Path Planning Using Reinforcement Learning

Professor Introduction

J. W | Ph.D. in  Mechanical Engineering

Home Institute：Monash University

Project Description

Path planning is a crucial component of robotics and autonomous systems, responsible for determining the most efficient or optimal route from a starting point to a destination while avoiding obstacles. Traditional path planning methods, such as A* and Dijkstra's algorithm, have been extensively studied and applied. However, these methods typically require a known environment and may not adapt well to dynamic or uncertain conditions. With the advent of machine learning technologies, particularly reinforcement learning (RL), there is an opportunity to develop more adaptive and robust path planning algorithms. RL provides a framework for decision-making in uncertain environments, making it highly suitable for path planning in complex dynamic scenarios. Moreover, control system theory offers a mathematical foundation for system stability and performance, which is crucial for the safe operation of autonomous agents. The integration of RL with control systems could result in a path planning algorithm that is not only adaptive but also mathematically sound, ensuring robustness and reliability.

Project Keywords

Project Outline

Part 1 :  Introduction to  Path Planning and Reinforcement Learning
• Overview of Traditional Path Planning Methods (A*, Dijkstra)
• Introduction to Reinforcement Learning and its Applications
• Importance of Adaptive Path Planning in Dynamic Environments

Part 2 : Research Objectives and Hypotheses
• Investigating the Effectiveness of RL-Based Path Planning Algorithms
• Developing and Optimizing the Path Planning Algorithm for Dynamic Environments

Part 3 :  Review of Current Research and Methods
• Review of Existing Path Planning Algorithms and RL Techniques
• Identification of Gaps and Limitations in Current Technologies

Part 4:  Design and Simulation of Path Planning Algorithms
• Designing the RL-Based Path Planning Algorithm
• Setting Up Simulation Environments and Parameters
• Implementing Control System Theory for Stability and Performance

Part 5 :  Development of Reinforcement Learning Models
• Collecting and Processing Training Data
• Developing and Training RL Models (using frameworks like TensorFlow or PyTorch)
• Integrating RL Models with Path Planning Algorithms

Part 6 :  Prototype Manufacturing and Testing
• Testing the Algorithm in Simulated Dynamic Environments
• Debugging and Initial Performance Evaluation
• Comparing RL-Based Path Planning with Traditional Methods

Part 7: Optimization and Parameter Tuning
• Identifying Optimal Parameters for Maximum Adaptability
• Iteratively Optimizing the RL Model for Better Performance
• Quantitative Analysis of Path Planning Efficiency and Robustness

Part 8: Results and Discussion
• Graphical Representation of Experimental Results and Performance Metrics
• Interpretation of Results and Discussion of Implications for Autonomous Systems
• Comparison with Existing Path Planning Technologies and Discussion of Advantages and Limitations

Part 9: Conclusion and Future Directions
• Summary of Key Findings and Their Significance
• Identification of Research Limitations and Suggestions for Future Research
• Recommendations for Practical Applications in Robotics and Autonomous Systems

Part 10: Reporting and Presentation
• Writing a Detailed Research Report with Clear Structure, Concise Language, and Accurate Data Presentation
• Preparing and Delivering a Clear and Engaging Oral Presentation of Research Background, Methods, Results, and Conclusions

Suitable for

High School Students:  
• Interest in Robotics and AI : Students with a strong interest in robotics, artificial intelligence, and path planning.
• Basic Knowledge : Students with a basic understanding of programming, algorithms, and mathematics.

University Students:
• Relevant Major :  Students majoring in mechanical engineering, electrical engineering, computer science, or related fields.
• Proficiency in Software Tools : Students with skills in programming languages (Python, C++), and familiarity with machine learning frameworks (TensorFlow, PyTorch).