A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach

Mohanty, Prases K.; Parhi, Dayal R.

doi:10.1007/s12293-015-0160-3

Cited by 57 publications

(29 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…34 Huang et al 35 have analysed exoskeleton upper body control of a robot using ANFIS methodology. Papers [36][37][38][39][40][41][42][43][44][45] discussed about cuckoo search method and other soft computing methods for path planning of robots in complex environments. Aziz Z& Hassanien 46 have discussed about feature selection using cuckoo search algorithm.…”

Section: Analysis Of Various Ai Techniques Used For Navigationmentioning

confidence: 99%

Advancement in navigational path planning of robots using various artificial and computing techniques

Parhi¹

2018

IRATJ

View full text Add to dashboard Cite

In this paper, a review analysis has been carried out on navigational methodologies of robots with the help of various artificial intelligence techniques such as Fuzzy Logic, Neural Network, Genetic Algorithm, Particle Swarm Optimisation (PSO) and other Artificial Intelligence techniques. During the survey analysis a systematic review has been done and shown how various artificial intelligence techniques can be successfully used for control and navigation of various types of robots subjected to various environmental conditions.

show abstract

Section: Analysis Of Various Ai Techniques Used For Navigationmentioning

confidence: 99%

Advancement in navigational path planning of robots using various artificial and computing techniques

Parhi¹

2018

IRATJ

View full text Add to dashboard Cite

show abstract

“…Likewise, various hybrid optimization algorithms have been implemented in both static and dynamic environments for single as well as multiple robots path planning in complex cluttered environments. [18][19][20] Recently, Kumar et al 21,22 have demonstrated the path planning of humanoid robots in static as well as dynamic environments by implementing both statistical and heuristic methods.…”

Section: Introductionmentioning

confidence: 99%

Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Chhotray¹,

Parhi²

2019

Robotica

Self Cite

View full text Add to dashboard Cite

SummaryThe present paper discusses on development and implementation of back-propagation neural network integrated modified DAYANI method for path control of a two-wheeled self-balancing robot in an obstacle cluttered environment. A five-layered back-propagation neural network has been instigated to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modified DAYANI method. The intensity of weight factors is found out using the neural technique with input parameters such as number of visible intersecting obstacles along the goal direction, minimum visible front obstacle distances as obtained from the sensors, minimum left side obstacle distance within the visible left side range of the robot, average of left side obstacle distances, minimum right side obstacle distance within the visible right side range of the robot, average of right side obstacle distances and goal distance from the robot’s probable next position. Comparison between simulation and experimental exercises is carried out for verifying the robustness of the proposed controller. Also, the authenticity of the proposed controller is verified through a comparative analysis between the results obtained by other existing techniques with the current technique in an exactly similar test scenario and an enhancement of the results is witnessed.

show abstract

“…APF approach has also been combined with other intelligent algorithms (Ahmed, Abdalla, & Abed, ; Bacek, Kasac, Majetic, & Brezak, ; Beloglazov, Finaev, Titov, Shapovalov, & Soloviev, ; Elkilany, Abouelsoud, & Fathelbab, ; Nazzal, ; Stoian, Ivanescu, Stoian, & Pana, ) as a hybridization scheme and to refine the parameters of some other algorithms. Navigational analysis of mobile robotic forms using various soft computing methods (Mohanty & Parhi, ; Mohanty & Parhi, ; Pandey & Parhi, ; Parhi, Deepak, Mohana, Ruppa, & Nayak, ; Parhi & Kundu, ) have been attempted by several researchers. Rath et al (Rath, Das, Parhi, & Kumar, ; Rath, Parhi, Das, & Kumar, ; Rath, Parhi, Das, Muni, & Kumar, ) have discussed possible uses of nature inspired algorithms in humanoid navigation.…”

Section: Introductionmentioning

confidence: 99%

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

2018

Self Cite

View full text Add to dashboard Cite

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.

show abstract

A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach

Cited by 57 publications

References 31 publications

Advancement in navigational path planning of robots using various artificial and computing techniques

Advancement in navigational path planning of robots using various artificial and computing techniques

Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

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

Contact Info

Product

Resources

About