用梯度场提前预警碰撞，让机器人在复杂环境中更安全灵活地避障。
Gradient Field-Based Dynamic Window Approach for Collision Avoidance in Complex Environments
- 利用障碍物距离梯度场作为新代价项，提前感知潜在碰撞
- 在非凸障碍物环境中，安全性和灵活性优于主流方法
- 适合多机器人系统在复杂场景中的实时避障应用
为实现多机器人系统在复杂环境中的安全与灵活导航，本文提出一种基于梯度场的动态窗口法（GF-DWA），在传统动态窗口法基础上引入障碍物距离梯度信息作为新代价项，以预测潜在碰撞。该梯度场由高斯过程距离场生成，通过高斯过程回归建模环境空间结构，同时输出距离场与梯度场。在多个避障及车队防碰撞场景中，GF-DWA展现出优于其他主流轨迹规划与控制方法的安全性与灵活性，尤其在包含非凸障碍物的复杂环境中表现突出。
原文摘要 · Abstract (English)
For safe and flexible navigation in multi-robot systems, this paper presents an enhanced and predictive sampling-based trajectory planning approach in complex environments, the Gradient Field-based Dynamic Window Approach (GF-DWA). Building upon the dynamic window approach, the proposed method utilizes gradient information of obstacle distances as a new cost term to anticipate potential collisions. This enhancement enables the robot to improve awareness of obstacles, including those with non-convex shapes. The gradient field is derived from the Gaussian process distance field, which generates both the distance field and gradient field by leveraging Gaussian process regression to model the spatial structure of the environment. Through several obstacle avoidance and fleet collision avoidance scenarios, the proposed GF-DWA is shown to outperform other popular trajectory planning and control methods in terms of safety and flexibility, especially in complex environments with non-convex obstacles.
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