论文标题
CIAO $^\ star $：基于MPC的可预测动态环境中的安全运动计划
CIAO$^\star$: MPC-based Safe Motion Planning in Predictable Dynamic Environments
论文作者
论文摘要
数十年来，机器人一直在动态环境和共享工作区中运行。但是，大多数基于优化的运动计划方法不考虑其他代理的运动，例如人类或其他机器人，因此不能保证在这种情况下避免碰撞。本文建立在凸内近似（CIAO）方法的基础上，并提出了一种运动计划算法，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，并提出成本函数以近似时间最佳运动计划。提出的方法是CIAO $^\ star $，使用模型预测控制（MPC）找到了动力动力学的可行性和无冲突轨迹。它优化了一个代理的运动，并解释了周围代理和障碍的预测运动。实验评估表明，CIAO $^\ star $达到了时间最佳行为。
Robots have been operating in dynamic environments and shared workspaces for decades. Most optimization based motion planning methods, however, do not consider the movement of other agents, e.g. humans or other robots, and therefore do not guarantee collision avoidance in such scenarios. This paper builds upon the Convex Inner ApprOximation (CIAO) method and proposes a motion planning algorithm that guarantees collision avoidance in predictable dynamic environments. Furthermore, it generalizes CIAO's free region concept to arbitrary norms and proposes a cost function to approximate time optimal motion planning. The proposed method, CIAO$^\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.
