Globally Guided Trajectory Planning in Dynamic Environments

TL;DR

This paper introduces a topology-aware trajectory planning method for mobile robots in dynamic, human-shared environments, enabling faster and safer navigation by guiding local planners with high-level trajectory information.

Contribution

It presents a novel approach to identify and propagate multiple locally optimal behaviors based on topology, improving local planning in complex dynamic environments.

Findings

The method achieves faster planning times in simulations.

It results in safer navigation in real-world experiments.

The approach effectively avoids local optima in dynamic obstacle avoidance.

Abstract

Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacles make the collision-free space nonconvex, which leads to two distinct passing behaviors per obstacle (passing left or right). For local planners, such as receding-horizon trajectory optimization, each behavior presents a local optimum in which the planner can get stuck. This may result in slow or unsafe motion even when a better plan exists. In this work, we identify trajectories for multiple locally optimal driving behaviors, by considering their topology. This identification is made consistent over successive iterations by propagating the topology information. The most suitable high-level trajectory guides a local optimization-based planner, resulting in fast and safe motion plans. We validate the proposed planner on a mobile robot in simulation and real-world experiments.