Path Planning and Task Assignment for Data Retrieval from Wireless Sensor Nodes Relying on Game-Theoretic Learning

TL;DR

This paper presents a game-theoretic approach for energy-efficient path planning and task assignment of mobile robots retrieving data from sensor nodes, enabling near real-time operation with high efficiency.

Contribution

It introduces a novel game-theoretic method for solving the complex energy-efficient trip allocation problem, bypassing NP-hard formulations for faster practical solutions.

Findings

The approach converges quickly in most practical scenarios.

Simulations demonstrate high efficiency and near real-time applicability.

The method reduces energy consumption compared to traditional algorithms.

Abstract

The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article. Given a team of robots and a set of targets (wireless sensor nodes), the planner computes all possible tours that each robot can make if it needs to visit a part of or the entire set of targets. Each segment of the tour relies on a minimum energy path planning algorithm. After the computation of all possible tour-segments, a utility function penalizing the overall energy consumption is formed. Rather than relying on the NP-hard Mobile Element Scheduling (MES) MILP problem, an approach using elements from game theory is employed. The suggested approach converges fast for most practical reasons thus allowing its utilization in near real time applications. Simulations are offered to highlight the efficiency of the developed algorithm.