Abstract
This work considers online optimal motion planning of an autonomous agent subject to linear temporal logic (LTL) constraints. Since user-specified tasks may not be fully realized (i.e., partially infeasible) in a complex environment, this work considers hard and soft LTL constraints, where hard constraints enforce safety requirements (e.g. avoid obstacles) while soft constraints represent tasks that can be relaxed to not strictly follow user specifications. The motion planning of the agent is to generate trajectories, in decreasing order of priority, to 1) guarantee the satisfaction of safety constraints; 2) mostly fulfill soft constraints (i.e., minimize the violation cost if desired tasks are partially infeasible); 3) locally optimize rewards collection over a finite horizon. To achieve these objectives, receding horizon control is synthesized with an LTL formula to maximize the accumulated utilities over a finite horizon, while ensuring that safety constraints are fully satisfied and soft constraints are mostly satisfied. Simulation and experiment results are provided to demonstrate the effectiveness of the developed motion strategy.
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