Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
Source abstract
Abstract
The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Path Planning Algorithms · Robotics and Sensor-Based Localization · Control and Dynamics of Mobile Robots
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechatronics Engineering, Atilim University, Ankara 06830, Turkey
Defense Technologies Institute, Gebze Technical University, Kocaeli 41400, Turkey
Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
References (30)
Show source-provided references
1. Searching and tracking people with cooperative mobile robots10.1007/s10514-017-9681-6 ↗
2. Aerial hose type robot by water jet for fire fighting10.1109/LRA.2018.2792701 ↗
3. Muijzert, E., and Welker, K.E. (2019). Seismic Data Acquisition Using Water Vehicles. (10,191,170), U.S. Patent.
4. Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses10.1177/0022243718822827 ↗
5. CPS oriented control design for networked surveillance robots with multiple physical constraints10.1109/TCAD.2016.2524653 ↗
6. Formation control and tracking for co-operative robots with non-holonomic constraints10.1007/s10846-015-0287-y ↗
7. Mobile robot navigation and obstacle avoidance techniques: A review
8. Path planning of an autonomous mobile robot using directed artificial bee colony algorithm
9. Probabilistic roadmaps for path planning in high-dimensional configuration spaces10.1109/70.508439 ↗
10. Berger, J., Jabeur, K., Boukhtouta, A., Guitouni, A., and Ghanmi, A. (2010). A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment. IEEE Congress on Evolutionary Computation, IEEE.10.1109/CEC.2010.5586311 ↗
11. Li, S., Ding, M., Cai, C., and Jiang, L. (2010, January 13–15). Efficient path planning method based on genetic algorithm combining path network. Proceedings of the Fourth International Conference on Genetic and Evolutionary Computing, Shenzhen, China.
12. Planning multiple paths with evolutionary speciation10.1109/4235.930309 ↗
13. An efficient stochastic clustering auction for heterogeneous robotic collaborative teams10.1007/s10846-012-9800-8 ↗
14. Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN’95—International Conference on Neural Networks, Perth, Australia.
15. Alam, M.S., Rafique, M.U., and Khan, M.U. (2020). Mobile robot path planning in static environments using particle swarm optimization. arXiv.
16. Ant colony test center for planning autonomous mobile robot navigation10.1002/cae.20463 ↗
17. Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique10.1016/j.robot.2014.07.002 ↗
18. Abraham, A., Hassanien, A.E., Siarry, P., and Engelbrecht, A. (2009). Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications. Foundations of Computational Intelligence Volume 3: Global Optimization, Springer.
19. Heuristic approaches in robot path planning: A survey10.1016/j.robot.2016.08.001 ↗
20. Bio-mimetic trajectory generation of robots via artificial potential field with time base generator10.1109/TSMCC.2002.807273 ↗
21. Khatib, O. (1986). Real-Time Obstacle Avoidance for Manipulators and Mobile Robots, Springer.10.1007/978-1-4613-8997-2_29 ↗
22. Swarm formation control utilizing elliptical surfaces and limiting functions10.1109/TSMCB.2009.2018139 ↗
23. Artificial potential field based path planning for mobile robots using a virtual obstacle concept
24. Wu, Q., Chen, Z., Wang, L., Lin, H., Jiang, Z., Li, S., and Chen, D. (2020). Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm. Sensors, 20.10.3390/s20010188 ↗
25. Biomimicry of bacterial foraging for distributed optimization and control10.1109/MCS.2002.1004010 ↗
26. Bacteria colony approaches with variable velocity applied to path optimization of mobile robots
27. A new online random particles optimization algorithm for mobile robot path planning in dynamic environments10.1155/2013/491346 ↗
28. Path planning of an autonomous mobile robot using enhanced bacterial foraging optimization algorithm10.22153/kej.2016.01.001 ↗
29. Dunias, P. (1996). Autonomous Robots Using Artificial Potential Fields. [Ph.D. Thesis, Technische Universiteit Eindhoven].
30. Cook, G. (2011). Mobile Robots: Navigation, Control and Remote Sensing, John Wiley & Sons.10.1002/9781118026403 ↗