Autonomous robot path planning in dynamic environment using a new optimization technique inspired by Bacterial Foraging technique

Hossain, Arafat; Ferdous, Israt

doi:10.1109/eict.2014.6777884

Cited by 23 publications

(27 citation statements)

References 8 publications

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“…A BFO algorithm was applied to determine the shortest feasible path from a current position to the target position in a 2D space with moving obstacles [265]. The algorithm is able to avoid obstacles and find a path towards the target position.…”

Section: Continuous Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

452

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Continuous Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

452

213

View full text Add to dashboard Cite

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“…Various works have also adopted heuristic methods and employed these to solve different aspects of path planning methods such as Bat algorithm (BA) [11], Particle Swarm Optimization [12], Cuckoo search (CS) algorithms [13], Bacterial Foraging optimization [14], Artificial Immune Systems [15], and the Whale Optimization Algorithm (WOA), implemented in a static environment to satisfy requirements for the shortest and smoothest path [16]. GA and its modified versions are frequently implemented to find the shortest path for mobile robot path planning in different environments [17], while path planning using neural networks was developed in [18].…”

Section: Related Workmentioning

confidence: 99%

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Ajeil

Ibraheem

Sahib

et al. 2020

Applied Soft Computing

138

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The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the PSO-MFB algorithm to be converted into feasible solutions. The third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed PSO-MFB algorithm is highly competitive in terms of path optimality.

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“…If environment parameters are unknown or highly uncertain then local planning is performed, also called online (sensor based, or reactive). Whereas, a known environment requires global planning, also called offline (map based) [23,54]. Further, RRT* variants based on bidirectional trees also exist in literature, which generate two trees simultaneously from start and goal states.…”

Section: Methodologies Based On Rrt* Algorithmmentioning

confidence: 99%

“…Evolutionary algorithms such as Particle Swarm Optimization (PSO) [17][18][19], Ant Colony Optimization (ACO) [20] and Genetic Algorithm (GA) [21] are suitable for multi-objective problems. Many other evolutionary algorithms such as Artificial Bee Colony (ABC) [22], Bacterial Foraging Optimization (BFO) [23], Bio Inspired Neural Networks [24,25], and Fire Fly algorithm [26] are often trapped in local optimum, and bear high computational cost. Moreover, they are highly sensitive to search space size and data representation scheme of problem [27,28].…”

Section: Introductionmentioning

confidence: 99%

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Noreen¹,

Khan²,

Habib³

2016

ijacsa

170

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Abstract-Optimal path planning refers to find the collision free, shortest, and smooth route between start and goal positions. This task is essential in many robotic applications such as autonomous car, surveillance operations, agricultural robots, planetary and space exploration missions. Rapidly-exploring Random Tree Star (RRT*) is a renowned sampling based planning approach. It has gained immense popularity due to its support for high dimensional complex problems. A significant body of research has addressed the problem of optimal path planning for mobile robots using RRT* based approaches. However, no updated survey on RRT* based approaches is available. Considering the rapid pace of development in this field, this paper presents a comprehensive review of RRT* based path planning approaches. Current issues relevant to noticeable advancements in the field are investigated and whole discussion is concluded with challenges and future research directions.

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Autonomous robot path planning in dynamic environment using a new optimization technique inspired by Bacterial Foraging technique

Cited by 23 publications

References 8 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

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