Path planning in obstacle-dense environments is a key challenge in robotics, and depends on inferring scene attributes and associated uncertainties. We present a multiple-hypothesis path planner designed to navigate complex environments using obstacle detections. Path hypotheses are generated by reasoning about uncertainty and range, as initial detections are typically at far ranges with high uncertainty, before subsequent detections reduce this uncertainty. Given estimated obstacles, we build a graph of pairwise connections between objects based on the probability that the robot can safely pass between the pair. The graph is updated in real time and pruned of unsafe paths, providing probabilistic safety guarantees. The planner generates path hypotheses over this graph, then trades between safety and path length to intelligently optimize the best route. We evaluate our planner on randomly generated simulated forests, and find that in the most challenging environments, it increases the navigation success rate over an A* baseline from 20% to 75%. Results indicate that the use of evolving, range-based uncertainty and multiple hypotheses are critical for navigating dense environments.

翻译：在障碍密集环境中进行路径规划是机器人技术中的一个关键挑战，它依赖于推断场景属性及其相关不确定性。我们提出了一种多假设路径规划器，旨在利用障碍物检测来导航复杂环境。路径假设是通过对不确定性和距离进行推理生成的，因为初始检测通常在远处且具有高不确定性，后续检测会降低这种不确定性。根据估计的障碍物，我们基于机器人能够安全通过两物体之间路径的概率，构建一个物体间成对连接的图。该图实时更新，并修剪不安全的路径，从而提供概率上的安全保障。规划器在此图上生成路径假设，然后在安全性和路径长度之间进行权衡，以智能地优化最佳路线。我们在随机生成的模拟森林中评估了该规划器，发现在最具挑战性的环境中，它相较于A*基线将导航成功率从20%提升至75%。结果表明，利用动态演变的基于距离的不确定性和多假设对于在密集环境中导航至关重要。