Behavior Based Robotic دکتر سعید شیری قیداری  کتاب Behavior Based Robotic Ronald C. Arkin Amirkabir University of Technology Computer Engineering & Information Technology Department روباتیک رفتارگرا

مقدمه Behavior-based roboticists argue that there is much that can be gained for robotics through the study of neuroscience, psychology, and ethology. Neuroscience: the study of the nervous system's anatomy, physiology, biochemistry,and molecular biology. Psychology: the study of mind and behavior Ethology: the study of animal behavior in natural conditions The behavior-based roboticist needs to decide how to use results from these other disciplines. Some scientists attempt to implement these results as closely as possibly, concerning themselves primarily with testing the underlying hypotheses of the biological models in question. Others choose to abstract the underlying details and use these models for inspiration to create more intelligent robots, unconcerned with any impact within the disciplines from which the original models arose. We will see examples of both approaches within this book.

کنترل آگاهانه Many different techniques and approaches for robotic control have been developed. Figme 1.10 depicts a spectrum of current robot control strategies. The left side represents methods that employ deliberative reasoning and the right represents reactive control. A robot employing deliberative reasoning requires relatively complete knowledge about the world and uses this knowledge to predict the outcome of its actions, an ability that enables it to optimize its performance relative to its model of the world. Deliberate reasoning often requires strong assumptions about this world model, primarily that the knowledge upon which reasoning is based is consistent, reliable, and certain. If the information the reasoner uses is inaccurate or has changed since obtained, the outcome of reasoning may err seriously. In a dynamic world, where objects may be moving arbitrarily (e.g., in a battlefield or a crowded corridor), it is potentially dangerous to rely on past information that may no longer be valid. Representational world models are therefore generally constructed from both prior knowledge about the environment and incoming sensor data in support of deliberation.

Deliberative reasoning systems often have several common characteristics: They are hierarchical in structure with a clearly identifiable subdivision of functionality, similar to the organization of commercial businesses or military command. Communication and control occurs in a predictable and predetermined manner, flowing up and down the hierarchy, with little if any lateral movement. Higher levels in the hierarchy provide subgoals for lower subordinate levels. Planning scope, both spatial and temporal, changes during descent in the hierarchy. Time requirements are shorter and spatial considerations are more local at the lower levels. They rely heavily on symbolic representation world models.

Reactive Systems The right side of the spectrum depicted in figure 1.10 represents reactive systems. Simply put, reactive control is a technique for tightly coupling perception and action, typically in the context of motor behaviors, to produce timely robotic response in dynamic and unstructured worlds. We further define the following: An individual behavior: a stimulus/response pair for a given environmental setting that is modulated by attention and detennined by intentio,n. Attention: prioritizes tasks and focuses sensory resources and is detennined by the current environmental context. Intention: determines which set of behaviors should be active based on the robotic agent's internal goals and objectives. Overt or emergent behavior: the global behavior of the robot or organism as a consequence of the interaction of the active individual behavior

سیستم های واکنشی این سیستمها ارتباط مستقیمی بین ادارک و عمل برقرار میکنند بدون اینکه به سابقه و یا ارتباط بین اطلاعات توجهی نمایند. خصوصیات عمده آنها بصورت زیر است: رفتارها عموما ساده بوده و از نوع sensorimotor هستند بدین معنا که