用预测最近点优化多体避障，提升机器人导航安全性和平滑性。
Non-Conservative Obstacle Avoidance for Multi-Body Systems Leveraging Convex Hulls and Predicted Closest Points
- 基于凸包和最近点预测构建距离约束
- 显著降低碰撞风险，轨迹更平滑
- 适合医疗机器人等高安全性场景
本文提出一种新方法，将未来最近点预测融入碰撞避免控制器的距离约束中，利用凸包结合最近点距离计算。通过解决最近点突变问题，有效降低碰撞风险并提升控制器性能。该方法应用于图像引导治疗机器人，在仿真和用户实验中验证，展现出更高的距离预测精度、更平滑的轨迹以及更安全的近障碍物导航能力。
原文摘要 · Abstract (English)
This paper introduces a novel approach that integrates future closest point predictions into the distance constraints of a collision avoidance controller, leveraging convex hulls with closest point distance calculations. By addressing abrupt shifts in closest points, this method effectively reduces collision risks and enhances controller performance. Applied to an Image Guided Therapy robot and validated through simulations and user experiments, the framework demonstrates improved distance prediction accuracy, smoother trajectories, and safer navigation near obstacles.
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