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Research in the domain of biologically inspired walking machines has been ongoing for over 20 years [59, 166, 190, 199, 207]. Most of it has focused on the construction of such machines [34, 47, 216, 223], on a dynamic gait control [43, 117, 201] and on the generation of an advanced locomotion control [30, 56, 104, 120], for instance on rough terrain [5, 66, 102, 180, 192]. In general, these walking machines were solely designed for the purpose of motion without responding to environmental stimuli. However, from this research area, only a few works have presented physical walking machines reacting to an environmental stimulus using different approaches [6, 36, 72, 95]. On the one hand, this shows that less attention has been paid to walking machines performing reactive behaviors. On the other hand, such complex systems can serve as a methodology for the study of embodied systems consisting of sensors and actuators for explicit agent-environment interactions.

Most of this book is devoted to creating and demonstrating so-called artificial perception-action systems inspired by biological sensing systems (perception) and animal behavior (action). Thus this chapter attempts to provide the biological background for understanding the approach taken in this book. It begins with a short introduction to some of the necessary principles of animal behavior. Then it concentrates on the obstacle avoidance and escape behavior of a scorpion and a cockroach, and continues with the prey capture behavior of a spider. Here, attention is given to the biological sensing systems used to trigger the described behaviors. Furthermore, different morphologies of walking animals are presented as inspiration for the design of walking machine platforms. Finally, a biologically inspired locomotion control, called a “central pattern generator” (CPG), is also discussed. This concept is later employed to generate the rhythmic leg movements of the machines.

This chapter presents methods and tools which are to be used throughout this book. It starts with a short introduction to a biological neuron together with an artificial neuron which is followed by the comparison of network structures between feedforward and recurrent neural networks. Then the discrete-time dynamical properties of the single neuron with a recurrent connection are described. Finally, artificial evolution is presented as a tool to develop and optimize neural structures as well as the strength of synapses.

This chapter describes the development of the physical components that lead to the artificial perception-action systems. It begins with the descriptions of different physical sensors which are used to sense environmental information, followed by the details of the walking machines simulated in a physical simulation environment as well as the robots we have built.

Where Chap. 2 investigated the biologically inspired perception-action systems, this chapter focuses on applying the principles of the biological domain to create artificial perception-action systems. First, several preprocessing units of different types of sensory signals are presented. They are used to filter and recognize the corresponding sensory signals and they can be described as perception parts. Second, the neural control of the four- and six-legged walking machines, which generates and controls the locomotion of the machines, is described. Third, the combination of the neural preprocessing and the neural control is explained. It gives rise to the ability of controlling reactive behaviors such as obstacle avoidance and sound tropism. Finally, both behavior controls are merged under a so-called behavior fusion controller by applying a sensor fusion technique to give a versatile perception-action system.

In order to test the capabilities of the artificial perception-action systems, several experiments were carried out. First, the signal processing networks were tested with the simulated signals and the real sensor signals. Afterwards the physical sensors, the neural preprocessing and the neural control were all together implemented on the physical walking machine(s) to demonstrate different reactive behaviors.

This book presents biologically inspired walking machines (four- and six-legged walking machines) interacting with their real environmental stimuli as agent-environment interactions. Different reactive behaviors of animals were investigated for the behavior design of the walking machine(s). On the one hand, the obstacle avoidance behavior, in analogy to the obstacle avoidance and escape behavior of scorpions and cockroaches, was implemented in the walking machines as a negative tropism. On the other hand, the sound tropism which mimics prey capture behavior of spiders is represented as a positive tropism. It was simulated on the four-legged walking machine.