Replanning in temporal logic tasks is extremely difficult during the online execution of robots. This study introduces an effective path planner that computes solutions for temporal logic goals and instantly adapts to non-static and partially unknown environments. Given prior knowledge and a task specification, the planner first identifies an initial feasible solution by growing a sampling-based search tree. While carrying out the computed plan, the robot maintains a solution library to continuously enhance the unfinished part of the plan and store backup plans. The planner updates existing plans when meeting unexpected obstacles or recognizing flaws in prior knowledge. Upon a high-level path is obtained, a trajectory generator tracks the path by dividing it into segments of motion primitives. Our planner is integrated into an autonomous mobile robot system, further deployed on a multicopter with limited onboard processing power. In simulation and real-world experiments, our planner is demonstrated to swiftly and effectively adjust to environmental uncertainties.

翻译：在时序逻辑任务的在线执行过程中，重规划极其困难。本研究提出一种高效的路径规划器，可计算满足时序逻辑目标的解，并即时适应非静态及部分未知环境。该规划器在给定先验知识与任务规范后，首先通过生长基于采样的搜索树来识别初始可行解。在执行已计算路径的过程中，机器人维护一个解库，以持续优化路径中未完成的部分并存储备用方案。当遇到意外障碍或发现先验知识缺陷时，规划器会更新现有方案。在获得高层路径后，轨迹生成器将其划分为运动基元段，从而对路径进行跟踪。本规划器被集成到自主移动机器人系统中，并进一步部署在计算能力受限的多旋翼飞行器上。仿真及真实世界实验表明，本规划器能快速且高效地适应环境不确定性。