Explainable AI-Enhanced Supervisory Control for Robust Multi-Agent Robotic Systems

2025-09-18

AI Summary

Safety-critical, resource-constrained multi-agent robotic systems—such as spacecraft formations and autonomous underwater vehicles—demand rigorous safety guarantees, robust trajectory tracking under disturbances, and interpretable, adaptive control. Method: This paper proposes an explainable AI-enhanced hierarchical supervisory control framework integrating (i) timed automata for verifiable, auditable mode switching; (ii) a hybrid Lyapunov-based stabilizing controller and boundary-layer sliding-mode controller to ensure robust large-angle maneuvers and high-precision disturbance rejection; and (iii) an explainable AI predictor that dynamically maps task context to control parameters and performance expectations. Contribution/Results: Evaluated under six-degree-of-freedom rigid-body dynamics and relative-motion constraints, the framework demonstrates cross-domain portability and reliability. Experiments show that, compared to a PD baseline, the sliding-mode controller reduces tracking error by 21.7% and energy consumption by 81.4%, while maintaining bounded steady-state error under stochastic disturbances.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

We present an explainable AI-enhanced supervisory control framework for multi-agent robotics that combines (i) a timed-automata supervisor for safe, auditable mode switching, (ii) robust continuous control (Lyapunov-based controller for large-angle maneuver; sliding-mode controller (SMC) with boundary layers for precision and disturbance rejection), and (iii) an explainable predictor that maps mission context to gains and expected performance (energy, error). Monte Carlo-driven optimization provides the training data, enabling transparent real-time trade-offs. We validated the approach in two contrasting domains, spacecraft formation flying and autonomous underwater vehicles (AUVs). Despite different environments (gravity/actuator bias vs. hydrodynamic drag/currents), both share uncertain six degrees of freedom (6-DOF) rigid-body dynamics, relative motion, and tight tracking needs, making them representative of general robotic systems. In the space mission, the supervisory logic selects parameters that meet mission criteria. In AUV leader-follower tests, the same SMC structure maintains a fixed offset under stochastic currents with bounded steady error. In spacecraft validation, the SMC controller achieved submillimeter alignment with 21.7% lower tracking error and 81.4% lower energy consumption compared to Proportional-Derivative PD controller baselines. At the same time, in AUV tests, SMC maintained bounded errors under stochastic currents. These results highlight both the portability and the interpretability of the approach for safety-critical, resource-constrained multi-agent robotics.

Problem

• Developing explainable AI supervisory control for multi-agent robotic systems
• Ensuring robust performance under uncertain dynamics and disturbances
• Optimizing real-time trade-offs between energy consumption and tracking precision

Innovation

• Timed-automata supervisor for safe mode switching
• Sliding-mode controller with boundary layers for precision
• Explainable predictor mapping mission context to performance