CIAO⁎: MPC-based Safe Motion Planning in Predictable Dynamic Environments
| Autor: | Per Rutquist, Moritz Diehl, Kai O. Arras, Luigi Palmieri, Tobias Schoels, Andrea Zanelli |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Computer science 020208 electrical & electronic engineering Regular polygon 02 engineering and technology Function (mathematics) Workspace Motion (physics) Computer Science::Robotics Model predictive control 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Robot Motion planning Collision avoidance |
| Zdroj: | IFAC-Papers |
| ISSN: | 2405-8963 |
| Popis: | 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*, 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. |
| Databáze: | OpenAIRE |
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