Modeling Hydra Behavior Using Methods Founded in Behavior-Based Robotics

Chapter 4Behavior-based roboticsAs mentioned in Chapter 1, BBR (in contrast with classical AI) takes a bottom-up approachto robotic behavior, and the complete robotic brain is built from a repertoire ofsimple behaviors. It should be mentioned that, more recently, hybrid architectures 1 havebecome increasingly common [48], thus filling the gap between BBR and AI somewhat.This chapter provides a brief introduction to the framework of BBR.The main characteristics of behavior-based systems are low-level intelligence, i.e. atight connection between the robot’s sensors and its actuators, and decomposition of therobot’s overall behavior into individual behaviors, each of which represents a distinctfunctional unit [1]. The generation and, in particular, organization of behaviors constitutevast field of studies, and many methods have been suggested by researchers in the field. Itshould be noted that methods for the generation and organization of behaviors apply notonly to architectures in BBR, but also to most hybrid architectures [48]. More informationconcerning generation and organization of behaviors in BBR can be found e.g. in [1, 47,67, 71]. For a review of current methods used for behavior coordination, see e.g. [49].Generation and coordination of robot behaviors can be accomplished by means ofhand-coding and traditional engineering techniques, or by means of artificial evolution,in which evolutionary algorithms (EAs) [25] are used as an optimization method. Thelatter case forms the field of evolutionary robotics (ER) [42]. A brief introduction toEAs is provided in Appendix A.4.1 Generation of robot behaviorsAs previously mentioned, in BBR relatively simple behaviors constitute the buildingblocks of the robotic brain. Several architectures for behaviors exist, e.g. if-then-else-1 Combining reactive behaviors from BBR, and deliberative plans from traditional AI.13