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代价度量与曲线规划空间相结合的方式，扩展了通用基于采样的运动规划器行为，以支持长距离路径跟踪过程中的避障。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中的先验地形知识。在此改进中，我们探讨了在曲线构型空间中规划的细微之处，并描述了一种自然的奇异性处理机制以提升通用性。随后，我们将重点转向轨迹生成问题，提出了一种新颖的模型预测控制架构，以更充分地利用路径规划器实现避障性能的改进。通过超过5公里的实地机器人试验，我们将该方法与更常见的直接路径跟踪MPC方法进行了比较，并讨论了这些技术对可靠长期自主运行的潜力。