A New Trajectory-Oriented Approach to Enhancing Comprehensive Crowd Navigation Performance

TL;DR

This paper introduces a unified framework for evaluating crowd navigation methods, emphasizing trajectory optimization and curvature, leading to improved naturalness, comfort, and efficiency in navigation systems.

Contribution

It presents a novel reward-shaping strategy for trajectory curvature and a comprehensive evaluation framework for multi-objective assessment.

Findings

Enhanced trajectory smoothness and naturalness in navigation.

Superior performance over state-of-the-art methods in experiments.

Improved energy efficiency and user comfort in crowd navigation.

Abstract

Crowd navigation has garnered considerable research interest in recent years, especially with the proliferating application of deep reinforcement learning (DRL) techniques. Many studies, however, do not sufficiently analyze the relative priorities among evaluation metrics, which compromises the fair assessment of methods with divergent objectives. Furthermore, trajectory-continuity metrics, specifically those requiring $C^2$ smoothness, are rarely incorporated. Current DRL approaches generally prioritize efficiency and proximal comfort, often neglecting trajectory optimization or addressing it only through simplistic, unvalidated smoothness reward. Nevertheless, effective trajectory optimization is essential to ensure naturalness, enhance comfort, and maximize the energy efficiency of any navigation system. To address these gaps, this paper proposes a unified framework that enables the fair and transparent assessment of navigation methods by examining the prioritization and joint evaluation of multiple optimization objectives. We further propose a novel reward-shaping strategy that explicitly emphasizes trajectory-curvature optimization. The resulting trajectory quality and adaptability are significantly enhanced across multi-scale scenarios. Through extensive 2D and 3D experiments, we demonstrate that the proposed method achieves superior performance compared to state-of-the-art approaches.