Swarm Intelligence: Ant-Based Robot Path Planning

Zhou, Jing; Dai, Guanzhong; He, Da; Ma, Jun; Cai, Xiaoyan

doi:10.1109/ias.2009.120

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…3,17 These techniques handle complex problems in high-dimensional spaces but usually operate in a binary world aiming to find out collision-free solutions rather than the optimal path. The ant colony algorithm [18][19][20][21][22][23] simulates the behavior of ants in nature. As mentioned in ref.…”

Section: Related Workmentioning

confidence: 99%

RimJump: Edge-based Shortest Path Planning for a 2D Map

Yao¹,

Zhang²,

Shi³

et al. 2018

Robotica

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SummaryPath planning under 2D map is a key issue in robot applications. However, most related algorithms rely on point-by-point traversal. This causes them usually cannot find the strict shortest path, and their time cost increases dramatically as the map scale increases. So we proposed RimJump to solve the above problem, and it is a new path planning method that generates the strict shortest path for a 2D map. RimJump selects points on the edge of barriers to form the strict shortest path. Simulation and experimentation prove that RimJump meets the expected requirements.

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Section: Related Workmentioning

confidence: 99%

RimJump: Edge-based Shortest Path Planning for a 2D Map

Yao¹,

Zhang²,

Shi³

et al. 2018

Robotica

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“…Set of particle position = {d1, d2, d3,……..d r } Set of particle velocity ={v1,v2,v3……......v r } Each particle is having its position best value ( X pbest )based on the communicated information among the swarm, the particles will approach to one global best position .The particle having the greatest fitness is treated as the global best position (X gbest ) [11].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Path Planning Problem

Vasishth¹,

Gigras²

2014

IJCA

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Path planning is the way of determination of a collision free path between start and goal position through obstacles cluttered in a workspace. Though it is a complex problem, but it is an essential task for the navigation and controlling the motion of autonomous robot manipulators. This NP-complete problem (those problems is difficult to solve specially in a dynamic environment where the optimal path needs to be rerouted in real time when a new obstacle appears. This paper provides two categories of path planning approaches:-Deterministic and Probabilistic approaches. Deterministic methods allow achieving the same result in each execution with the same initial conditions. They are perfectly predictable, hence suitable for static environment, but not effective when they are used in a real time environment as there could be sudden changes in environment. The most used solution to overcome the problem of real time environment are the probabilistic methods such as Particle swarm optimization[pso], Ant colony optimization[aco], genetic algorithm[ga], multi agent path planning,etc.

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“…However, one of the most important characteristics of multiple UAVs is to realize the group behavior of the UAV group with the help of the local interaction between UAVs, which has distributed characteristics, while classical search algorithms do not have distributed characteristics. To solve this problem—inspired by the fact that in nature, and in order to make up for the limited ability of individuals, many biological populations can present some kind of group behavior through the communication and cooperation between individuals or local regions—scholars at home and abroad have proposed a series of swarm intelligence methods, such as the Particle Swarm Optimization, PSO algorithm [ 10 ], Ant Colony Optimization, ACO algorithm [ 11 , 12 , 13 ], Artificial fish swarm, AFO algorithm [ 14 , 15 , 16 ], pigeon-inspired optimization, PIO algorithm [ 17 ], Firefly algorithm, FA algorithm [ 18 ], Genetic Algorithm and GA algorithm, and so on [ 19 ]. Coyotes have become the masters of the prairies, through their own strong cognitive ability and tight organization structure within the team.…”

Section: Introductionmentioning

confidence: 99%

Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

Zhang

Liu

2022

Biomimetics

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The Gray Wolf (GWO) algorithm aims to address the path planning problem of multiple UAVs, and the scene setting is mainly to avoid threats, meet the constraints of UAVs themselves and avoid obstacles between UAVs. The scene setting is relatively simple. To address such problems, the problem of time windows is considered in this paper, so that the UAV can arrive at the same time, and the Gray Wolf algorithm is used to optimize the problem. Finally, the experimental results verify that the proposed method can plan a safe flight path in the process of multi-UAV flight and reach the goal point at the same time. The mean error of flight time between UAVs of the GWO is 0.213, which is superior to PSO (0.382), AFO (0.315) and GA (0.825).

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Swarm Intelligence: Ant-Based Robot Path Planning

Cited by 7 publications

References 9 publications

RimJump: Edge-based Shortest Path Planning for a 2D Map

RimJump: Edge-based Shortest Path Planning for a 2D Map

Path Planning Problem

Path Planning with Time Windows for Multiple UAVs Based on Gray Wolf Algorithm

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