Critic-actor reinforcement learning for optimized cooperative formation of multi-nonholonomic wheeled Mobile vehicles
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an optimized formation control strategy for multiple nonholonomic wheeled mobile vehicles using critic-actor reinforcement learning and adaptive neural networks. The method effectively manages complex dynamics for cooperative navigation.
Area Of Science
- Robotics
- Control Systems
- Artificial Intelligence
Background
- Formation control for multi-vehicle systems is complex due to under-actuation and nonholonomic constraints.
- Solving the Hamilton-Jacobi-Bellman equation for optimized formation protocols is challenging.
- Existing methods struggle with uncertainties in Lagrange dynamics.
Purpose Of The Study
- To develop an optimized leader-follower formation control scheme for multiple nonholonomic wheeled mobile vehicles (MNWMVs).
- To integrate critic-actor reinforcement learning (RL) with adaptive neural networks (NN) for robust formation tracking.
- To address nonlinear and coupled characteristics of the Hamilton-Jacobi-Bellman equation.
Main Methods
- A distributed cooperative formation scheme combining kinematic and dynamic torque controllers.
- Integration of an adaptive identifier within the critic-actor RL strategy to handle Lagrange dynamics uncertainties.
- Utilizing RL training laws derived from the negative gradient of a positive function for simplification.
Main Results
- A novel optimized formation tracking algorithm for MNWMVs was developed.
- The adaptive identifier effectively compensated for uncertainties in Lagrange dynamics.
- The RL training laws simplified the overall formation scheme.
Conclusions
- The proposed distributed cooperative formation scheme is effective for MNWMVs.
- The integration of RL and adaptive NNs provides a robust solution for complex formation control problems.
- Numerical simulations and physical experiments validated the theoretical findings.
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