An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance

Wang, Hongjun; Fu, Yong; Zhao, Zhuoqun; Yue, Youjun

doi:10.1155/2019/6097591

Cited by 20 publications

(10 citation statements)

References 12 publications

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“…Siddique et al [137] studied meta-heuristic and nature-inspired algorithms that imitate natural phenomena of natural sciences. Numerous researchers have addressed the ground and aerial vehicle trajectory planning and obstacle avoidance problem using the optimization algorithm that mimics the behavior of living things, such as fish, ants, bees, whales, wolves, and bats [138][139][140][141][142][143]. They are known as non-conventional methods.…”

Section: Bio-inspired Methodsmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

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In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

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Section: Bio-inspired Methodsmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

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“…Another advantage of SI is to efficiently solve nonlinear real world problems [16]. For path planning, algorithms as Ant Colony Optimization (ACO) [17,18], Bat Algorithm (BA) [19,20], Firefly Algorithm (FA) [21,22], and PSO [23,24], among others, can be used. In Table 1 is shown a comparison between some SI algorithms [25,26].…”

Section: Related Workmentioning

confidence: 99%

An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

2021

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Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques.

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“…Furthermore, Wang et al. (2019) and Bakhtiari et al. (2012) used the Ant Colony Optimisation (ACO) algorithm for obstacle avoidance and optimal path production for agricultural land coverage.…”

Section: Introductionmentioning

confidence: 99%

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

et al. 2022

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Multi-rotor Unmanned Aerial Vehicles (UAVs), although originally designed and developed for defence and military purposes, in the last ten years have gained momentum, especially for civilian applications, such as search and rescue, surveying and mapping, and agricultural crops and monitoring. Thanks to their hovering and Vertical Take-Off and Landing (VTOL) capabilities and the capacity to carry out tasks with complete autonomy, they are now a standard platform for both research and industrial uses. However, while the flight control architecture is well established in the literature, there are still many challenges in designing autonomous guidance and navigation systems to make the UAV able to work in constrained and cluttered environments or also indoors. Therefore, the main motivation of this work is to provide a comprehensive and exhaustive literature review on the numerous methods and approaches to address path-planning problems for multi-rotor UAVs. In particular, the inclusion of a review of the related research in the context of Precision Agriculture (PA) provides a unified and accessible presentation for researchers who are initiating their endeavours in this subject.

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An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance

Cited by 20 publications

References 12 publications

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

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