An Intelligent Path Planning Approach for Humanoid Robots Using Adaptive Particle Swarm Optimization

Sahu, Chinmaya; Kumar, Priyadarshi Biplab; Parhi, Dayal R.

doi:10.1142/s021821301850015x

Cited by 16 publications

(7 citation statements)

References 29 publications

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“…FA is known to work well in solving engineering optimization problems with avoidance of local minima situations. 2,3 To compare the suitability of FA in generating the required turning angle (TA) based on the above mentioned objective function, it has been compared against Dijkstra's algorithm (DA), 48 A* algorithm (AA), 49 particle swarm optimization (PSO) 50 and ant colony optimization (ACO). 51 Table 1 represents a comparison of FA against other metaheuristic approaches in generation of the required TA.…”

Section: Global Fitness Functionmentioning

confidence: 99%

Humanoid navigation: A firefly based approach

Kumar

Parhi

Sethy

2021

Computer Animation & Virtual

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Humanoid navigation has been the center of attraction among robotic researchers since many years by virtue of their increasing use in industrial automation and smart manufacturing. In the current research, a firefly based computer vision integrated navigational analysis has been performed on humanoid robots for smooth movement by negotiation with obstacles present in complicated terrains. Here, the logic of the firefly algorithm has been used to design the controller by careful consideration of the navigational parameters. A computer vision based method is integrated along with the developed controller to resolve some conflicting situations that may arise by encountering large sized obstacle or detection of an obstacle exactly in front of the robot where the robot becomes confused regarding the direction of turn. The developed navigational model has been tested in simulation environments by considering NAO robot as the humanoid platform. The simulation results are also verified through an experimental platform developed under research laboratory conditions. The data obtained from both the platforms are compared in terms of navigational parameters and are found to be in close proximity to each other.Finally, the developed navigational model is also authenticated against other existing models for validating better efficiency.

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Section: Global Fitness Functionmentioning

confidence: 99%

Humanoid navigation: A firefly based approach

Kumar

Parhi

Sethy

2021

Computer Animation & Virtual

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“…Other smart algorithms developed for intelligent navigation of robotic agents are mostly applied to mobile robots. Along with that, humanoid navigation has been reported in mostly specific environmental constraints (Mahyuddin, Khan, & Herrmann, ; Okada, Inaba, & Inoue, ; Sahu, Parhi, & Kumar, ; Sahu, Kumar, Parhi ). Although some of the researchers (Kumar, Mohapatra, & Parhi ) have tried to solve the humanoid motion planning problem, they have been mostly applied on specific arena conditions.…”

Section: Introductionmentioning

confidence: 97%

Path planning of humanoids based on artificial potential field method in unknown environments

2018

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In this paper, an artificial potential field based navigational controller has been developed for motion planning of humanoid robots. Here, NAO robots are used as the humanoid platform using the underlying principles of potential field based method. The movement of the robot is considered to be under a negative gradient scheme by the combined effect of attractive and repulsive forces generated due to target and obstacles, respectively. The working of the controller is tested in a V‐REP simulation platform, and the simulation results are validated through a real‐time experimental set‐up developed under laboratory conditions. Here, the navigation of both single and multiple humanoids has been attempted. For avoiding intercollision among multiple humanoids during their navigation in a common platform, a Petri‐Net control scheme has been proposed. The results obtained from both the simulation and experimental platforms are compared against each other with a good agreement between them having minimal percentage of deviations. Finally, the proposed controller is also evaluated against another existing navigational model, and a significant performance improvement has been observed.

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“…To date, some variants of PSO have been presented to improve the original version PSO. Most of the current existing PSOs can be roughly divided into three categories: parameter selection, hybrid versions, and topology structure, respectively [27].…”

Section: Introductionmentioning

confidence: 99%

Cubic Spline Interpolation-Based Robot Path Planning Using a Chaotic Adaptive Particle Swarm Optimization Algorithm

Lian

Xiao

et al. 2020

Mathematical Problems in Engineering

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This paper proposed a cubic spline interpolation-based path planning method to maintain the smoothness of moving the robot’s path. Several path nodes were selected as control points for cubic spline interpolation. A full path was formed by interpolating on the path of the starting point, control points, and target point. In this paper, a novel chaotic adaptive particle swarm optimization (CAPSO) algorithm has been proposed to optimize the control points in cubic spline interpolation. In order to improve the global search ability of the algorithm, the position updating equation of the particle swarm optimization (PSO) is modified by the beetle foraging strategy. Then, the trigonometric function is adopted for the adaptive adjustment of the control parameters for CAPSO to weigh global and local search capabilities. At the beginning of the algorithm, particles can explore better regions in the global scope with a larger speed step to improve the searchability of the algorithm. At the later stage of the search, particles do fine search around the extremum points to accelerate the convergence speed of the algorithm. The chaotic map is also used to replace the random parameter of the PSO to improve the diversity of particle swarm and maintain the original random characteristics. Since all chaotic maps are different, the performance of six benchmark functions was tested to choose the most suitable one. The CAPSO algorithm was tested for different number of control points and various obstacles. The simulation results verified the effectiveness of the proposed algorithm compared with other algorithms. And experiments proved the feasibility of the proposed model in different dynamic environments.

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An Intelligent Path Planning Approach for Humanoid Robots Using Adaptive Particle Swarm Optimization

Cited by 16 publications

References 29 publications

Humanoid navigation: A firefly based approach

Humanoid navigation: A firefly based approach

Path planning of humanoids based on artificial potential field method in unknown environments

Cubic Spline Interpolation-Based Robot Path Planning Using a Chaotic Adaptive Particle Swarm Optimization Algorithm

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