Intelligent Navigation of a Self-Fabricated Biped Robot using a Regression Controller

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

doi:10.24200/sci.2018.50018.1466

Cited by 10 publications

(14 citation statements)

References 33 publications

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“…Here obstacle distances such as front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD) and current turning angle (TA) recorded by the sensory network of the humanoid are fed as inputs to the GA controller. By the sequential process of parent selection, crossover, mutation and optimality check (Kumar et al , 2018a, b), GA provides the next feasible best solution as the initial output which is interpreted as Turning Angle 1 (TA1).…”

Section: Analysis Of Genetic Algorithm-neural Network Hybrid Techniquementioning

confidence: 99%

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Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot

Rath¹,

Parhi

Das

et al. 2020

IJIUS

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PurposeTo navigate humanoid robots in complex arenas, a significant level of intelligence is required which needs proper integration of computational intelligence with the robot's controller. This paper describes the use of a combination of genetic algorithm and neural network for navigational control of a humanoid robot in given cluttered environments.Design/methodology/approachThe experimental work involved in the current study has been done by a NAO humanoid robot in laboratory conditions and simulation work has been done by the help of V-REP software. Here, a genetic algorithm controller is first used to generate an initial turning angle for the robot and then the genetic algorithm controller is hybridized with a neural network controller to generate the final turning angle.FindingsFrom the simulation and experimental results, satisfactory agreements have been observed in terms of navigational parameters with minimal error limits that justify the proper working of the proposed hybrid controller.Originality/valueWith a lack of sufficient literature on humanoid navigation, the proposed hybrid controller is supposed to act as a guiding way towards the design and development of more robust controllers in the near future.

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Section: Analysis Of Genetic Algorithm-neural Network Hybrid Techniquementioning

confidence: 99%

“…Rath et al (2018c, 2019) have presented a fuzzy logic-based navigational control approach for motion planning of a humanoid robot. Kumar et al (2020a, b) have developed computational intelligence based humanoid navigational models for testing in complex environments.…”

Section: Introductionmentioning

confidence: 99%

Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot

Rath¹,

Parhi

Das

et al. 2020

IJIUS

Self Cite

View full text Add to dashboard Cite

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“…Kumar et al developed and implemented regression controller for path optimization and obstacle avoidance of biped robots during motion planning. They also developed a hybridized regression‐ant colony optimization controller for motion planning and navigation of humanoid robots in complex environments . Sahoo et al applied neural network method in ROBONOVA humanoid robot for navigation in the cluttered environment having more obstacles and obtained the optimal motion planning of humanoid .…”

Section: Introductionmentioning

confidence: 99%

Implementation of grey wolf optimization controller for multiple humanoid navigation

Muni

Parhi

Kumar

2020

Computer Animation & Virtual

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In this paper, grey wolf optimization controller (GWOC) is considered as a multiobjective technique for multiple humanoid navigations. Upon activation of GWOC, the humanoids mimic the group hunting behavior of grey wolves and navigate toward the target in a collision-free manner in presence of both static and dynamic hurdles. The wolves in the pack will either diverge for searching prey or converge together for attacking the prey following the best search agent (Leader Alpha). GWOC has the ability to keep the humanoid free from being trapped in local minima whereas it facilitates it to head toward global minima. GWOC provides better results as compared to other intelligent techniques because of its five characteristics that include safe boundary, protection, following, hunting, and caring. Both simulation and experimental navigation in laboratory conditions for single as well as for multiple humanoid NAOs have been carried out. From the results of simulation and experimental data, it is confirmed that GWOC provides global minima for humanoid robots in complex environments with different shaped obstacles. A Petri-net controller is considered while navigating multiple humanoids, as during multiple humanoid navigations, one humanoid robot acts as a dynamic obstacle to other humanoids. K E Y W O R D SGWOC, NAO humanoid, navigation, petri-net model INTRODUCTIONLot of research works have been performed for path navigation of mobile robots, biped robots, and industrial robots by using both classical and artificial intelligent techniques. But there is still some improvement needed to avoid the trapping of robots in local minima. Today, the main challenging task for researchers is the path navigation of humanoids having multidegrees of freedom by using computational methods. Though several researchers are working on it, still it is in virtue to obtain the best intelligent method for effective and efficient navigation. For this purpose, grey wolf optimization controller (GWOC) is considered. The humanoid NAO considered for this research work is developed by ALDEBARAN robotics France, which is having 25 degrees of freedom. 1 There are sensors for detecting the obstacles, cameras to take the images of hurdles, motors, actuators, and batteries to perform the predefined tasks. The obstacle distances and shape of the obstacles are detected by inbuilt ultrasonic sensors and camera mount on the head. The programmed GWOC activates when NAO finds any obstacle in its path while heading toward target. Several researchers have worked toward the navigation of different kinds of robots using many intelligent techniques as path navigation of robots plays a Comp Anim Virtual Worlds. 2020;31:e1919. wileyonlinelibrary.com/journal/cav

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“…Clever and Mombaur 23 developed a motion transfer scheme from humans to humanoids using a 3D template model. Kumar et al [24][25][26] developed various humanoid motion planning models using computational intelligent techniques. Baskoro and Priyono 27 used zero moment point and inverse kinematics information for stability analysis of a humanoid model.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Strategy Planning of Humanoid Robots Using Glowworm-Based Optimization

et al. 2020

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SUMMARY In this paper, a novel dynamic navigational planning strategy is proposed for single as well as multiple humanoids in intricate environments on a glowworm-based optimization method. The sensory information regarding the obstacle distances and target information are supplied as inputs to the navigational model. The essential turning angle is generated as the output of the controller to avoid obstacles present in the environment and reach the target location with ease. The proposed model is certified in a V-REP simulation software, and the simulation results are authenticated in a real-time setup arranged under testing conditions.

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Intelligent Navigation of a Self-Fabricated Biped Robot using a Regression Controller

Cited by 10 publications

References 33 publications

Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot

Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot

Implementation of grey wolf optimization controller for multiple humanoid navigation

Dynamic Strategy Planning of Humanoid Robots Using Glowworm-Based Optimization

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