A reconfigurable spherical robot

Chadil, N.; Phadoongsidhi, Marong; Suwannasit, K.; Manoonpong, Poramate; Laksanacharoen, Pudit

doi:10.1109/icra.2011.5979756

Cited by 15 publications

(11 citation statements)

References 4 publications

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“…Due to modularity, the PSN and VRN are typically independent of each other in their functioning and do not influence or become influenced by other components. Thus, they can be combined to form controllers of different types of robots (Manoonpong et al, 2007, 2008b; Steingrube et al, 2010; Chadil et al, 2011) where they do not require fine tuning for the specific system in which they are employed.…”

Section: Methodsmentioning

confidence: 99%

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

Manoonpong¹,

Parlitz²,

Wörgötter³

2013

Front. Neural Circuits

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165

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Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

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Section: Methodsmentioning

confidence: 99%

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

Manoonpong¹,

Parlitz²,

Wörgötter³

2013

Front. Neural Circuits

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100

165

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“…sequence. 1 The robot also has two infrared (IR) sensors installed at its front to detect obstacles. We use the physics simulation environment called Yet Another Robot Simulator (YARS) 6,7 to simulate the robot ( Fig.…”

Section: Three-legged Reconfigurable Robot With Omnidirectional Wheelsmentioning

confidence: 99%

“…During the last years, we have developed a physical three-legged reconfigurable robot with omnidirectional wheels. 1 It combines the concept of using legs, wheels, and rolling sphere for multi-locomotion modes. Due to its closed-spherical shape, it can roll passively where this rolling motion can minimize the friction and lead to energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Neural Control of a Three-Legged Reconfigurable Robot With Omnidirectional Wheels

Manoonpong

Wörgötter²,

Laksanacharoen³

2013

Nature-Inspired Mobile Robotics

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In this article, we present neural control of a three-legged reconfigurable robot with omnidirectional wheels. It is systematically synthesized based on a modular structure such that the neuromodules are small and their structurefunction relationship can be understood. The resulting network consists of four main modules. A so-called minimal recurrent control (MRC) module is for sensory signal processing and state memorization. It directly drives the motion of two front wheels while a rear wheel is indirectly controlled through a velocity regulating network (VRN) module. In parallel, a simple neural oscillator network module serves as a central pattern generator (CPG) producing basic rhythmic signals for sidestepping where stepping directions are controlled by a phase switching network (PSN) module. The combination of these neuromodules generates various locomotion patterns. Applying sensor inputs to the neural controller enables the robot to avoid obstacles as well as a corner. The presented neuromodules are developed and firstly tested using a physical simulation environment, and then finally transferred to the real robot.

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“…Chen et al 18 proposed a leg-wheel transformable mobile robot. Miniature jumping robots with leghopping and rolling capabilities had been proposed in Armour et al 2 A preliminary attempt to solve the terrain perception issue of reconfigurable legged robots was presented in Sinha et al 19 Chadil et al 20 presented a reconfigurable spherical robot that can be reconfigured into a form of two interconnected hemispheres with three legs equipped with omni-directional wheels. A quadruped robot based on modularized electrical paddle modules was designed, 21 which could achieve both legged and wheeled locomotion.…”

Section: Introductionmentioning

confidence: 99%

Scorpio

Tan

Elara

Elangovan

2016

International Journal of Advanced Robotic Systems

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This paper presents the bio-inspired design, realization, and validation of a reconfigurable rolling-crawling robot. The developed platform is able to mimic Cebrennus rechenbergi, a species of huntsman spider which can crawl and roll using only its legs. Mechanical design, control architecture, and actuator selection strategies targeting platform miniaturization are presented in detail. The navigating and autonomous capabilities of the robot are examined in two facets: (1) recovery behaviors where a robot in a previously unknown state after a fall recovers autonomously to a known standing gait state using Inertial Measurement Unit (IMU); and (2) terrain perception where the robot is capable of autonomously assessing the characteristics of the terrain and chooses the appropriate morphology and locomotion mode in relation to the perceived terrain.

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A reconfigurable spherical robot

Cited by 15 publications

References 4 publications

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

Neural Control of a Three-Legged Reconfigurable Robot With Omnidirectional Wheels

Scorpio

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