文章基本信息

标题：CIAO ⁎: MPC-based Safe Motion Planning in Predictable Dynamic Environments
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作者：Tobias Schoels ; Per Rutquist ; Luigi Palmieri 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：6555-6562
DOI：10.1016/j.ifacol.2020.12.072
语种：English
出版社：Elsevier
摘要：AbstractRobots 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*, 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* reaches close to time optimal behavior.
关键词：Keywordstime optimal controlsafetyconvex optimizationpredictive controltrajectorypath planningmotion controlautonomous mobile robotsdynamic environments