此外，它将 CIAO 的自由区域概念推广到任意规范，并提出了一个成本函数来近似时间最优运动规划。所提出的方法 CIAO$^\star$ 使用模型预测控制 (MPC) 为受约束的单体机器人找到运动动力学可行且无碰撞的轨迹。它优化了一个代理的运动，并考虑了周围代理和障碍物的预测运动。实验评估表明，CIAO$^\star$ 接近时间最优行为。