A Comprehensive Review of Path Planning Techniques for Mobile Robot Navigation in Known and Unknown Environments
DOI:
https://doi.org/10.22399/ijcesen.797Keywords:
Autonomous Navigation, Robotics, Robot Kinematics, Robotic Process AutomationAbstract
The exponential increase in the utilisation of mobile robots in day-to-day life emphasizes the need for effective path-planning algorithms that allow them to navigate safely and reliably through unknown or known environments. Path planning is the procedure in which a prime and secure path needs to be determined for the robot to relocate from source to destination. Discovering a collision-free path may be the most difficult aspect for mobile robots to navigate. Several optimal path-planning techniques have been proposed until now for finding optimal paths from source to sink in the presence of obstacles, which are essential for cost-effectiveness in terms of time of traversal and resource utilization. This paper gives a critical review of classical, heuristic and hybrid path-planning techniques. Classical technologies such as Cell Decomposition, Potential Field Methods and Roadmap Methods are characterized by computation efficiencies which range from time complexity of O(nlogn) to O(n2), and these techniques have the limitation of being not suitable for dynamic environments. Heuristic techniques that provide more flexibility in dynamic environments include Bacterial Foraging Techniques, Particle Swarm Optimization, Genetic Algorithms ,Artificial Neural Networks, Fuzzy Logic, Ant Colony Optimization, and Particle Swarm Optimization. Ant Colony Optimization and Particle Swarm Optimization provide robust real-time adaptability with very high consumption in computational resources--typically under O(WL) and O(NL) time complexity, respectively. Hybrid techniques indicate that benefits from the classical and heuristic methods reduce the path length and enhance the energy efficiency comparatively to classical methods. Hybrid techniques generally have the order of time complexity, about O(n2), to find a balance between real-time adaptability and computational efficiency. Path length, smoothness, safety degree, etc., are important optimization criteria. It assesses Key optimization criteria, such as path length, smoothness, safety level, and energy efficiency. This paper also discusses the integration of robot modelling with path planning methodologies, emphasising the importance of considering robot dynamics and kinematics. Finally, the review discusses potential directions of research in this area with a roadmap for futuristic mobile robot path planning techniques.
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