About

1 About This Survey

This website presents a comprehensive survey on Safe-Optimal Control for Motion Planning based on Reinforcement Learning. The survey covers the intersection of control theory, safety constraints, and reinforcement learning approaches for autonomous systems.

1.1 Scope

The survey encompasses several key areas:

Control Theory Foundations: Classical optimal control methods, model predictive control, and control-theoretic approaches
Safety Considerations: Robust control, risk-aware planning, and constraint satisfaction in uncertain environments
Learning Approaches: Reinforcement learning theory and applications, imitation learning, and inverse reinforcement learning
Motion Planning: Search-based, sampling-based, optimization-based, and reactive planning methods
Multi-agent Systems: Game theory and multi-agent reinforcement learning for coordinated control

1.2 Applications

The methods covered in this survey have broad applications including:

Autonomous Vehicles: Self-driving cars, autonomous navigation, and intelligent transportation systems
Robotics: Manipulation, locomotion, and human-robot interaction
Aerospace: Unmanned aerial vehicles (UAVs), spacecraft control, and air traffic management
Industrial Systems: Process control, manufacturing automation, and supply chain optimization
Healthcare: Medical robotics, assistive devices, and automated diagnosis systems

1.3 Methodology

This survey follows a systematic approach:

Literature Review: Comprehensive coverage of peer-reviewed papers, conference proceedings, and technical reports
Taxonomic Organization: Structured categorization of methods by approach and application domain
Comparative Analysis: Discussion of trade-offs, advantages, and limitations of different approaches
Future Directions: Identification of open challenges and promising research directions

1.4 Target Audience

This survey is designed for:

Researchers working on control theory, robotics, and machine learning
Engineers developing autonomous systems and intelligent control applications
Graduate Students studying control systems, robotics, or reinforcement learning
Industry Practitioners seeking to understand state-of-the-art methods for safe autonomous systems

1.5 Structure

The survey is organized into the following main sections:

Optimal Control: Fundamental control theory and optimization methods
Safe Control: Safety-critical control and robust planning approaches
Reinforcement Learning: RL theory, algorithms, and applications to control
Learning from Demonstrations: Imitation learning and inverse RL
Motion Planning: Planning algorithms and their integration with learning

1.6 Citation

If you find this survey useful in your research, please cite it as:

@misc{safe_optimal_control_survey_2022,
  title={Safe-Optimal Control for Motion Planning based on Reinforcement Learning: Survey},
  year={2022},
  url={https://phatcvo.github.io/Safe-Optimal-Control},
  note={Accessed: [Date]}
}

1.7 Contact

For questions, suggestions, or corrections regarding this survey, please open an issue on the GitHub repository.

Last updated: August 2025