An Optimized Path Planning for the Mobile Robot Using Potential Field Method and PSO Algorithm

Mandava, Ravi Kumar; Bondada, Sukesh; Vundavilli, Pandu R.

doi:10.1007/978-981-13-1595-4_11

Cited by 28 publications

(20 citation statements)

References 10 publications

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“…From Fig. 3, it can be observed that both the proposed approaches and the approach available in [30] are found to generate the collision-free paths in both the scenarios shown above. Further, Table 1 gives the path lengths and time taken to generate the path during the said simulation study.…”

Section: Simulation Results In the Static Environmentmentioning

confidence: 90%

“…In the present section FMM and FMMHRS algorithms are compared with an artificial potential field method (APF) combined with particle swarm optimization (PSO) and three point smoothing method available in the literature [30] are shown in Fig. 3.…”

Section: Simulation Results In the Static Environmentmentioning

confidence: 99%

“…To design an optimal path planning algorithm in [29], the authors developed an effective and improved artificial potential field method combined with regression search. Simulataneously, Ravi et al [30] established a hybridized path planning algorithm for static and dynamic environments. In this work, they used a 3-point smoothing method to generate the optimal path.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Motion Planning Algorithm for a Biped Robot Using Fast Marching Method Hybridized with Regression Search

2019

APH

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In the past few years, studies of biped robot locomotion and navigation have increased enormously due to its ease in mobility in the terrains that are designed exclusively for the humans. To navigate the biped robot in static and dynamic environments without hitting obstacles is a challenging task. In the present research, the authors have developed a hybridized motion planning algorithm that is, fast marching method hybridized with regression search (FMMHRS) methodology. In this work, initially the fast marching algorithm has been used to observe the environment and identify the path from start to final goal. Later on, the regression search method is combined with the fast marching method (FMM) algorithm to optimize the path without hitting any obstacles. The main objective of the present research work is to generate the path for both the static and dynamic scenarios in simulation and in a real environment. To conduct the testing of the proposed algorithm, the authors have chosen an 18-DOF two legged robot that was developed in our laboratory.

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Section: Simulation Results In the Static Environmentmentioning

confidence: 90%

Section: Simulation Results In the Static Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Motion Planning Algorithm for a Biped Robot Using Fast Marching Method Hybridized with Regression Search

2019

APH

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“…A combination of the PSO technique with a potential field method (PFm) was proposed to resolve the PP problem. The authors used PSO to optimise obstacle avoidance and PFM to obtain the target position 22 . PSO is used for finding optimal routing and Neural Network algorithms for obstacle avoidance 23 .…”

Section: Introductionmentioning

confidence: 99%

An Approach to Improve Multi objective Path Planning for Mobile Robot Navigation using the Novel Quadrant Selection Method

Baskaran

Panchu

et al. 2021

Def. Sc. J.

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Currently, automated and semi-automated industries need multiple objective path planning algorithms for mobile robot applications. The multi-objective optimisation algorithm takes more computational effort to provide optimal solutions. The proposed grid-based multi-objective global path planning algorithm [Quadrant selection algorithm (QSA)] plans the path by considering the direction of movements from starting position to the target position with minimum computational effort. Primarily, in this algorithm, the direction of movements is classified into quadrants. Based on the selection of the quadrant, the optimal paths are identified. In obstacle avoidance, the generated feasible paths are evaluated by the cumulative path distance travelled, and the cumulative angle turned to attain an optimal path. Finally, to ease the robot’s navigation, the obtained optimal path is further smoothed to avoid sharp turns and reduce the distance. The proposed QSA in total reduces the unnecessary search for paths in other quadrants. The developed algorithm is tested in different environments and compared with the existing algorithms based on the number of cells examined to obtain the optimal path. Unlike other algorithms, the proposed QSA provides an optimal path by dramatically reducing the number of cells examined. The experimental verification of the proposed QSA shows that the solution is practically implementable.

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“…Path planning is an important research direction in the field of mobile robots, and it is one of the main difficulties in research on such robots [1]. e path planning problem aims to find the safest and shortest path autonomously without collisions from the start point to the target point under a given environment with barriers [2,3]. Path planning has been widely used in fields such as logistics distribution, intelligent transportation, and weapons navigation [4][5][6].…”

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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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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An Optimized Path Planning for the Mobile Robot Using Potential Field Method and PSO Algorithm

Cited by 28 publications

References 10 publications

Dynamic Motion Planning Algorithm for a Biped Robot Using Fast Marching Method Hybridized with Regression Search

Dynamic Motion Planning Algorithm for a Biped Robot Using Fast Marching Method Hybridized with Regression Search

An Approach to Improve Multi objective Path Planning for Mobile Robot Navigation using the Novel Quadrant Selection Method

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

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