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Deep Reinforcement Learning for Collision-Aware Leader-Follower Formation Control
Sławomir Romaniuk1, Jakub Budnik1
1Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Bialystok University of Technology, 15-351 Bialystok, Poland.
Sensors (Basel, Switzerland)
|July 15, 2026
Summary
This study uses Proximal Policy Optimization (PPO) for adaptive leader-follower navigation, improving safety and tracking during unpredictable maneuvers. The PPO follower offers a better safety-tracking balance than baseline methods.
Area of Science:
- Robotics and Control Systems
- Artificial Intelligence and Machine Learning
Background:
- Leader-follower navigation is critical for multi-agent systems.
- Unpredictable leader movements pose significant collision risks.
Purpose of the Study:
- To develop an adaptive follower strategy for safe leader-follower navigation.
- To enhance tracking performance while maintaining a minimum safety distance.
Main Methods:
- Proximal Policy Optimization (PPO) was employed to train the follower agent.
- A specialized reward function integrated formation constraints, collision avoidance, and control effort.
Main Results:
- The PPO-based follower demonstrated a superior safety-tracking trade-off compared to A2C and PID baselines.
- Effective policy learning was achieved through the task-specific reward function.
Conclusions:
- PPO is a viable method for adaptive follower control in dynamic environments.
- While improved, collision-free operation remains a challenge in all scenarios.
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