Behaviour-based modelling of hexapod locomotion:: linking biology and technical application
Behaviour-based modelling of hexapod locomotion:: linking biology and technical application
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DOI：
10.1016/j.asd.2004.05.004
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发表时间：
2004-07-01
影响因子：
2
通讯作者：
Cruse, H
中科院分区：
文献类型：
--
作者：
Dürr, V;Schmitz, J;Cruse, H
昆虫和大多数六足机器人的行走需要同时控制多达18个关节。此外，通过身体和地面机械耦合的接头的数量从一个时刻到下一个时刻变化，并且外部条件例如摩擦、柔性和基底的斜率通常是不可预测的。因此，行走行为需要对许多自由度进行自适应、上下文相关的控制。因此，建模腿运动解决了许多方面的任何电机behaviors.Based从节肢动物的行为实验的结果，我们描述了一个运动学模型的六足行走：分布式人工神经网络控制器WALKNET。从概念上讲，该模型解决了腿部运动的三个基本问题。(I)首先，几条腿的协调需要相邻腿的步进周期之间的耦合，优化协同推进，但通过灵活调整外部干扰来确保稳定性。一组行为派生的腿协调规则可以考虑分散生成不同的步态，并允许稳定行走的昆虫模型以及一些腿的机器人。(II)其次，必须能够进行各种不同的腿部运动，例如寻找立足点、抓取物体或梳理身体表面。我们提出了一个简单的神经网络控制器，可以模拟有针对性的摆动轨迹，避障反射和循环搜索运动。(III)第三，通过利用身体、腿和基底之间的物理相互作用来实现站立时腿的机械耦合关节的控制。局部正位移反馈作用于单个腿部关节，将关节的被动位移转换为主动运动，在所有机械耦合关节中产生协同辅助反射。(C)2004爱思唯尔有限公司保留所有权利。
Walking in insects and most six-legged robots requires simultaneous control of up to 18 joints. Moreover, the number of joints that are mechanically coupled via body and ground varies from one moment to the next, and external conditions such as friction, compliance and slope of the substrate are often unpredictable. Thus, walking behaviour requires adaptive, context-dependent control of many degrees of freedom. As a consequence, modelling legged locomotion addresses many aspects of any motor behaviour in general.Based on results from behavioural experiments on arthropods, we describe a kinematic model of hexapod walking: the distributed artificial neural network controller WALKNET. Conceptually, the model addresses three basic problems in legged locomotion. (I) First, coordination of several legs requires coupling between the step cycles of adjacent legs, optimising synergistic propulsion, but ensuring stability through flexible adjustment to external disturbances. A set of behaviourally derived leg coordination rules can account for decentralised generation of different gaits, and allows stable walking of the insect model as well as of a number of legged robots. (II) Second, a wide range of different leg movements must be possible, e.g. to search for foothold, grasp for objects or groom the body surface. We present a simple neural network controller that can simulate targeted swing trajectories, obstacle avoidance reflexes and cyclic searching-movements. (III) Third, control of mechanically coupled joints of the legs in stance is achieved by exploiting the physical interactions between body, legs and substrate. A local positive displacement feedback, acting on individual leg joints, transforms passive displacement of a joint into active movement, generating synergistic assistance reflexes in all mechanically coupled joints. (C) 2004 Elsevier Ltd. All rights reserved.