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Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot
Kornél Katona1, Husam A Neamah1, Péter Korondi1
1Department of Electrical Engineering and Mechatronics, Faculty of Engineering, University of Debrecen, 4028 Debrecen, Hungary.
Sensors (Basel, Switzerland)
|June 19, 2024
Summary
This article reviews obstacle avoidance algorithms for robotics and autonomous vehicles. It covers classic and modern techniques to ensure safe navigation and collision prevention in dynamic environments.
Area of Science:
- Robotics and Artificial Intelligence
- Autonomous Systems Navigation
Background:
- Path planning is essential for robots to navigate environments and reach destinations.
- Obstacle avoidance is a critical component of path planning, ensuring collision-free autonomous operation.
- Effective obstacle avoidance algorithms are vital for the safe and efficient functioning of robots and autonomous vehicles.
Purpose of the Study:
- To provide a comprehensive overview of key obstacle avoidance algorithms in robotics.
- To analyze the advantages, limitations, and applications of various obstacle avoidance techniques.
- To highlight current research trends and future prospects in the field of obstacle avoidance robotics.
Main Methods:
- Review of classic obstacle avoidance algorithms (e.g., Bug algorithm, Dijkstra's algorithm).
- Exploration of modern approaches including genetic algorithms and neural network-based methods.
- Analysis of predictive methods and deep learning strategies for enhanced obstacle avoidance.
Main Results:
- Detailed comparison of the strengths and weaknesses of different obstacle avoidance algorithms.
- Identification of suitable application areas for each algorithmic approach.
- Insight into the evolution and current state-of-the-art in obstacle avoidance technology.
Conclusions:
- Obstacle avoidance algorithms are fundamental to advancing robotics and autonomous systems.
- Continued research in areas like deep learning and predictive methods promises more robust navigation solutions.
- The selection of an appropriate algorithm depends on specific application requirements and environmental complexities.
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