We extend the behaviour of generic sample-based motion planners to support obstacle avoidance during long-range path following by introducing a new edge-cost metric paired with a curvilinear planning space. The resulting planner generates naturally smooth paths that avoid local obstacles while minimizing lateral path deviation to best exploit prior terrain knowledge from the reference path. In this adaptation, we explore the nuances of planning in the curvilinear configuration space and describe a mechanism for natural singularity handling to improve generality. We then shift our focus to the trajectory generation problem, proposing a novel Model Predictive Control (MPC) architecture to best exploit our path planner for improved obstacle avoidance. Through rigorous field robotics trials over 5 km, we compare our approach to the more common direct path-tracking MPC method and discuss the promise of these techniques for reliable long-term autonomous operations.

翻译：我们通过引入一种新的边成本度量与曲线规划空间相结合，扩展了通用基于采样的运动规划器的行为，以支持在长距离路径跟踪过程中的障碍物避让。由此产生的规划器能够生成自然平滑的路径，在避开局部障碍物的同时最小化横向路径偏差，从而充分利用参考路径提供的先验地形信息。在此改进中，我们深入探讨了曲线构型空间中的规划细节，并描述了一种用于自然处理奇异性的机制以提升通用性。随后我们将研究重点转向轨迹生成问题，提出了一种新颖的模型预测控制（MPC）架构，以最优方式利用我们的路径规划器来增强避障能力。通过超过5公里的严格野外机器人实地测试，我们将本方法与更常见的直接路径跟踪MPC方法进行对比，并探讨了这些技术在实现可靠长期自主运行方面的潜力。