This paper explores minimum sensing navigation of robots in environments cluttered with obstacles. The general objective is to find a path plan to a goal region that requires minimal sensing effort. In [1], the information-geometric RRT* (IG-RRT*) algorithm was proposed to efficiently find such a path. However, like any stochastic sampling-based planner, the computational complexity of IG-RRT* grows quickly, impeding its use with a large number of nodes. To remedy this limitation, we suggest running IG-RRT* with a moderate number of nodes, and then using a smoothing algorithm to adjust the path obtained. To develop a smoothing algorithm, we explicitly formulate the minimum sensing path planning problem as an optimization problem. For this formulation, we introduce a new safety constraint to impose a bound on the probability of collision with obstacles in continuous-time, in contrast to the common discrete-time approach. The problem is amenable to solution via the convex-concave procedure (CCP). We develop a CCP algorithm for the formulated optimization and use this algorithm for path smoothing. We demonstrate the efficacy of the proposed approach through numerical simulations.

翻译：本文探索了机器人在障碍物密集环境中进行最小感知导航的问题。总体目标是找到一条通往目标区域且需最小感知努力的路径规划。文献[1]提出了信息几何RRT*（IG-RRT*）算法，用于高效寻找此类路径。然而，与任何基于随机采样的规划器类似，IG-RRT*的计算复杂度随节点数快速增加，限制了其在大量节点场景下的应用。为解决此局限，我们建议采用适中数量的节点运行IG-RRT*，然后利用光滑算法对所得路径进行优化调整。为开发光滑算法，我们明确将最小感知路径规划问题建模为优化问题。在该建模过程中，我们引入了一种新的安全约束，用于限制连续时间内与障碍物碰撞的概率，区别于常见的离散时间方法。该问题可通过凸凹过程（CCP）求解。针对所构建的优化问题，我们开发了一种CCP算法，并将其用于路径光滑处理。通过数值仿真验证了所提方法的有效性。