An NMPC Approach using Convex Inner Approximations for Online Motion Planning with Guaranteed Collision Avoidance
This work addresses trajectory optimization and collision avoidance for mobile robots, offering a method that scales to high-dimensional systems and unifies optimization and tracking for safe motion in dynamic environments, representing a novel method for a known bottleneck rather than a foundational advancement.
The paper tackles the problem of online motion planning for mobile robots by proposing a nonlinear model predictive control (NMPC) framework with a novel convex inner approximation for collision avoidance, resulting in kinodynamically feasible and collision-free trajectories typically found in one iteration, with experimental evaluation showing it outperforms state-of-the-art baselines in planning efficiency and path quality.
Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remain subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a nonlinear model predictive control (NMPC) framework, that is based on a novel convex inner approximation of the collision avoidance constraint. The proposed Convex Inner ApprOximation (CIAO) method finds kinodynamically feasible and continuous time collision free trajectories, in few iterations, typically one. For a feasible initialization, the approach is guaranteed to find a feasible solution, i.e. it preserves feasibility. Our experimental evaluation shows that CIAO outperforms state of the art baselines in terms of planning efficiency and path quality. Experiments on a robot with 12 states show that it also scales to high-dimensional systems. Furthermore real-world experiments demonstrate its capability of unifying trajectory optimization and tracking for safe motion planning in dynamic environments.