Balanced gait generations of a two-legged robot on sloping surface

Vundavilli, Pandu R.; Pratihar, Dilip Kumar

doi:10.1007/s12046-011-0031-7

Cited by 14 publications

(6 citation statements)

References 20 publications

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“…Related to this work, many researchers across the globe present and develop different controllers such as [18][19][20], where the authors proposed on gait generation for a 7-DOF robot. While in [21,22], they present a PID controller with ZMP to test the various joints of motors.…”

Section: Introductionmentioning

confidence: 99%

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

Wassef

Hadi

2023

Eng. rev. (Online)

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In this paper, an adaptive neuro-fuzzy based on fractional-order proportional-integral-derivative (ANFFOPID) controller with an improved slime mould algorithm (ISMA) for the two-legged robot (TLR) is proposed to achieve the minimum angular displacement error of the joint motors. Achieving such error is considered a challenging and time-consuming process due to the gain values set for the FOPID controller. Thus the neural-fuzzy network is used to provide the FOPID input signals by the adaptive magnitude gains. The adaptive mechanism depends on the ISMA to train the neural network weights. The outstanding properties of the ANFFOPID controller are evaluated by comparing the proposed controller with other existing work that is modified chaotic invasive weed optimization based on neural network (MCIWO-NN) for various walking terrains that are flat surface, stair ascending, and stair descending. Finally, the results obtained show the effectiveness of the ANFFOPID controller.

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Section: Introductionmentioning

confidence: 99%

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

Wassef

Hadi

2023

Eng. rev. (Online)

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“…Pandu Ranga Vundavilli and Dilip Kumar Pratihar focused on the stability of the gait creation problem for seven degrees of freedom (DoF) bipedal robot moving down and up through the sloping surface [3]. Sujan Warnakulasooriya et al investigated the design of a humanoid robot.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Experimental Gait Cycle of Two Types of Degree of Freedom Bipedal Robot

AbulKareem¹,

Ali²

2020

(AREJ)

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Another type of legged robots is a bipedal walking robot or humanoid robot, which can be designed to implement various functions as necessary and mimic like a human. Often, balance while moving and when the first leg in the swing process and the second leg on the ground is difficult than most other kinds of robots. Two bipedal robot prototypes are designed with 10 degrees of freedom and 17 degrees of freedom to fulfill a gait cycle. The robot's locomotion can also be controlled via two types of microcontrollers, Arduino microcontroller and LOBOT LSC-32 driver. So, the KHR-2HV simulation model by Webots is used to simulate the experimental results of the bipedal robots. The results showed that the cubic polynomial foot trajectory for 10 degrees of freedom and 17 degrees of freedom bipedal robots are (= 4 × 10 −16 3 − 0.0433 2 + 0.4329 + 0.7619 with regression 0.9276) and (= −0.000074 3 − 0.13 2 + 0.671 + 1.1326 with regression 0.939) respectively. After several methods for programming, the bipedal robot by LOBOT LSC-32 driver model is the better than Arduino with PCA 96685 driver-16 channel servo driver. Experimental results carried out during the KHR-2HV simulation model by Webots program. This model gives a better estimation and a fast response to confirm the stability of the10 degrees of freedom and 17 degrees of freedom bipedal robots.

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“…Experimental results confirmed the effectiveness and applicability of the proposed fuzzy stabilization tuning approach. Vundavilli et al (2007a, b), Vundavilli & Pratihar (2010, 2011a used soft computing-based approaches to study dynamically balanced gaits of a 7-dof biped robot in its SSP for different terrains, namely staircase, sloping surface and ditches. However, their study focussed on optimization of a single objective in a cycle.…”

Section: Introductionmentioning

confidence: 99%

Analysis of double support phase of biped robot and multi-objective optimization using genetic algorithm and particle swarm optimization algorithm

Rajendra

Pratihar

2015

Sadhana

Self Cite

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This paper deals with multi-objective optimization in gait planning of a 7-dof biped robot during its double support phase, while ascending and descending some staircases. For determining dynamic balance margin of the robot in terms of zero-moment point, its double support phase has been assumed to be consisting of two single support phases on non-coincidental parallel surfaces. Thus, dynamic balance margin of the biped robot during its double support phase is obtained by using a virtual zero-moment point of the system. Moreover, a smooth transition from single to double support phases in a cycle is to be maintained for the walking robots. Two contrasting objectives, namely power consumption and dynamic balance margin have been considered during optimization. Pareto-optimal fronts of solutions are obtained using genetic algorithm and particle swarm optimization algorithm, separately. To the best of the authors' knowledge, it is the first attempt to solve multi-objective optimization problem in double support phase of a biped robot.

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Balanced gait generations of a two-legged robot on sloping surface

Cited by 14 publications

References 20 publications

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

An adaptive neuro-fuzzy based on a fractional-order proportional integral derivative design for a two-legged robot with an improved swarm algorithm

Simulation and Experimental Gait Cycle of Two Types of Degree of Freedom Bipedal Robot

Analysis of double support phase of biped robot and multi-objective optimization using genetic algorithm and particle swarm optimization algorithm

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