Hormone-based control for self-reconfigurable robots

Shen, Wei-Min; Lu, Yimin; Will, Peter

doi:10.1145/336595.336602

Cited by 54 publications

(34 citation statements)

References 8 publications

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“…There is also an interpreter acting between the CC and the embedded behaviours: the heterogeneous agent. 5 The heterogeneous agents of all modules form the heterogeneous layer, which is called a middle layer because it acts between the CC (highest-level layer) and the onboard control (lowest-level layer). Regarding the physical layout, control is divided in (Fig.…”

Section: Layer Structurementioning

confidence: 99%

“…Most modular robot designs are homogeneous [3][4][5][6][7][8], at least in a locomotive sense. Polypod [9] and I-Cubes [10] are composed of two types of module, one of which is passive and mainly functions to carry the power supply.…”

Section: Introductionmentioning

confidence: 99%

“…Among homogeneous modular robot architectures, three of them have been found to be of special interest: CONRO [13,5], PolyBot [14,15] and M-TRAN [16,17]. All of these feature control algorithms for distinct locomotion modes.…”

Section: Introductionmentioning

confidence: 99%

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A behaviour-based control architecture for heterogeneous modular, multi-configurable, chained micro-robots

Brunete

Hernando

Gambao

et al. 2012

Robotics and Autonomous Systems

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This article presents a new control architecture designed for heterogeneous modular, multi-configurable, chained micro-robots. This architecture attempts to fill the gap that exists in heterogeneous modular robotics research, in which little work has been conducted compared to that in homogeneous modular robotics studies. The architecture proposes a three-layer structure with a behaviour-based, low-level embedded layer, a halfdeliberative half-behaviour-based high layer for the central control, and a heterogeneous middle layer acting as a bridge between these two layers. This middle layer is very important because it allows the central control to treat all modules in the same manner, facilitating the control of the robot. A communication protocol and a module description language were also developed for the control architecture to facilitate communication and information flow between the heterogeneous modules and the central control. Owing to the heterogeneous behaviour of the architecture, the system can automatically reconfigure its actions to adapt to unpredicted events (such as actuator failure). Several behaviours (at low and high levels) are also presented here.

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Section: Layer Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A behaviour-based control architecture for heterogeneous modular, multi-configurable, chained micro-robots

Brunete

Hernando

Gambao

et al. 2012

Robotics and Autonomous Systems

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“…Hormone-based control as described in (Shen et al 2000, Salemi et al 2001, partially inspired the concept of active self-assembly. The "hormones" presented in these papers are messages, which trigger different actions in different places.…”

Section: Related Workmentioning

confidence: 99%

Shape Restoration by Active Self-Assembly

Arbuckle

Requicha

2005

Applied Bionics and Biomechanics

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Shape restoration is defined as the problem of constructing a desired, or goal, solid shape Sg by growing an initial solid Si, which is a subset of the goal but is otherwise unknown. This definition attempts to capture abstractly a situation that often arises in the physical world when a solid object loses its desired shape due to wear and tear, corrosion or other phenomena. For example, if the top of the femur becomes distorted, the hip joint no longer functions properly and may have to be replaced surgically. Growing it in place back to its original shape would be an attractive alternative to replacement. This paper presents a solution to the shape restoration problem by using autonomous assembly agents (robots) that self-assemble to fill the volume between Sg and Si. If the robots have very small dimension (micro or nano), the desired shape is approximated with high accuracy. The assembly agents initially execute a random walk. When two robots meet, they may exchange a small number of messages. The robot behavior is controlled by a finite state machine with a small number of states. Communication contact models chemical communication, which is likely to be the medium of choice for robots at the nanoscale, while small state and small messages are limitations that also are expected of nanorobots. Simulations presented here show that swarms of such robots organize themselves to achieve shape restoration by using distributed algorithms. This is one more example of an interesting geometric problem that can be solved by the Active Self-Assembly paradigm introduced in previous papers by the authors.

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“…In particular, we describe the Digital Hormone Model (DHM) that is generalized from an existing distributed control system for self-reconfigurable robots [1][2][3]. The model is inspired by the fact that many complex patterns in biological systems appear to be the results of self-organization among homogenous cells regulated by hormones, and selforganization is based on local interactions among cells rather than super-imposed and pre-determined global structures [6,7].…”

Section: Introductionmentioning

confidence: 99%