A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

Li, Fengling; Fan, Xingjiang; Hou, Zhenting

doi:10.1109/access.2020.3023999

Cited by 24 publications

(13 citation statements)

References 28 publications

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“…After defining the optimization problem in Section 2, the TLBO algorithm is applied to obtain the optimum path that minimizes the total objective function presented in Equation (15).…”

Section: Tlbo Algorithmmentioning

confidence: 99%

“…where G j is the penalty function of the constraint g j and a k is a positive constant known as the penalty parameter. Equation (15) indicates that while minimizing the objective function, a positive penalty is added whenever a constraint is violated [29]. There are many common penalty functions.…”

Section: Tlbo Algorithmmentioning

confidence: 99%

“…This algorithm is combined with the continuous high-degree Bezier curve to smooth the path. In [15], an optimal method of path planning based on FFA with self-adaptive population size is proposed. The evaluation of the degree of collision is established at the costs of avoiding the collision.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Path Planning Algorithm Based on Teaching-Learning-Based-Optimization for an Autonomous Vehicle

Desoky¹,

Kamel²,

Said³

et al. 2022

Communications

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This study provides a teaching-learning-based optimization (TLBO) path planning method for an autonomous vehicle in a cluttered environment, which takes into account path smoothness and the possibility of collision with nearby obstacles. The path planning problem is tackled as a multiobjective optimization in order to plan an efficient path that allows the vehicle to travel autonomously in crowded settings. The TLBO algorithm is used to find the ideal path, with the goals of finding the shortest path to the target site and maximizing path smoothness, while avoiding obstacles and taking into account the vehicle's dynamic and algebraic properties.

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“…After defining the optimization problem in Section 2, the TLBO algorithm is applied to obtain the optimum path that minimizes the total objective function presented in Equation (15).…”

Section: Tlbo Algorithmmentioning

confidence: 99%

Section: Tlbo Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Path Planning Algorithm Based on Teaching-Learning-Based-Optimization for an Autonomous Vehicle

Desoky¹,

Kamel²,

Said³

et al. 2022

Communications

View full text Add to dashboard Cite

show abstract

“…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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“…Path planning is roughly classified into two types; global path planning and local path planning. Global path planning generates a path that does not collide with obstacles based on a prior map [9]- [11]. However, global path planning cannot consider obstacles that do not exist in the prior map.…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Kobayashi

Motoi

2022

IEEE Access

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Local path planning considering static and dynamic obstacles for a mobile robot is one of challenging research topics. Conventional local path planning methods generate path candidates by assuming constant velocities for a certain period time. Therefore, path candidates consist of straight line and arc paths. These path candidates are not suitable for dynamic environments and narrow spaces. This paper proposes a novel local path planning method based on dynamic window approach with virtual manipulators (DWV). DWV consists of dynamic window approach (DWA) and virtual manipulator (VM). DWA is the local path planning method that performs obstacle avoidance for static obstacles under robot constraints. DWA also generates straight line and arc path candidates by assuming constant velocities. VM generates velocities of reflective motion by using virtual manipulators and environmental information. DWV generates path candidates by variable velocities modified by VM and predicted positions of static and dynamic obstacles. Therefore, in an environment with dynamic obstacles, the obstacle-avoidable paths which include non-straight line and non-arc paths are generated. The effectiveness of the proposed method was confirmed from simulation and experimental results.

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A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

Cited by 24 publications

References 28 publications

Path Planning Algorithm Based on Teaching-Learning-Based-Optimization for an Autonomous Vehicle

Path Planning Algorithm Based on Teaching-Learning-Based-Optimization for an Autonomous Vehicle

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

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

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