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

Sotiris Papatheodorou, Michalis Smyrnakis, Tembine Hamidou, Anthony Tzes

TL;DR

The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article and an approach using elements from game theory is employed.

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.

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

TL;DR

The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article and an approach using elements from game theory is employed.

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.

Paper Structure

This paper contains 12 sections, 18 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem statement
  3. Energy efficient path computation
  4. Agent Kinematic Model
  5. Energy Efficient Path
  6. Path construction
  7. Game Theory Preliminaries
  8. Game Theory
  9. Learning in games
  10. Utility formulation
  11. Simulation Studies
  12. Conclusions

Figures (5)

  • Figure 2: Splitting of the robot team into subgroups and region assignment using the Generalized Voronoi diagram.
  • Figure 3: Team's cost for the first case.
  • Figure 4: Team's cost for the third case.
  • Figure 5: Trajectories of the robots for the first case when Fictitious play algorithm is used.
  • Figure 6: Trajectories of the robots for the second case when Joint strategy fictitious play algorithm is used.