A novel path planning algorithm based on plant growth mechanism

Zhou, Yaoming; Wang, Yongchao; Chen, Xuzhi; Zhang, Lei; Wu, Kan

doi:10.1007/s00500-016-2045-x

Cited by 32 publications

(30 citation statements)

References 37 publications

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“…Although less frequently used, there exist some other nature-inspired heuristics. The plant growth path planning (PGPP) algorithm [13] was inspired by plant growth and the way in which plants seek any light source. The experimental results demonstrated that the PGPP algorithm could improve path planning results.…”

Section: Literature Reviewmentioning

confidence: 99%

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Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

et al. 2021

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With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios.

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Section: Literature Reviewmentioning

confidence: 99%

“…A hybrid path planning algorithm is proposed in [12] which combines the A* and D* lite [13] algorithms. The proposed hybrid algorithm requires less computation time and demonstrated better re-planning results compared to the original algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

et al. 2021

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“…Apart from that, the BCO possess the ability to settle deterministic combinatorial problems, including combinatorial problems that are categorized by uncertainty [107]. Other than that, BCO has been utilized for the purpose of devising path in mobile robots [120]- [122]. The challenges of this study refer to the effort of finding out the trajectory of motion of the robots.…”

Section: Swarm Intelligencementioning

confidence: 99%

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

Nurmaini

Tutuko

2017

IJECE

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This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments.

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“…Although these traditional methods have achieved good results, they have some disadvantages, such as expensive computation in high dimensions, lack of adaptation, and the local minima, which make them ineffective in a practical situation. On the other hand, a wide variety of heuristic methodologies, such as neural networks, 9 artificial bee colony, 10 cuckoo optimization algorithm, 11 slime mold algorithm, 12 plant growth mechanism, 13 ant colony algorithm (ACA), [14][15][16][17] and so on, have been introduced for solving the aforementioned disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

Zhang

et al. 2021

International Journal of Advanced Robotic Systems

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Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

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A novel path planning algorithm based on plant growth mechanism

Cited by 32 publications

References 37 publications

Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

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