Long-Term Dynamic Window Approach for Kinodynamic Local Planning in Static and Crowd Environments

TL;DR

This paper introduces a long-term dynamic window approach combined with graph optimization for kinodynamic local planning, enabling real-time, safe, and smooth navigation in static and crowded environments.

Contribution

It presents a novel local planning method that integrates long-term dynamic window planning with graph optimization to handle kinodynamic constraints in complex environments.

Findings

Outperforms most state-of-the-art approaches in experiments.

Adapts effectively to both static and crowd environments.

Generates safer and more jitterless actions in real time.

Abstract

Local planning for a differential wheeled robot is designed to generate kinodynamic feasible actions that guide the robot to a goal position along the navigation path while avoiding obstacles. Reactive, predictive, and learning-based methods are widely used in local planning. However, few of them can fit static and crowd environments while satisfying kinodynamic constraints simultaneously. To solve this problem, we propose a novel local planning method. The method applies a long-term dynamic window approach to generate an initial trajectory and then optimizes it with graph optimization. The method can plan actions under the robot's kinodynamic constraints in real time while allowing the generated actions to be safer and more jitterless. Experimental results show that the proposed method adapts well to crowd and static environments and outperforms most SOTA approaches.