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:在机器人在线执行过程中,对时间逻辑任务进行重新规划极为困难。 这项研究引入了一个有效的路径规划器, 用于计算时间逻辑目标的解决方案, 并立即适应非静态和部分未知的环境。 根据先前的知识和任务规格, 规划器首先通过种植基于取样的搜索树确定了初步可行的解决方案。 在执行计算计划时, 机器人保持一个解决方案库, 以不断增强计划未完成的部分, 并存储备份计划 。 规划器在遇到意外障碍或发现先前知识缺陷时更新了现有计划 。 获得高水平路径后, 轨迹生成器通过将其分为运动原始部分来跟踪路径 。 我们的规划器被整合到一个自主的移动机器人系统中, 进一步安装在机载处理能力有限的多机组计算机上。 在模拟和现实世界实验中, 我们的规划器被证明能够快速和有效地适应环境不确定性 。