Waves and locomotion control of bio-inspired robots

In this lecture two applications of wave-computing to control bio-inspired robots are discussed. In the first example, the paradigm of emergent wave-computation is applied to locomotion control in legged robots: the locomotion gait is the result of the self-organization of a network of locally coupled nonlinear oscillators. This means to adopt the biological paradigm of Central Pattern Generator (CPG) that is here implemented by using Cellular Neural Network (CNN). The whole control strategy is hybrid in the sense that the gait generation is accomplished by the CNN and is fully analog, while a simple logic unit modulates the behavior of the CNN-based CPG to allow the locomotion system to adapt to sensory feedback. Methodological issues, the design of a VLSI chip implementing the CNN-based CPG and experimental results on robot prototypes are presented. The experimental results confirm the suitability of the approach and open the way to the design of a fully autonomous bio-inspired micro-robot. In the second example, a new methodology and experimental implementations for real-time wave-based robot navigation in a complex, dynamically changing environment are introduced. The main idea behind the approach is to consider the robot arena as an excitable medium, in which moving objects, obstacles and the target, are represented by sites of autowave generation: the target generates attractive waves, while the obstacles repulsive ones. The moving robot detects traveling and colliding wave fronts and uses the information about dynamics of the autowaves to adapt its direction of collision-free motion towards the target. This approach allows to achieve a highly adaptive robot behaviour and thus an optimal path along which the robot reaches the target while avoiding obstacles. At the computational and experimental levels principles of computation in reaction-diffusion (RD) nonlinear active media are adopted. Nonlinear media where autowaves are used for information processing purposes can therefore be considered as RD computing devices. In this paper I design and experiment with two types of RD processors: computational CNN processor and experimental RD CNN VLSI chip, the Complex Analog and Logic Computing Engine (CACElk). I demonstrate how to experimentally implement robot navigation using space-time snapshots of active chemical medium and how to overcome low speed limitation of this 'wetware' implementation in CNN-based silicon processors.