High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2607.03512v1
• Date: Fri, 03 Jul 2026 17:38:17 GMT
• Title: High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning
• Abstract summary: This paper proposes a hierarchical hybrid physics-informed deep reinforcement learning (HHy-PIDRL) framework.<n>It aims to realize high-precision, highly responsive formation control for heterogeneous multi-robot systems.<n> Experimental results demonstrate that, the success rate of both the upper-layer autonomous navigation policy network and the HM-DRL based formation control policy networks reach 100%.
• Score: 10.865402396881416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing classical control methods commonly require precise models and struggle to cope with model uncertainties and external disturbances, while end-to-end reinforcement learning (RL) approaches suffer from low sample efficiency and poor convergence. To overcome these challenges, this paper proposes a hierarchical hybrid physics-informed deep reinforcement learning (HHy-PIDRL) framework, aiming to realize high-precision, highly responsive formation control for heterogeneous multi-robot systems (HMRSs). The proposed framework contains two layers. Specifically, first, the upper layer designs an autonomous navigation policy network for Ackermann-steering leader based on the Soft Actor-Critic (SAC) deep reinforcement learning (DRL) algorithm. Second, the lower module integrates a high-fidelity physical feed-forward controller, a classical proportional-derivative (PD) controller, and an adaptive DRL residual controller to propose an effective hybrid model and DRL (HM-DRL)-based formation control policy network. Third, a unique hierarchical reward function is designed for training Omnidirectional followers, which effectively guides agents toward a refined, stable control policy. Experimental results demonstrate that, the success rate of both the upper-layer autonomous navigation policy network and the HM-DRL based formation control policy networks reach 100%. Meanwhile, ablation experiments are conducted to verify the validity and credibility of the proposed method.

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