Epistemic Planning for Heterogeneous Robotic Systems

TL;DR

This paper introduces an epistemic planning framework enabling heterogeneous robotic systems to reason about their knowledge states, improve communication, and optimize task allocation despite unreliable communication channels in complex, time-sensitive missions.

Contribution

It presents a novel epistemic planning approach using dynamic epistemic logic and mixed integer programming for belief management and task allocation in heterogeneous multi-robot systems.

Findings

Framework improves information dissemination efficiency

Validated through simulations and real experiments

Enhances robustness in unreliable communication environments

Abstract

In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles.