Adaptive Charged System Search Approach to Path Planning for Multiple Mobile Robots

Precup, Radu‐Emil; Petriu, Emil M.; Radae, Mircea-Bogdan; Voisan, Emil-Ioan; Drăgan, Florin

doi:10.1016/j.ifacol.2015.08.147

Cited by 11 publications

(14 citation statements)

References 25 publications

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“…This algorithm consists of global path planning and local path planning via a hybrid artificial fish swarm algorithm (HAFSA) and an expansion logic strategy. The application of adaptive Charged System Search (CSS) algorithms has been used (Precup et al, 2015) to find an optimal path for multiple mobile robots. They examined these algorithms on holonomic wheeled platforms in static environments.…”

Section: Related Workmentioning

confidence: 99%

The boundary node method for multi‐robot multi‐goal path planning problems

2021

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In this paper, we investigate the multi-goal path planning problem to find the shortest collision-free path connecting a given set of goal points located in the robot working environment. This problem combines two sub-problems: first, optimize the sequence of the goal points located in the free workspace; second, compute the shortest collision-free path between goal points. In this study, a genetic algorithm is used to optimize the sequence of the goal points that the robot should visit. Once the sequence of the goal points is available, our developed method called Boundary Node Method (BNM) is applied to generate an initial collision-free path between every pair of the sequenced goal points. Subsequently, an additional developed method called Path Enhancement Method (PEM) is used to find an optimal or nearoptimal path by reducing the overall initial path length. In the BNM, the robot and its immediate surroundings are simulated by a nine-node quadrilateral element, where the centroid node represents the robot's position. The robot moves between goals with boundary nodes in the working environment depending on the boundary node's potential value. The potential value of each point in the working environment is calculated based on the proposed potential function. Additionally, this article investigates the multi-goal path planning problem for multi-robot systems, when each goal reached by several robots. Finally, simulations and experiments are performed on a real mobile robot to demonstrate the effectiveness of the developed methods.

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Section: Related Workmentioning

confidence: 99%

The boundary node method for multi‐robot multi‐goal path planning problems

2021

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“…As shown in Figure 4, the initial poses of the three mobile robots were (1, 1, 0), (1,5,0) and (1, 9, 0). The goal positions were (10,14), (17,1) and (18,13). The global path planning trajectories of the three mobile robots are shown in Figure 5.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…In previous studies, only static obstacles in the unknown environment were considered. For holonomic wheeled mobile robots in static environments, an optimal multiple mobile robot path planning method based on adaptive charged system search (CSS) algorithms was addressed [13]. However, path planning methods in an unknown environment with dynamic obstacles are even more acute in multiple mobile robot areas.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

Huang

Jiang

et al. 2019

Applied Sciences

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The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.

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“…Step 4. After a round of ant search, the pheromone was calculated by formulas (10) and (12), the evaporation coefficient was calculated by (13), and the pheromone was updated according to formulas (9).…”

Section: Algorithmmentioning

confidence: 99%

“…The idea of Mo and Xu [8] solved the path planning by PSO with the position updating strategy and biogeography particle swarm optimization algorithm to increase the diversity of population and optimize the paths in a static environment. Precup et al [9] proposed that the adaptive charged system search algorithm was applied to the optimal path planning problem of multiple mobile robots in static environment. Several modifications and improvements of A star algorithm were introduced by Ducho et al [10] and Guruji et al [11] considering a static or dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

Zhao

Cheng

Chen

2016

Mathematical Problems in Engineering

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This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. Ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results.

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Adaptive Charged System Search Approach to Path Planning for Multiple Mobile Robots

Cited by 11 publications

References 25 publications

The boundary node method for multi‐robot multi‐goal path planning problems

The boundary node method for multi‐robot multi‐goal path planning problems

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

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