Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm

Yang, Xue; Sun, Jian‐Qiao

doi:10.3390/app8091425

Cited by 48 publications

(30 citation statements)

References 43 publications

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“…The performance of the proposed controller is better in terms of path optimality when compared with other intelligent navigational approaches. FF is used for multimodal function optimization . A benchmarking of FF, particle swam optimization, and GA shows that FF is superior in terms of efficiency and success rate and hence more powerful for solving NP‐complete problems.…”

Section: Aco and Ffmentioning

confidence: 99%

“…FF is used for multimodal function optimization. 5,7,8,35 A benchmarking of FF, particle swam optimization, and GA shows that FF is superior in terms of efficiency and success rate and hence more powerful for solving NP-complete problems.…”

Section: Aco and Ffmentioning

confidence: 99%

“…Reactive approaches are used for real-time navigation problems and classical approaches can be improved by hybridizing with the reactive approaches. [39][40][41]…”

Section: Aco and Ffmentioning

confidence: 99%

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Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Gangadharan

Salgaonkar

2020

Engineering Reports

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Metaheuristic algorithms such as ant colony optimization (ACO) and firefly (FF) have been successfully employed to solve the optimization problems such as robot motion planning in dynamic environments. The systematic plantation of rubber trees on a rectangular grid motivated us to explore application of grid search algorithms. We compared the ACO and FF algorithms in various scenarios by changing simulation parameters like density of the environment, land size, number of robots simultaneously available, and hillock plantations. In all different scenarios, we evaluated the performance of ACO and FF in terms of path length and time of execution, we found that later is outperforming the former. Regression equations are framed to establish the contributions of different parameters. Statistical significance of the results has been in favor of this hypothesis. The shortest path on a plain land is the relatively simplest scenario, while the Hamiltonian on a concave surface is arguably the most difficult. The novelty of this work lies in the very idea of an autonomous robot for the rubber tapping and then path optimization by employing soft computing techniques. This proposal of rubber harvesting robot if implemented for latex collection, has a potential to drive the rubber farming and allied businesses to scale up the economy of the coastal areas of India, say for example, Kerala.

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Section: Aco and Ffmentioning

confidence: 99%

Section: Aco and Ffmentioning

confidence: 99%

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Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Gangadharan

Salgaonkar

2020

Engineering Reports

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“…Also applied to mobile robotics, Ref. [44] proposed a multi-objective evolutionary algorithm, tested using five scenarios and quality metrics.…”

Section: Multi-objective Path Planningmentioning

confidence: 99%

An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders

Lucas

Hernández-Sosa

Greiner

et al. 2019

Sensors

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Underwater gliders are energy-efficient vehicles that rely on changes in buoyancy in order to convert up and down movement into forward displacement. These vehicles are conceived as multi-sensor platforms, and can be used to collect ocean data for long periods in wide range areas. This endurance is achieved at the cost of low speed, which requires extensive planning to ensure vehicle safety and mission success, particularly when dealing with strong ocean currents. As gliders are often involved on missions that pursue multiple objectives (track events, reach a target point, avoid obstacles, sample specified areas, save energy), path planning requires a way to deal with several constraints at the same time; this makes glider path planning a multi-objective (MO) optimization problem. In this work, we analyse the usage of the non-dominated sorting genetic algorithm II (NSGA-II) to tackle a MO glider path planning application on a complex environment integrating 3D and time varying ocean currents. Multiple experiments using a glider kinematic simulator coupled with NSGA-II, combining different control parameters were carried out, to find the best parameter configuration that provided suitable paths for the desired mission. Ultimately, the system described in this work was able to optimize multi-objective trajectories, providing non dominated solutions. Such a planning tool could be of great interest in real mission planning, to assist glider pilots in selecting the most convenient paths for the vehicle, taking into account ocean forecasts and particular characteristics of the deployment location.

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“…In order to reduce the computational complexity, many approaches have been applied to the path planning problem, which can be classified into traditional methods and bionic inspired algorithms. The traditional path planning methods contain the A* search algorithm [6], the polynomial optimization method [7], and artificial potential field based methods [8], while the bionic inspired algorithms contain the simulated annealing algorithm (SAA) [9], genetic algorithm (GA) [5], differential evolution (DE) [10], artificial bee colony (ABC) [11], particle swarm optimization (PSO) algorithm [12,13], and ant colony optimization (ACO) [14]. Among these bionic inspired algorithms, ACO is usually applied to the combinational optimization problems [14,15], while the others are usually used to solve the continuous optimization problems.…”

mentioning

confidence: 99%

Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

2020

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The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy.

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Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm

Cited by 48 publications

References 43 publications

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders

Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

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