Robot Path Planning Based on Random Expansion of Ant Colony Optimization

Bai, Jinke; Chen, Lijia; Jin, He; Chen, Ruixia; Mao, Haitao

doi:10.1007/978-3-642-25789-6_21

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…However, the path calculation requires knowledge about the state of other agents in the environment, thus requiring global knowledge. However, approaches like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) do not use mapping information of the environment-they rely on the knowledge of the state of other agents [33][34][35][36]. The DARPA Urban Challenge [37] is an example of how all the above can be integrated into an agent in a very complex scenario.…”

Section: Previous Workmentioning

confidence: 99%

Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics

Silva-Contreras,

Godoy-Calderon

2024

Applied Sciences

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When autonomous agents are deployed in an unknown environment, obstacle-avoiding movement and navigation are required basic skills, all the more so when agents are limited by partial-observability constraints. This paper addresses the problem of autonomous agent navigation under partial-observability constraints by using a novel approach: Artificial Potential Fields (APF) assisted by heuristics. The well-known problem of local minima is addressed by providing the agents with the ability to make individual choices that can be exploited in a swarm. We propose a new potential function, which provides precise control of the potential field’s reach and intensity, and the use of auxiliary heuristics provides temporary target points while the agent explores, in search of the position of the real intended target. Artificial Potential Fields, together with auxiliary search heuristics, are integrated into a novel navigation model for autonomous agents who have limited or no knowledge of their environment. Experimental results are shown in 2D scenarios that pose challenging situations with multiple obstacles, local minima conditions and partial-observability constraints, clearly showing that an agent driven using the proposed model is capable of completing the navigation task, even under the partial-observability constraints.

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Section: Previous Workmentioning

confidence: 99%

Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics

Silva-Contreras,

Godoy-Calderon

2024

Applied Sciences

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Section: Related Workmentioning

confidence: 99%

“…26 To narrow the searching range of algorithm and raise the searching speed, Bai and Chen proposed a random expansion ACO algorithm through giving a possible way in the initial pheromone distribution. 27 However, ACO-based hybrid algorithms have also been studied. Yang and Zhuang introduced the genetic operator into ACO and modified the global updating rules to form an improved ACO (IACO) algorithm for solving mobile agent routing problem.…”

Section: Related Workmentioning

confidence: 99%

Cutting trajectory planning of sections with complex composition for roadheader

Wang

2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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In order to realize the autonomous cutting for tunneling robot, the method of cutting trajectory planning of sections with complex composition was proposed. Firstly, based on the multi-sensor parameters, the existence, the location, and size of the dirt band were determined. The roadway section environment was modeled by grid method. Secondly, according to the cutting process and tunneling cutting characteristics, the cutting trajectory ant colony algorithm was proposed. To ensure the operation safety and avoid the cutting head collision, the expanding operation was adopt for dirt band, and the aborting strategy for the ants trapped in the local optimum was put forward to strengthen the pheromone concentration of the found path. The simulation results showed that the proposed method can be used to plan the optimal cutting trajectory. The ant colony algorithm was used to search for the shortest path to avoid collision with the dirt band, and the S-path cutting was used for the left area to fulfill section forming by following complete cover principle. All the ants have found the optimal path within 50 times iteration of the algorithm, and the simulation results were better than particle swarm optimization and basic ant colony optimization.

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“…A typical example of this is when the performance measure values are received from computer simulation (see, e.g., Dębski, 2014a). In such an instance, most classic optimization methods cannot be used (at least not directly) and the optimization process is often based on soft-computing/AI methods (Vasile and Locatelli, 2009;Ceriotti and Vasile, 2010;Pošík et al, 2012;Szłapczyński and Szłapczyńska, 2012;Zamuda and Sosa, 2014;Sun and Wu, 2011;Ćurković et al, 2009;Li and Lü, 2014;Bai et al, 2012;Kojic et al, 2013;Zhou et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

An adaptive multi-spline refinement algorithm in simulation based sailboat trajectory optimization using onboard multi-core computer systems

Dźbski

2016

International Journal of Applied Mathematics and Computer Science

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A new dynamic programming based parallel algorithm adapted to on-board heterogeneous computers for simulation based trajectory optimization is studied in the context of "high-performance sailing". The algorithm uses a new discrete space of continuously differentiable functions called the multi-splines as its search space representation. A basic version of the algorithm is presented in detail (pseudo-code, time and space complexity, search space auto-adaptation properties). Possible extensions of the basic algorithm are also described. The presented experimental results show that contemporary heterogeneous on-board computers can be effectively used for solving simulation based trajectory optimization problems. These computers can be considered micro high performance computing (HPC) platforms-they offer high performance while remaining energy and cost efficient. The simulation based approach can potentially give highly accurate results since the mathematical model that the simulator is built upon may be as complex as required. The approach described is applicable to many trajectory optimization problems due to its black-box represented performance measure and use of OpenCL.

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Robot Path Planning Based on Random Expansion of Ant Colony Optimization

Cited by 6 publications

References 3 publications

Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics

Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics

Cutting trajectory planning of sections with complex composition for roadheader

An adaptive multi-spline refinement algorithm in simulation based sailboat trajectory optimization using onboard multi-core computer systems

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