To Explore or Not to Explore: Regret-Based LTL Planning in Partially-Known Environments

TL;DR

This paper introduces a regret-based planning approach for robots operating in partially-known environments with temporal logic specifications, optimizing for minimal regret rather than worst-case cost.

Contribution

It proposes a novel regret metric for LTL planning in unknown environments and provides an algorithm to find optimal plans minimizing this regret.

Findings

The regret-based approach effectively balances exploration and exploitation.

The algorithm finds plans satisfying LTL specifications with minimal regret.

Case study demonstrates practical applicability in firefighting robots.

Abstract

In this paper, we investigate the optimal robot path planning problem for high-level specifications described by co-safe linear temporal logic (LTL) formulae. We consider the scenario where the map geometry of the workspace is partially-known. Specifically, we assume that there are some unknown regions, for which the robot does not know their successor regions a priori unless it reaches these regions physically. In contrast to the standard game-based approach that optimizes the worst-case cost, in the paper, we propose to use regret as a new metric for planning in such a partially-known environment. The regret of a plan under a fixed but unknown environment is the difference between the actual cost incurred and the best-response cost the robot could have achieved if it realizes the actual environment with hindsight. We provide an effective algorithm for finding an optimal plan that satisfies the LTL specification while minimizing its regret. A case study on firefighting robots is provided to illustrate the proposed framework. We argue that the new metric is more suitable for the scenario of partially-known environment since it captures the trade-off between the actual cost spent and the potential benefit one may obtain for exploring an unknown region.