CIAO$^\star$: MPC-based Safe Motion Planning in Predictable Dynamic Environments

TL;DR

CIAO$^igstar$ introduces a model predictive control-based motion planning algorithm that guarantees collision avoidance in predictable dynamic environments, generalizes previous concepts, and approximates time optimal trajectories.

Contribution

It extends CIAO's free region concept to arbitrary norms and develops a MPC-based method for collision-free, kinodynamically feasible trajectories in dynamic settings.

Findings

Achieves near time-optimal motion planning.

Guarantees collision avoidance with predictable agents.

Handles arbitrary norms in free region computation.

Abstract

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.