Matrix-Binary Codes based Genetic Algorithm for path planning of mobile robot

Patle, B.K.; Parhi, Dayal R.; Jagadeesh, A.; Kashyap, Sunil Kumar

doi:10.1016/j.compeleceng.2017.12.011

Cited by 145 publications

(74 citation statements)

References 11 publications

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“…Equations (1) and (2) show that the Bezier curve is determined by the control points. erefore, the problem is equal to finding the control points of the Bezier curve with constraint (9).…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In recent years, the genetic algorithm (GA) has been widely applied in mobile robot path planning problems because of its great global optimization ability and implicit parallel computing characteristics [7,8]. e GA searches for the optimal solution by simulating the natural evolution based on the theoretical models of the genetic inheritance and variation in Darwin's biological evolution [9,10]. Recently, some meaningful results have been reported for the GA. For example, a generalized segmentation crossover operator was introduced into the GA to improve the local optimization ability and execution efficiency of the algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robot Path Planning Based on Genetic Algorithm Fused with Continuous Bezier Optimization

Liu

Zang

et al. 2020

Computational Intelligence and Neuroscience

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In this study, a new method of smooth path planning is proposed based on Bezier curves and is applied to solve the problem of redundant nodes and peak inflection points in the path planning process of traditional algorithms. First, genetic operations are used to obtain the control points of the Bezier curve. Second, a shorter path is selected by an optimization criterion that the length of the Bezier curve is determined by the control points. Finally, a safe distance and adaptive penalty factor are introduced into the fitness function to ensure the safety of the walking process of the robot. Numerous experiments are implemented in two different environments and compared with the existing methods. It is proved that the proposed method is more effective to generate a shorter, smoother, and safer path compared with traditional approaches.

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“…Equations (1) and (2) show that the Bezier curve is determined by the control points. erefore, the problem is equal to finding the control points of the Bezier curve with constraint (9).…”

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robot Path Planning Based on Genetic Algorithm Fused with Continuous Bezier Optimization

Liu

Zang

et al. 2020

Computational Intelligence and Neuroscience

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show abstract

“…[49] used an improved genetic algorithm to do multi-objective path planning, applied to a free-form surface milling, with three fitness functions (efficiency, energy, and carbon footprint) being converted to one single fitness function and replicating the travelling salesman problem. Another approach to multi-objective path planning using a genetic algorithm was done by [50], by using both dynamic and static 2D environments and concluding that their matrix-binary codes approach is more efficient and robust than random search algorithms.…”

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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“…For the identification of multiple objects, an optimal hierarchical global path planning approach has been applied in a "cluttered environment" [38] using particle swarm optimisation. Patle et al [39] propose an approach based on matrix-binary codes with a genetic algorithm to implement CPP including manipulator control and theoretical ideas to solve the CPP problem. Munoz et al have proposed a unified framework for path and task planning for autonomous robots [40].…”

Section: Related Researchmentioning

confidence: 99%

Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

Hai

Moore

2018

Machines

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Human behaviour demonstrates environmental awareness and self-awareness which is used to arrive at decisions and actions or reach conclusions based on reasoning and inference. Environmental awareness and self-awareness are traits which autonomous robotic systems must have to effectively plan an optimal route and operate in dynamic operating environments. This paper proposes a novel approach to enable autonomous robotic systems to achieve efficient coverage path planning, which combines adaptation with knowledge reasoning techniques and hedge algebras to achieve optimal coverage path planning in multiple decision-making under dynamic operating environments. To evaluate the proposed approach we have implemented it in a mobile cleaning robot. The results demonstrate the ability to avoid static and dynamic (moving) obstacles while achieving efficient coverage path planning with low repetition rates. While alternative current coverage path planning algorithms have achieved acceptable results, our reported results have demonstrated a significant performance improvement over the alternative coverage path planning algorithms.

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Matrix-Binary Codes based Genetic Algorithm for path planning of mobile robot

Cited by 145 publications

References 11 publications

Robot Path Planning Based on Genetic Algorithm Fused with Continuous Bezier Optimization

Robot Path Planning Based on Genetic Algorithm Fused with Continuous Bezier Optimization

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

Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

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