Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

In the era of robotics, path planning and navigation of bipeds in multifaceted environments have always remained as one of the most promising areas of research. In this paper, a novel hybridized navigational controller is proposed using the logic of both statistical technique and computational intelligence method for path planning of bipeds. The proposed path controller is a hybridization of regression analysis with adaptive particle swarm optimization. The inputs given to the regression controller are in the forms of obstacle distances, and the output of the regression controller is interim turning angle. The output interim turning angle is again fed to the adaptive particle swarm optimization controller along with other inputs. The output of the adaptive particle swarm optimization controller termed as final turning angle acts as the directing factor for smooth navigation of bipeds in a cluttered environment. The proposed navigational controller is experimented for a bipedal robot in simulation and experimental environments. The outputs from various simulations and experimental results are compared and good agreement between navigational parameters like path length and path time is observed. Again a better efficacy has been observed by comparing the designed hybridization technique with an existing approach which validates the novel approach.

Department of Design and Automation

School of Mechanical Engineering

Vellore Campus

World University root node

Soft Computing

Navigational strategy of a biped robot using regression-adaptive PSO approach

This research work focuses on navigational strategy of humanoid robots in complex environments using a fuzzy embedded neural network based controller. The obstacle distances are measured from robot’s current position and referred as front obstacle distance, right obstacle distance and left obstacle distance. These obstacle distances are served as input variables to the neural network model and target angle is obtained as output parameter. The target angle obtained from neural network is fed to the Mamdani fuzzy system along with the obstacle distances as input variables to obtain the effective target angle for the humanoid robot. A Petri-net controller is embedded with developed neuro-fuzzy controller to perform dynamic path analysis in complex workspaces Single as well as multiple humanoid robots are used to analyze simulation and experimental navigation in different complex environments using developed neuro-fuzzy-petri-net controller. Various simulations are carried out using V-REP simulation software and similar scenario as per simulation is developed under laboratory conditions for various experimental navigation. The results from both the scenarios are related and are found to be in good covenant with each other having permissible range of errors. Simulation and experimental results in relation to navigational parameters shows the robustness of the developed controller. Surface plots and contour plots developed from the designed controller shows the effectiveness and efficacy in achieving global path during motion planning through optimizing target angle. To validate the results and to find out the effectiveness, the developed controller is compared with existing techniques such as IDQ and substantial progress of 16.66% in relation to path length is observed.

Applied Soft Computing

Towards motion planning of humanoids using a fuzzy embedded neural network approach

This paper focuses on navigation of a humanoid robot cluttered with obstacles, avoiding collisions in static environment using Prim’s algorithm. Prim’s algorithm is a minimum spanning tree (MST) method with greedy approach which uses the concept of sets. It generates the MST by selecting least weights from the weighted graph and randomly forms disjoint sets with picking one least weight edge from the ones remaining for creating node incident to form the tree. Similar approach repeats for selecting all ‘n – 1’ edges to the tree which is the path direction to humanoid NAO. The developed algorithm is implemented in both simulation and experimental platforms to obtain the navigational results. The simulation and experimental navigational results confirm the efficiency of the path planning strategy as the percentage of deviations of navigational parameters is below 6%. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

Global Path Optimization of Humanoid NAO in Static Environment Using Prim’s Algorithm

This research work utilizes the concept of recurrent strategy of neurons, which performs sequential tasks where the output and input data are dependent with each other. The major advantage of using recurrent neural network (RNN) for humanoid motion planning lies in spending the previous used long sequence information through memory. RNNs form direct cycles having internal state and form prime candidate for handling learning procedure. In this paper, long short-term memory (LSTM) RNN is implemented in humanoid robot to test the motion planning analysis. In the neural network model, the obstacle distances from robot’s location are fed as input parameters, and moving angle (MA) is obtained as the output parameter from RNN to guide the humanoid to reach the target with LSTM. Both simulation and experimental navigations are carried out through the developed technique. Probability plot between the simulation and experimental results is performed with normal distribution with comparison analysis. It is found that the results are satisfactory for humanoid navigation. The percentage of deviation between simulation and experimental results in terms of navigation variable is below 6%, which is in acceptable limit range. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Mechanical Engineering

Probability Plot Result Comparison with Recurrent Neural Network Approach for Path Navigation of a Humanoid in Complex Terrain

In the current research, kinematic analysis of a humanoid NAO is attempted. Here, both Denavit-Hartenberg (DH) parameter approach and multibody formulation approach have been analyzed. In the DH parameter approach, the NAO robot is solved by separating it into five individual kinematic chains. In the multibody formulation approach, NAO is divided into 15 segments, and each segment is analyzed. Kinematic analysis holds a significant importance; as from the data obtained in the kinematic analysis, the robots can be designed for real-time path planning and navigation. The current analysis is a novel approach to analyze the NAO based on its kinematic constraints. © The Author(s), 2021. Published by Cambridge University Press.

Robotica

Humanoid NAO: A Kinematic Encounter

With the growing demand for using biped robots in industrial automation and other related applications, navigation and path planning has emerged as one of the most challenging research topics over the last few decades. In this paper, a novel navigational controller is designed and implemented in a self-fabricated biped robot. After fabricating biped robots equipped with a large set of sensors, a regression controller is implemented on them for the purposes of obstacle avoidance and path optimization. The obstacle distances detected by the biped's sensory network are fed as input parameters to the regression controller, and the output obtained from the controller is the necessary heading angle required to avoid the obstacles present randomly in the environment. The biped is tested in a simulated environment for obstacle avoidance and target-following behavior. Further, to validate the simulation results, a real-time experimental setup is designed under laboratory conditions. The results obtained from both environments are compared in terms of navigational parameters and, then, good agreement is observed between them. Being a relatively new area of research, the navigation of bipeds can pave the way towards industrial automation. © 2020 Sharif University of Technology. All rights reserved.

Scientia Iranica

Intelligent navigation of a self-fabricated biped robot using a regression controller

Industrial Robot

An approach to optimize the path of humanoid robots using a hybridized regression-adaptive particle swarm optimization-adaptive ant colony optimization method

Static and dynamic path planning of humanoids using an advanced regression controller

Humanoids are preferred over their wheeled counter parts because of their ability to replace human efforts. Navigation and path planning of humanoids is very much important and challenging area of investigation for robotic researchers to enable the humanoids for accomplishing tedious and repetitive tasks. In this paper, a novel hybridization scheme is attempted for the path planning and navigation of humanoids in a cluttered environment. Here, hybridization has been attempted on NAO humanoid robots using regression technique and adaptive ant colony optimization. In the hybridization scheme, the navigational parameters of the humanoids are fed to the regression controller initially in terms of obstacle distances and the interim output from the regression controller is again fed to the adaptive ant colony optimization controller to obtain the final output. By using V-REP software, navigation simulations are performed and the simulation results are also tested against real experimental set-up developed under laboratory conditions. The simulation and experimental results reveal that the humanoids are successful in avoiding the obstacles and reach their destinations safely with path optimization. The results obtained from both the environments are compared against each other and are in good agreement with minimal percentage of errors. The navigational controller is tested for both single as well as multiple humanoids and it works well for both the cases. Finally, the proposed controller is validated against other navigational approaches and a significant enhancement of results is obtained.

Applied Soft Computing Journal

A hybridized regression-adaptive ant colony optimization approach for navigation of humanoids in a cluttered environment

International Journal on Artificial Intelligence Tools

An intelligent path planning approach for humanoid robots using adaptive particle swarm optimization

Journal of Bionic Engineering

An Approach to Optimize the Path of Humanoids using Adaptive Ant Colony Optimization

Archives of Control Sciences

Navigation of humanoids by a hybridized regression-adaptive particle swarm optimization approach

iEECON 2018 - 6th International Electrical Engineering Congress

Humanoid Navigation: An Intelligent Computer Vision Based Approach

Computer Animation and Virtual Worlds

Navigation of multiple humanoid robots using hybrid adaptive swarm-adaptive ant colony optimisation technique

With an increasing demand in the field of industrial automation, robotics research has occupied a significant attention of people dealing with automation technology. In this paper, a hybridization technique is proposed combining regression analysis with adaptive particle swarm optimization for navigation of humanoids. In context of humanoid navigation, sensory information regarding obstacle distances are fed as input parameters to a basic regression controller and the output of the regression controller is again fed as input to the adaptive particle swarm optimization controller to obtain the final output. The final output of the hybridized controller acts as the controlling factor for humanoid navigation in a complex environment. The logic of the proposed hybridized controller is tested in both simulated and experimental environments and the results obtained from both the environments are compared against each other with a good agreement between them. Finally, the proposed controller is also tested against other existing navigational techniques to validate the efficiency.