Insect walking is based on a decentralized architecture revealing a simple and robust controller

Abstract

Control of walking in rugged terrain requires one to incorporate different issues, such as the mechanical properties of legs and muscles, the neuronal control structures for the single leg, the mechanics and neuronal control structures for the coordination between legs, as well as central decisions that are based on external information and on internal states. Walking in predictable environments and fast running, to a large degree, rely on muscle mechanics. Conversely, slow walking in unpredictable terrain, e.g. climbing in rugged structures, has to rely on neuronal systems that monitor and intelligently react to specific properties of the environment. An arthropod model system that shows the latter abilities is the stick insect, based on which this review will be focused. An insect, when moving its six legs, has to control 18 joints, three per leg, and therefore has to control 18 degrees of freedom (d.f.). As the body position in space is determined by 6 d.f. only, there are 12 d.f. open to be selected. Therefore, a fundamental problem is as to how these extra d.f. are controlled. Based mainly on behavioural experiments and simulation studies, but also including neurophysiological results, the following control structures have been revealed. Legs act as basically independent systems. The quasi-rhythmic movement of the individual leg can be described to result from a structure that exploits mechanical coupling of the legs via the ground and the body. Furthermore, neuronally mediated influences act locally between neighbouring legs, leading to the emergence of insect-type gaits. The underlying controller can be described as a free gait controller. Cooperation of the legs being in stance mode is assumed to be based on mechanical coupling plus local positive feedback controllers. These controllers, acting on individual leg joints, transform a passive displacement of a joint into an active movement, generating synergistic assistance reflexes in all mechanically coupled joints. This architecture is summarized in the form of the artificial neural network, Walknet, that is heavily dependent on sensory feedback at the proprioceptive level. Exteroceptive feedback is exploited for global decisions, such as the walking direction and velocity.

A single muscle's multifunctional control potential of body dynamics for postural control and running

“…The nervous system must exert its effects through the existing dynamics [5,23], and here we have discovered that this neuromechanical coupling can reveal unexpected control functions even at the level of individual muscles. During slow locomotion, simulations of neural feedback acting through a set of coordination rules have had success in capturing locomotion in quasi-static regimes, including walking, gap crossing and foothold searching [8,10,47,49,50]. For posture control tasks, the linear control potentials of the ventral femoral extensor (figure 3a) support the simple transfer functions in these models.…”

“…A limb phase of zero was defined as the onset of limb extension or the anterior extreme position (AEP), which is approximately the point of stance initiation [5,45]. Limb phase has been used extensively in the literature [5,7,8,45,47] and is important for determining the relative timing of muscle activity with respect to muscle strain resulting from limb movement [16]. However, limb phase is typically a poor estimate of the actual neuromechanical gait of the animal because trajectories of individual limbs are highly variable [34].…”

“…For slow, precise locomotor behaviours, detailed neurophysiological studies have revealed neural feedback strategies for control with seemingly little dependence on underlying mechanics [7][8][9][10]. However, as an organism moves more dynamically, sensory structures are strained and deformed [11,12], processing delays become challenging [3,6,13,14], muscles undergo dynamic oscillations with complex history dependence [1,[15][16][17][18] and inertial effects increase [3,4].…”

Insects running on elastic surfaces

“…The former strategy is referred to as a feedback control strategy, whereas the latter is a feedforward control strategy. An extensive literature exists on feedback control strategies used by stick insects and other arthropods performing slow, quasi-static walking behaviour (Büschges et al, 2008;Cruse et al, 2007;Ritzmann and Büschges, 2007), including the finding that feedback-mediated control strategies change with substrate compliance (Cruse et al, 2004). Feedback is used to control individual legs (Burrows, 1992), limb loads (Noah et al, 2004), and inter-leg coordination of the timing of leg movements (Cruse, 1990).…”

Walking: technology and biology

“…Humans belong to the mammals, and mammals possess much more complex control behaviour than insects, which are easier to investigate and where as a consequence quite a lot about walking and walking control is known. Cruse et al (2006), Gabriel & Büschges (2006) and their research groups are working on the problems of walking and walking control of stick insects. The control concept of these insects is widely decentralized including more or less three layers, the lowest layers for stabilization purposes, the middle layer in a similar form as a finite state machine for controlling the cycles and regarding all unexpected obstacles and the upper layer for organizing the gaits by influencing neighbouring legs shifting their phase in the right direction, inhibiting some actions or set actions going.…”