Journal ArticleDOI
Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles
TLDR
It was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59.Abstract:
The BPF proposal ensures a feasible, optimal and safe path for robot navigation.The results of BPF overcomes APF and other EAPF methods like those based in GAs.The BPF is quite faster in optimization leading to reduction in computation burden.The BPF running in parallel mode is the most suitable to fulfill local and global controllability.The BPF is capable to work in offline and online mode with static and dynamic obstacles. In this paper, optimal paths in environments with static and dynamic obstacles for a mobile robot (MR) are computed using a new method for path planning. The proposed method called Bacterial Potential Field (BPF) ensures a feasible, optimal and safe path. This novel proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method taking all the advantages of using the APF method, strongly reducing its disadvantages. Comparative experiments for sequential and parallel implementations of the BPF method against the classic APF method, as well as with the Pseudo-Bacterial Potential Field (PBPF) method, and with the Genetic Potential Field (GPF) method, all of them based on evolutionary computation to optimize the APF parameters, were achieved. A simulation platform that uses an MR realistic model was designed to test the path planning algorithms. In general terms, it was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59. These results have a positive impact in the ability of the BPF path planning method to satisfy local and global controllability in dynamic complex environments, avoiding collisions with objects that will interfere the navigation of the MR.read more
Citations
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Journal ArticleDOI
A review: On path planning strategies for navigation of mobile robot
TL;DR: It has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches and are used to improve the performance of the classical approaches as a hybrid algorithm.
Journal ArticleDOI
Mobile robot path planning using membrane evolutionary artificial potential field
TL;DR: The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length, and it exhibits a better performance regarding path length.
Journal ArticleDOI
Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles
TL;DR: In this article, a real-time dynamic path planning method for autonomous driving that avoids both static and moving obstacles is presented, which determines not only an optimal path, but also the appropriate acceleration and speed for a vehicle.
Journal ArticleDOI
Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm
Anish Pandey,Dayal R. Parhi +1 more
TL;DR: A singleton type-1 fuzzy logic system (T1-SFLS) controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment is introduced.
Journal ArticleDOI
Person-following by autonomous robots: A categorical overview:
TL;DR: This paper provides a comprehensive overview of the literature by categorizing different aspects of person-following by autonomous robots and state-of-the-art methods for perception, planning, control, and interaction and their applicability in varied operational scenarios.
References
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