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.

翻译：本文提出一种创新方法，将未来最近点预测集成至避障控制器的距离约束中，通过凸包与最近点距离计算的协同作用实现优化。该方法通过解决最近点突变问题，有效降低了碰撞风险并提升了控制器性能。在图像引导治疗机器人平台的应用中，通过仿真与用户实验验证，该框架展现出更精确的距离预测能力、更平滑的运动轨迹以及在障碍物附近更安全的导航特性。