Special Feature on Bio-Inspired Robotics

Fukuda, Toshio; Chen, Fei; Qiu, Shi

doi:10.3390/app8050817

Cited by 8 publications

(6 citation statements)

References 30 publications

(30 reference statements)

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“…The global coordinate system is fixed at terrain level at 0,0,0 coordinates and will be used to account for robot position with regard to the terrain and the environment. Therefore for the first iteration the transformation matrix for the global to body coordinate system T globalbody,0 (alias: T gb,0 ) will be as shown in (1).…”

Section: A Endpoints Handlingmentioning

confidence: 99%

“…The gait engine is responsible for assigning the swing and push roles as well as the direction and amount of space to cover for each iteration tick. The gait engine employed is not fundamental to describe the desired formulation and will not be discussed as already available in literature 1 . Therefore from the user input comprised of x-speed, y-speed and z-axis rotation speed the gait engine provides the movement data the robot is supposed to follow, the kinematic problem is to find the related endpoints displacement.…”

Section: A Endpoints Handlingmentioning

confidence: 99%

“…First of all it's important to unequivocally define what the 'body isolation' condition is: This can be achieved by defining a set of points in the robot body and then consider the height of these points with respect to the ground as a way to evaluate the body relative position to the terrain. A possible approach is to ask for those points to maintain a distance to the ground as close as possible to the one defined by SP z from (1). If these points of interest are correctly chosen in order to represent the vertices of the robot body, the entire base should follow terrain profile and prevent unwanted situations like the ones discussed beforehand.…”

Section: B Terrain Compensation Algorithmmentioning

confidence: 99%

“…Bio-inspired robotics is a fairly new, still early in development branch of modern robotics. Control architectures coming from bio-inspired designs are able to perform complex tasks as to walk, swim, crawl, jump or even fly [1] thanks to specific forms of movement, known as gaits, that can sometimes even surpass in efficiency conventional engineering.…”

Section: Introductionmentioning

confidence: 99%

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Control of a Hexapod Robot Considering Terrain Interaction

Zangrandi¹,

Arrigoni²,

Braghin³

2021

Preprint

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Bio-inspired walking hexapod robots are a relatively young branch in robotics in both state of the art and applications. Despite their high degree of flexibility and adaptability derived by their redundant design, the research field that compliments their abilities is still very lacking. In this paper will be proposed state-of-the-art hexapod robot specific control architecture that allows for full control over robot speed, body orientation and walk gait type to employ. Furthermore terrain interaction will be deeply investigated, leading to the development of a terrain-adapting control algorithm that will allow the robot to react swiftly to terrain shape and asperities such as non-linearities and non-continuity within the workspace. It will be presented a dynamic model derived from the interpretation of the hexapod movement to be comparable to these of the base-platform PKM machines, and said model will be validated through Matlab SimMechanics TM physics simulation. A feed-back control system able to recognise leg-terrain touch and react accordingly to assure movement stability will then be developed. Finally results coming from an experimental campaign based of the PhantomX AX Metal Hexapod Mark II robotic platform by Trossen Robotics TM is reported.

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Section: A Endpoints Handlingmentioning

confidence: 99%

Section: A Endpoints Handlingmentioning

confidence: 99%

Section: B Terrain Compensation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Control of a Hexapod Robot Considering Terrain Interaction

Zangrandi¹,

Arrigoni²,

Braghin³

2021

Preprint

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“…Nature provides investigators with abundant insights and inspirations that aid in solving engineering problems and creating new inventions [1,2]. With the advancement of science, engineering, and technology, a growing number of robots have been designed in different shapes and sizes to mimic biological systems [3,4]. These biomimetic robots mimic animals, such as four-legged animals (quadrupeds) [5], monkeys [6], snakes [7], fish [8,9], bats [10,11], and various birds and insects [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors

Aurecianus

Park

et al. 2022

Applied Sciences

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In this paper, model parameter identification results are presented for a longitudinal mode dynamic model of an insect-like tailless flapping-wing micro air vehicle (FWMAV) using angle and angular rate data from onboard sensors only. A gray box model approach with indirect method was utilized with adaptive Gauss–Newton, Levenberg–Marquardt, and gradient search identification methods. Regular and low-frequency reference commands were mainly used for identification since they gave higher fit percentages than irregular and high-frequency reference commands. Dynamic parameters obtained using three identification methods with two different datasets were similar to each other, indicating that the obtained dynamic model was sufficiently reliable. Most of the identified dynamic model parameters had similar values to the computationally obtained ones, except stability derivatives for pitching moment with forward velocity and pitching rate variations. Differences were mainly due to certain neglected body, nonlinear dynamics, and the shift of the center of gravity. Fit percentage of the identified dynamic model (~49%) was more than two-fold higher than that of the computationally obtained one (~22%). Frequency domain analysis showed that the identified model was much different from that of the computationally obtained one in the frequency range of 0.3 rad/s to 5 rad/s, which affected transient responses. Both dynamic models showed that the phase margin was very low, and that it should be increased by a feedback controller to have a robustly stable system. The stable dominant pole of the identified model had a higher magnitude which resulted in faster responses. The identified dynamic model exhibited much closer responses to experimental flight data in pitching motion than the computationally obtained dynamic model, demonstrating that the identified dynamic model could be used for the design of more effective pitch angle-stabilizing controllers.

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