Attraction-Based Receding Horizon Path Planning with Temporal Logic Constraints

TL;DR

This paper presents a novel motion planning framework for robots that balances high-level temporal logic surveillance tasks with local reward collection in dynamic environments, using automata-based methods and user preferences.

Contribution

It introduces a new approach combining automata-based model checking with reward optimization and user preferences for complex robot motion planning.

Findings

Framework effectively balances surveillance and reward collection.

Demonstrated benefits in a simulated example.

Handles arbitrary reward dynamics and user preferences.

Abstract

Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. We assume that arbitrary assumptions on the reward dynamics can be given. The robot aims to accomplish a high-level temporal logic surveillance mission and to locally optimize the collection of the rewards in the visited regions. These two objectives often conflict and only a compromise between them can be reached. We address this issue by taking into consideration a user-defined preference function that captures the trade-off between the importance of collecting high rewards and the importance of making progress towards a surveyed region. Our solution leverages ideas from the automata-based approach to model checking. We demonstrate the utilization and benefits of the suggested framework in an illustrative example.