Hexapod robot locomotion over typical obstacles

AbstractLegged locomotion of robots can be greatly improved by bioinspired tribological structures and by applying the principles of computational morphology to achieve fast and energy-efficient walking. In a previous research, we mounted shark skin on the belly of a hexapod robot to show that the passive anisotropic friction properties of this structure enhance locomotion efficiency, resulting in a stronger grip on varying walking surfaces. This study builds upon these results by using a previously investigated sawtooth structure as a model surface on a legged robot to systematically examine the influences of different material and surface properties on the resulting friction coefficients and the walking behavior of the robot. By employing different surfaces and by varying the stiffness and orientation of the anisotropic structures, we conclude that with having prior knowledge about the walking environment in combination with the tribological properties of these structures, we can greatly improve the robot’s locomotion efficiency.

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Experimental validation of hexapod robot locomotion over different terrain

10.1063/5.0002458 ◽

2020 ◽

Author(s):

Md Masum Billah ◽

Abdul Malik Mohd Ali ◽

Zulkhairi Mohd Yusof ◽

Kushsairy Kadir ◽

Kanendra Naidu Vijyakumar

Keyword(s):

Experimental Validation ◽

Hexapod Robot ◽

Robot Locomotion

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A Distributed Neural Network Architecture for Hexapod Robot Locomotion

Neural Computation ◽

10.1162/neco.1992.4.3.356 ◽

1992 ◽

Vol 4 (3) ◽

pp. 356-365 ◽

Cited By ~ 78

Author(s):

Randall D. Beer ◽

Hillel J. Chiel ◽

Roger D. Quinn ◽

Kenneth S. Espenschied ◽

Patrik Larsson

Keyword(s):

Neural Network ◽

Real World ◽

Network Architecture ◽

Command Neuron ◽

Legged Robot ◽

Hexapod Robot ◽

Neural Network Architecture ◽

Insect Locomotion ◽

Robot Locomotion ◽

Continuous Range

We present fully distributed neural network architecture for controlling the locomotion of a hexapod robot. The design of this network is directly based on work on the neuroethology of insect locomotion. Previously, we demonstrated in simulation that this controller could generate a continuous range of statically stable insect-like gaits as the activity of a single command neuron was varied and that it was robust to a variety of lesions. We now report that the controller can be utilized to direct the locomotion of an actual six-legged robot, and that it exhibits a range of gaits and degree of robustness in the real world that is quite similar to that observed in simulation.

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Leg Coordination Mechanisms in the Stick Insect Applied to Hexapod Robot Locomotion

Adaptive Behavior ◽

10.1177/105971239300100404 ◽

1993 ◽

Vol 1 (4) ◽

pp. 455-468 ◽

Cited By ~ 48

Author(s):

Kenneth S. Espenschied ◽

Hillel J. Chiel ◽

Roger D. Quinn ◽

Randall D. Beer

Keyword(s):

Stick Insect ◽

Coordination Mechanisms ◽

Hexapod Robot ◽

Robot Locomotion ◽

Leg Coordination

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An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel’s Loihi

International Conference on Neuromorphic Systems 2020 ◽

10.1145/3407197.3407205 ◽

2020 ◽

Author(s):

Ioannis Polykretis ◽

Guangzhi Tang ◽

Konstantinos P. Michmizos

Keyword(s):

Central Pattern Generator ◽

Pattern Generator ◽

Hexapod Robot ◽

Robot Locomotion

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Motor Imagery Classification Based on a Recurrent-Convolutional Architecture to Control a Hexapod Robot

Mathematics ◽

10.3390/math9060606 ◽

2021 ◽

Vol 9 (6) ◽

pp. 606

Author(s):

Tat’y Mwata-Velu ◽

Jose Ruiz-Pinales ◽

Horacio Rostro-Gonzalez ◽

Mario Alberto Ibarra-Manzano ◽

Jorge Mario Cruz-Duarte ◽

...

Keyword(s):

Signal Processing ◽

Real Time ◽

Motor Imagery ◽

Scientific Data ◽

Intelligent Vehicles ◽

Hexapod Robot ◽

Eeg Signals ◽

Robot Locomotion ◽

Main Challenge ◽

Bci System

Advances in the field of Brain-Computer Interfaces (BCIs) aim, among other applications, to improve the movement capacities of people suffering from the loss of motor skills. The main challenge in this area is to achieve real-time and accurate bio-signal processing for pattern recognition, especially in Motor Imagery (MI). The significant interaction between brain signals and controllable machines requires instantaneous brain data decoding. In this study, an embedded BCI system based on fist MI signals is developed. It uses an Emotiv EPOC+ Brainwear®, an Altera SoCKit® development board, and a hexapod robot for testing locomotion imagery commands. The system is tested to detect the imagined movements of closing and opening the left and right hand to control the robot locomotion. Electroencephalogram (EEG) signals associated with the motion tasks are sensed on the human sensorimotor cortex. Next, the SoCKit processes the data to identify the commands allowing the controlled robot locomotion. The classification of MI-EEG signals from the F3, F4, FC5, and FC6 sensors is performed using a hybrid architecture of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This method takes advantage of the deep learning recognition model to develop a real-time embedded BCI system, where signal processing must be seamless and precise. The proposed method is evaluated using k-fold cross-validation on both created and public Scientific-Data datasets. Our dataset is comprised of 2400 trials obtained from four test subjects, lasting three seconds of closing and opening fist movement imagination. The recognition tasks reach 84.69% and 79.2% accuracy using our data and a state-of-the-art dataset, respectively. Numerical results support that the motor imagery EEG signals can be successfully applied in BCI systems to control mobile robots and related applications such as intelligent vehicles.

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CENTER OF MASS-BASED ADMITTANCE CONTROL FOR MULTI-LEGGED ROBOT WALKING ON THE BOTTOM OF OCEAN

Jurnal Teknologi ◽

10.11113/jt.v74.4802 ◽

2015 ◽

Vol 74 (9) ◽

Author(s):

Addie Irawan ◽

Md. Moktadir Alam ◽

Yee Yin Tan ◽

Mohd Rizal Arshad

Keyword(s):

Buoyancy Force ◽

Center Of Mass ◽

The Body ◽

Total Force ◽

Vertical Force ◽

Hexapod Robot ◽

Admittance Control ◽

Foot Placement ◽

Robot Locomotion ◽

Foot Motion

This paper presents a proposed adaptive admittance control that is derived based on Center of Mass (CoM) of the hexapod robot designed for walking on the bottom of water or seabed. The study has been carried out by modeling the buoyancy force following the restoration force to achieve the drowning level according to the Archimedes’ principle. The restoration force needs to be positive in order to ensure robot locomotion is not affected by buoyancy factor. As a solution to regulate this force, admittance control has been derived based on the total force of foot placement to determine CoM of the robot while walking. This admittance control is designed according to a model of a real-time based 4-degree of freedom (DoF) leg configuration of a hexapod robot that able to perform hexapod-to-quadruped transformation. The analysis focuses on the robot walking in both configuration modes; hexapod and quadruped; with both tripod and traverse-trot walking pattern respectively. The verification is done on the vertical foot motion of the leg and the body mass coordination movement for each walking simulation. The results show that the proposed admittance control is able to regulate the force restoration factor by making vertical force on each foot sufficiently large (sufficient foot placement) compared to the buoyancy force of the ocean, thus performing stable locomotion for both hexapod and quadruped mode.

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Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation

Computational Intelligence and Neuroscience ◽

10.1155/2016/5615618 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 13

Author(s):

A. Espinal ◽

H. Rostro-Gonzalez ◽

M. Carpio ◽

E. I. Guerra-Hernandez ◽

M. Ornelas-Rodriguez ◽

...

Keyword(s):

Neural Network ◽

Hardware Implementation ◽

Pattern Generator ◽

Quadruped Robot ◽

Spiking Neural Network ◽

Resource Usage ◽

Hexapod Robot ◽

Biologically Inspired ◽

Robot Locomotion ◽

Locomotion System

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

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Matlab Application for Hexapod Robot Locomotion over Obstacles

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.762.195 ◽

2015 ◽

Vol 762 ◽

pp. 195-200

Author(s):

Sorin Manoiu-Olaru ◽

Mircea Nitulescu

Keyword(s):

Interpolation Method ◽

Spline Interpolation ◽

Static Stability ◽

Hexapod Robot ◽

Robot Locomotion ◽

Workspace Analysis ◽

Kinematical Model ◽

Gravitational Stability ◽

Simulation Results ◽

Trajectory Generator

In this paper the authors present a software platform made using Matlab for studying hexapod robot stability in gravitational field and some basic locomotion simulations over obstacles. For the proposed design of the leg was calculated the kinematical model and a workspace analysis was made. The trajectory generator for the leg tip was implemented using piecewise cubic spline interpolation method. Next are presented the most common types of obstacles and the influence upon robot locomotion. The analysis of the robot static stability is made for different cases of loco-motion. The paper includes some simulation results related to the static gravitational stability de-pending on the support polygon, single leg control and locomotion over common types of obstacles.

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