Energy-Efficient Intent Estimation And Distributed Optimal Control For Human-Multi-Robot Collaboration
Abstract
This paper introduces an energy-efficient distributed optimal control framework for human-multi-robot cooperative manipulation, combining a spiking neural network (SNN) observer for biologically inspired intent estimation with a game-theoretic distributed optimal control strategy for coordination. At the estimation level, the event-driven SNN captures human motor intent from local force feedback and consensus information, enabling real-time trajectory estimation through sparse spike processing. By operating on asynchronous events, it reduces computation and energy relative to conventional networks, while online adaptive weight updates preserve accuracy under dynamic uncertainties. At the control level, a NN-based actor-critic architecture applies adaptive dynamic programming within a cooperative game-theoretic setting. Coupled Hamilton-Jacobi-Bellman equations are solved through neighborhood optimization, allowing each robot to minimize a performance cost that incorporates local dynamics, neighboring interactions, and human-robot force coordination. Simulation results on human-multi-robot collaboration demonstrate improved tracking with a significant reduction in operational cost compared to state-of-the-art methods. The SNN-based observer also achieves a 60% reduction in energy consumption while maintaining estimation accuracy, supporting real-time deployment on resource-constrained robotic systems.
Recommended Citation
I. Ganie and S. Jagannathan, "Energy-Efficient Intent Estimation And Distributed Optimal Control For Human-Multi-Robot Collaboration," Proceedings of the American Control Conference, pp. 4060 - 4065, Institute of Electrical and Electronics Engineers, Jan 2026.
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Distributed optimal control; Human-multi-robot collaboration; Intent estimation; Spiking neural networks
International Standard Book Number (ISBN)
979-8-3315-9381-0; 979-8-3315-9382-7
International Standard Serial Number (ISSN)
0743-1619
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2026
Comments
Army Research Office, Grant W911NF-24-2-0178