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.

翻译：近年来，人群导航研究引起了广泛关注，尤其是随着深度强化学习（DRL）技术的广泛应用。然而，许多研究未能充分分析评估指标间的相对优先级，这损害了对具有不同目标方法的公平评估。此外，轨迹连续性指标，特别是那些要求$C^2$光滑度的指标，很少被纳入考量。当前的DRL方法通常优先考虑效率和近端舒适度，往往忽视轨迹优化，或仅通过简单且未经验证的光滑度奖励进行处理。然而，有效的轨迹优化对于确保自然性、提升舒适度以及最大化任何导航系统的能效至关重要。为弥补这些不足，本文提出了一个统一框架，通过考察多个优化目标的优先级设置与联合评估，实现对导航方法的公平透明评估。我们进一步提出了一种新颖的奖励塑造策略，明确强调轨迹曲率优化。由此，轨迹质量和适应性在多尺度场景中得到了显著提升。通过大量的2D和3D实验，我们证明所提方法相比现有先进方法实现了更优的性能。