Efficient Path Planning Method Based on Genetic Algorithm Combining Path Network

Li, Shidong; Ding, Mingyue; Cai, Chengtao; Jiang, Lei

doi:10.1109/icgec.2010.55

Cited by 15 publications

(4 citation statements)

References 7 publications

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“…However, the path obtained by steepest descent method based on time distance map is not always satisfy the mobility constraints for UAVs [10]; on the other hand, turning continuously means high fuel consumption and more difficult to control for UAVs. Therefore, the path is usually expressed as combination of line segment and arc.…”

Section: D Path Planning Methodsmentioning

confidence: 99%

Fast 3D Path Pre-Planning Method for UAVs

Song

et al. 2013

AMM

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Path pre-planning generate the feasible conference path between start and destination for UAVs, it relates to restrictions from UAVs itself and environment information. Generally, the path is produced by searching in configuration space with some intelligent searching algorithms, the space is always so huge that it is difficult to obtain high efficiency, even lead to NP hard. We proposed a fast 3D path pre-planning method which plan 2D path firstly and then process height planning based on obtained 2D path, considering typical constraints such as turning angle, climbing/diving angle and static threat avoidance. Fast Marching Method (FMM) and Sparse A-Star (SAS) searching method are used in the course of 2D path planning and height planning respectively, Experiments showed that the proposed method generates path quickly, the obtained path follows terrain and avoids obstacles well.

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Section: D Path Planning Methodsmentioning

confidence: 99%

Fast 3D Path Pre-Planning Method for UAVs

Song

et al. 2013

AMM

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“…Finally, the method was applied to the road network to plan a more attractive route for users to guide them through their favourite areas. Li et al [12] proposed a new genetic algorithm based on a path network. Each chromosome represents a feasible path which avoids the search cycle.…”

Section: Related Workmentioning

confidence: 99%

Modelling and Optimization of Personalized Scenic Tourism Routes Based on Urgency

Wang

Zhang

et al. 2023

Applied Sciences

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Traditional route planning methods usually plan the “fastest” or “lowest cost” travel route for users with the goal of finding the shortest path or the lowest cost, but this method cannot meet the needs of tourism users for personalized and multifunctional travel routes. Given this phenomenon, this paper proposes a personalized route planning model based on urgency. First, the model uses the visitor’s historical tourism data and public road network data to extract their preferences, POI (point of interest) relationships, edge scenic values and other information. Then, the planned route function is determined according to the urgency value, which provides users with travel routes that accommodate their interest preferences and urgency. Finally, the improved genetic algorithm based on gene replacement and gene splicing operators is used to carry out numerical experiments on the Xi’an and Wuhan road network datasets. The experimental results show that the proposed algorithm is not only capable of planning routes with different functions for diverse users but also performs personalized route planning according to their preferences.

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“…Rapidly-exploring random trees, probabilistic roadmaps [9], genetic algorithms [10][11][12], neural networks, simulated annealing [13], particle swarm optimization [14,15], ant colony optimization [16], and bacterial foraging optimization [17,18] to name a few, belong to the wide class of heuristic and meta-heuristic algorithms [19]. These methods have their own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm

et al. 2020

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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.

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Efficient Path Planning Method Based on Genetic Algorithm Combining Path Network

Cited by 15 publications

References 7 publications

Fast 3D Path Pre-Planning Method for UAVs

Fast 3D Path Pre-Planning Method for UAVs

Modelling and Optimization of Personalized Scenic Tourism Routes Based on Urgency

Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm

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