DT*: Temporal Logic Path Planning in a Dynamic Environment
This addresses path planning for robots in dynamic settings like warehouses, but it is incremental as it builds on existing LTL and SMT methods.
The paper tackles the problem of robot path planning under Linear Temporal Logic (LTL) specifications in dynamic environments with obstacles, presenting DT*, an SMT-based receding horizon strategy that effectively handles dynamism, as shown in experiments on a pick-and-drop warehouse application.
Path planning for a robot is one of the major problems in the area of robotics. When a robot is given a task in the form of a Linear Temporal Logic (LTL) specification such that the task needs to be carried out repetitively, we want the robot to follow the shortest cyclic path so that the number of times the robot completes the mission within a given duration gets maximized. In this paper, we address the LTL path planning problem in a dynamic environment where the newly arrived dynamic obstacles may invalidate some of the available paths at any arbitrary point in time. We present DT*, an SMT-based receding horizon planning strategy that solves an optimization problem repetitively based on the current status of the workspace to lead the robot to follow the best available path in the current situation. We implement our algorithm using the Z3 SMT solver and evaluate it extensively on an LTL specification capturing a pick-and-drop application in a warehouse environment. We compare our SMT-based algorithm with two carefully crafted greedy algorithms. Our experimental results show that the proposed algorithm can deal with the dynamism in the workspace in LTL path planning effectively.