Collaborative Rover-copter Path Planning and Exploration with Temporal Logic Specifications Based on Bayesian Update Under Uncertain Environments

Abstract

This paper investigates a collaborative rover-copter path planning and exploration with temporal logic specifications under uncertain environments. The objective of the rover is to complete a mission expressed by a syntactically co-safe linear temporal logic (scLTL) formula, while the objective of the copter is to actively explore the environment and reduce its uncertainties, aiming at assisting the rover and enhancing the efficiency of the mission completion. To formalize our approach, we first capture the environmental uncertainties by environmental beliefs of the atomic propositions, under an assumption that it is unknown which properties (or, atomic propositions) are satisfied in each area of the environment. The environmental beliefs of the atomic propositions are updated according to the Bayes rule based on the Bernoulli-type sensor measurements provided by both the rover and the copter. Then, the optimal policy for the rover is synthesized by maximizing a belief of the satisfaction of the scLTL formula through an implementation of an automata-based model checking. An exploration policy for the copter is then synthesized by employing the notion of an entropy that is evaluated based on the environmental beliefs of the atomic propositions, and a path that the rover intends to follow according to the optimal policy. As such, the copter can actively explore regions whose uncertainties are high and that are relevant to the mission completion. Finally, some numerical examples illustrate the effectiveness of the proposed approach.

Active Perception Policy Learning by Reinforcement Learning

“…Support of belief Belief update trigger Sharan et al [16] States No update Bouton et al [17] States Observation Hashimoto et al [18] States Event detection RMSM [20] Automaton states Direct learning PUnS [8] LTL…”

Finite-Horizon Shield for Path Planning Ensuring Safety/Co-Safety Specifications and Security Policies

“…This logic is referred to as LTL f [27], [28], [29], [30], [31]. Moreover, safety and co-safety properties are important [32], [33]. A typical example of co-safety properties is that a robot reaches a goal state in a finite horizon.…”