Online Generation of Collision-Free Trajectories in Dynamic Environments

TL;DR

This paper introduces an online method to convert arbitrary paths into smooth, collision-free, kinematically feasible trajectories that adapt in real-time to dynamic environments, suitable for real-world robotic applications.

Contribution

It presents a novel real-time trajectory generation approach that guarantees collision avoidance and kinematic feasibility in dynamic settings, compatible with various path planners.

Findings

Favorable smoothness and computational performance in simulations.

Effective real-time adaptation to environment changes.

Successful real-world robot experiments with human presence.

Abstract

In this paper, we present an online method for converting an arbitrary geometric path represented by a sequence of states, generated by any planner (e.g., sampling-based planners like RRT or PRM, search-based planners like ARA*, etc.), into a corresponding kinematically feasible, jerk-limited trajectory. The method generates a sequence of quintic/quartic splines that can be discretized at a user-specified control rate, and then streamed to a low-level robot controller. Our approach enables real-time adaptation to newly captured changes in the environment. It can also be re-invoked at any time instance to generate a new trajectory from the robot's current to a desired target state or sequence of states. We can guarantee that the trajectory will remain collision-free for a certain amount of time in dynamic environments, while allowing bounded geometric deviation from the original path. The kinematic constraints are taken into account, including limited jerk. We validate the approach in a comparative simulation study against the competing method, demonstrating favorable behavior w.r.t. smoothness, computational time, and real-time performance, particularly in scenarios with frequent changes of target states (up to 1 [kHz]). Experiments on a real robot demonstrate that the proposed approach can be used in real-world scenarios including human presence.