Fast Near Time-Optimal Motion Planning for Holonomic Vehicles in Structured Environments

TL;DR

This paper introduces a fast, optimization-based motion planning method for holonomic vehicles in structured environments, enabling near time-optimal trajectories in real-time for applications like assembly lines and laboratories.

Contribution

It presents a novel approach encoding environments with free-space corridors and using motion primitives, significantly reducing computation time while maintaining near-optimality.

Findings

Achieves lower computation times compared to state-of-the-art methods.

Provides near time-optimal trajectories suitable for real-time applications.

Validated on simulation and real-world Beckhoff XPlanar system.

Abstract

This paper proposes a novel and efficient optimization-based method for generating near time-optimal trajectories for holonomic vehicles navigating through complex but structured environments. The approach aims to solve the problem of motion planning for planar motion systems using magnetic levitation that can be used in assembly lines, automated laboratories or clean-rooms. In these applications, time-optimal trajectories that can be computed in real-time are required to increase productivity and allow the vehicles to be reactive if needed. The presented approach encodes the environment representation using free-space corridors and represents the motion of the vehicle through such a corridor using a motion primitive. These primitives are selected heuristically and define the trajectory with a limited number of degrees of freedom, which are determined in an optimization problem. As a result, the method achieves significantly lower computation times compared to the state-of-the-art, most notably solving a full Optimal Control Problem (OCP), OMG-tools or VP-STO without significantly compromising optimality within a fixed corridor sequence. The approach is benchmarked extensively in simulation and is validated on a real-world Beckhoff XPlanar system