Abstract

Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system.

Highlights

  • The success of an autonomous, robotic mission can be measured by the effectiveness and efficiency in completing the mission [1]

  • We propose an information consensus control scheme in which position of the agents, communication frequency, and the shared information state are simultaneously co-regulated in a single framework

  • The agents took 43 s to orbit the areas of interest once and to start communicating the individual temperature states

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Summary

Introduction

The success of an autonomous, robotic mission can be measured by the effectiveness and efficiency in completing the mission [1]. Small wearable temperature sensors can be used by firefighters to build a shallow, but serviceable temperature profile This offline data gathering method does not help in monitoring the live fire and requires physical access to the area selected for the burn. Information consensus [10] provides an excellent strategy for estimating the temperature profile with multiple, moving agents in a decentralized fashion In this strategy, improved measurements can be obtained by being closer to the point of interest, which may restrict communication with other agents given the topography. There are times when it is prudent for each agent to fly close to the area in question, change its frequency of communication, or fly higher to communicate its value with others In this scenario, an online co-regulated and co-designed information consensus algorithm can dynamically adjust cyber, physical, and communication resources in response to holistic, multi-agent system performance

Summary of Approach
Related Work
Graph Theory in Consensus
Matrix Theory for Consensus
Discrete Information Consensus
Linear Quadratic Regulator Problem
Co-Regulation for Information Consensus
Co-Regulated Communication Consensus
Co-Regulated Position Consensus
Convergence Guarantees
Performance Metrics
Convergence Time
Average Cost of Communications
Average Error in Converged Value
Simulation Setup
LQR Design
Experiment Design
Co-Regulation of Multiple Shared States
Results
Neighbors
Conclusions
Full Text
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