Perception-driven adaptive CPG-based locomotion for hexapod robots

Barrón-Zambrano, José Hugo; Torres-Huitzil, César; Girau, Bernard

doi:10.1016/j.neucom.2015.02.087

Cited by 26 publications

(17 citation statements)

References 24 publications

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“…It can be seen that: (1) The robot joints output a coordinated rhythmic signal; when the leg curve rose, the corresponding leg was in the swing phase; when the leg curve dropped, the corresponding leg was in the support phase. (2) The phase difference between leg groups in the hip joint output curve, denoted as π, amounts to half a cycle, such that different leg groups took turns to complete coordinated movement; the hip joint control signals were the same among legs in the same group.…”

Section: Debugging and Results Analysismentioning

confidence: 99%

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Gait Control of Hexapod Robot Based on Field-Programmable Gate Array and Central Pattern Generator

Qiao¹

2020

JESA

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This paper attempts to improve the terrain adaptability of hexapod robot through gait control. Firstly, the multi-leg coupling in the tripodal gait of the hexapod robot was modeled by Hopf oscillator. Then, annular central pattern generator (CPG) was adopted to simulate the leg movements of hexapod robot between signals. Furthermore, a physical prototype was designed for the gait control test on field-programmable gate array (FPGA), and the algorithm of the rhythmic output of the model was programmed in Verilog, a hardware description language. Finally, the effectiveness of our gait control method was verified through the simulation on Xilinx. The results show that the phase difference of the CPG network remained stable; the designed hexapod robot moved at about 5.15cm/s stably in a tripodal gait, and outperformed wheeled and tracked robots in terrain adaptation. The research findings lay a solid basis for the design of all-terrain multi-leg robots.

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Section: Debugging and Results Analysismentioning

confidence: 99%

“…For hexapod robots, the gait is usually controlled by kinematics and dynamics models, which integrate model simplification, stability criterion, and energy optimization [1,2]. By these models, the robot model is firstly reduced by the stability criterion into a simple inverted pendulum.…”

Section: Introductionmentioning

confidence: 99%

Gait Control of Hexapod Robot Based on Field-Programmable Gate Array and Central Pattern Generator

Qiao¹

2020

JESA

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“…That is to say, a control diagram similar to the one seen in animals denominated central pattern generator. 44 The locomotion of hexapod robots is one of the most popular ones due to the ability to generate an inherent walking in a stable state. The type of walking in stable, mostly used in these robots, is known as a tripod.…”

Section: Robot Platformsmentioning

confidence: 99%

Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

Martinez-Ledezma

Barrón-Zambrano

Diaz-Manríquez

et al. 2020

International Journal of Advanced Robotic Systems

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Brain–computer interfaces (BCI) have been focused on improving people’s lifestyles with motor or communication disabilities. However, the utilization of this technology has found news applications, such as increasing human capacities. Nowadays, several researchers are working on probing human capabilities to control several robotic devices simultaneously. The design of BCI is an intricate work that needs a long time to its implementation. For this reason, an architecture to design and implement different types of BCIs is presented in this article. The architecture has a modular design capable of reading various electroencephalography (EEG) sensors and controlling several robotic devices similar to the plug-and-play paradigm. To test the proposed architecture, a BCI was able to manage a hexapod robot and a drone was implemented. Firstly, a mobile robotic platform was designed and implemented. The BCI is based on eye blinking, where a single blinking represents a robot command. The command orders the robot to initiate or stops their locomotion for the hexapod robot. For the drone, a blink represents the takeoff or landing order. The blinking signals are obtained from the prefrontal and frontal regions of the head by EEG sensors. The signals are then filtered using temporal filters, with cutoff frequencies based on delta, theta, alpha, and beta waves. The filtered signals were labeled and used to train a classifier based on the multilayer perceptron (MLP) model. To generate the robot command, the proposal BCI used two models of MLP to ensure the classifier prediction. So, when the two classifiers make the same prediction, within a defined time interval, send the signal to the robot to start or stop its movement. The obtained results show that it is possible to get high precision to control the hexapod robot with a precision of 91.7% and an average of 81.4%.

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“…16 Barron-Zambrano et al proposed a CPG-based control strategy, which can adjust the motion speed according to visual information and manage the smooth gait transition in the hexapod robot. 17 The stability of motion is an important guarantee for the robot to complete the task. Static stability margin (SSM) is suitable for robot's motion without external interference and low speed, 18 and zero moment point (ZMP) is applicable to robot's motion with external interference.…”

Section: Introductionmentioning

confidence: 99%

The gait planning of hexapod robot based on CPG with feedback

Wang

Zhang

Yang

et al. 2020

International Journal of Advanced Robotic Systems

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To realize the omnidirectional motion, the transition motion of hexapod robot from flat to slope is studied, and a new type of stability criterion is proposed. Firstly, the landing point problem of the hexapod robot in the process of transition is studied, the relationship between the introduced angle in ankle of the supporting leg and the body pitch is acquired, and the transition gait based on central pattern generator bottom feedback is planned. Secondly, the slope motion is analyzed, the relationship between the angle variable of the supporting knee joint and the pitch angle of hexapod is obtained, and the slope gait is planned based on central pattern generator middle level feedback. According to vector product, the solution of working out the stability margin of hexapod robot’s motion is designed. Lastly, MATLAB/ADAMAS co-simulation platform and physical hardware are constructed, the simulation and experiment of transition motion of hexapod robot from flat to 12° slope and motion of climbing 16° slope are done. According to the analysis of the results, in the transition motion from flat to 12° slope, based on the transition gait, hexapod robot can keep three foots touch the ground, and the foot force is uniform. According to the means designed to work out a stability margin based on vector product, the stability margin constant is greater than zero. In the motion of climbing 16° slope, based on the slope gait, hexapod robot completes the motion of climbing 16° slope. Based on transition gait, hexapod robot implements the transition movement from flat to slope stably. Based on slope gait, hexapod robot improves the ability of slope motion.

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Perception-driven adaptive CPG-based locomotion for hexapod robots

Cited by 26 publications

References 24 publications

Gait Control of Hexapod Robot Based on Field-Programmable Gate Array and Central Pattern Generator

Gait Control of Hexapod Robot Based on Field-Programmable Gate Array and Central Pattern Generator

Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

The gait planning of hexapod robot based on CPG with feedback

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