Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An Behavior-based Robotics
A hierarchical architecture for behavior-based robots
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Making reinforcement learning work on real robots
Making reinforcement learning work on real robots
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This paper presents a technique for an intelligent robot to adaptively behave in unforeseen and dynamic circumstances. Since the traditional methods utilized the relatively reliable information about the environment to control intelligent robots, they were robust but could not behave adaptively in complex and dynamic world. On the contrary, behavior-based approach is suitable for generating autonomous behaviors in the environment, but it still lacks of the capabilities to infer dynamic situations for high-level behaviors. This paper proposes a 2-layer control architecture to generate adaptive behaviors, which perceive and avoid dynamic moving obstacles as well as static obstacles. The first level is to generate reflexive and autonomous behaviors with the behavior network, and the second level is to infer dynamic situation of mobile robots with Bayesian network. Experimental results with various situations have shown that the robot reaches the goal points while avoiding static or moving obstacles with the proposed architecture.