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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
autonomous vehiclesglobal path planninglocal path planningnavigation algorithmsobstacle avoidance

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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.