Unified Behavior Framework for Reactive Robot Control

Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of the existing reactive controllers. Additionally, the modular design of the behavior framework: (1) simplifies development and testing; (2) promotes the reuse of code; (3) supports designs that scale easily into large hierarchies while restricting code complexity; and (4) allows the behavior based system developer the freedom to use the behavior system they feel will function the best. When a hybrid or three layer control architecture includes the unified behavior framework, a common interface is shared by all behaviors, leaving the higher order planning and sequencing elements free to interchange behaviors during execution to achieve high level goals and plans. The framework\u27s ability to compose structures from independent elements encourages experimentation and reuse while isolating the scope of troubleshooting to the behavior composition. The ability to use elemental components to build and evaluate behavior structures is demonstrated using the Robocode simulation environment. Additionally, the ability of a reactive controller to change its active behavior during execution is shown in a goal seeking robot implementation

Behavior-based robot control using fuzzy discrete event system

The key issue associated with behavior-based architecture is the development of a behavior coordinator. A behavior coordinator should possess a number of important properties. The literature suggests that (a) both behavior arbitration and command fusion techniques should be combined in order to coordinate competitive and cooperative behaviors, (b) the coordinator should possess adequate means of modeling the current state of the world, (c) sensory uncertainties should be modeled using multivalued logic, (d) the coordinator should be capable of making persistent behavior selection, (e) it should provide the opportunity to accommodate hierarchical decision-making for reactive action generation, (f) it should possess predictive decision-making capability for handling future environmental uncertainties, (g) it should provide satisfactory means of decision analysis, and finally (h) it should be modular to achieve robustness with a larger number of behaviors. This thesis attempts to address some of the issues mentioned above while exploiting fuzzy logic (FL) based methodologies for behavior coordination. -- The initial investigation includes experimentation of different methodologies in the literature. A FL-based controller is designed for motor schema based mobile robot navigation. Fuzzy meta rules have been used to adaptively generate weights for each motor schema. It uses human reasoning to model the deterministic uncertainty of sensory data, which in turn helps to reduce the possibility of generating incorrect weights for motor schemas. Experimental results demonstrate that the fuzzy logic based approach overcomes some of the common problems of schema-based navigation. It has been observed that a pure FL-based method has several disadvantages, such as it imposes a scalability problem when the system consists of both competitive and cooperative behaviors and it does not provide any feedback measures for decision analysis. Moreover, the rule-based knowledge representation becomes complex in cases where previous state information is incorporated for persistent decision-making. -- While understanding the shortcomings of each technique, this thesis present a novel behavior coordination architecture using Fuzzy Discrete Event System (FDES). This architecture addresses the shortcomings of the FL-based technique using complimentary properties of Discrete Event System (DES). The DES-based method provides supervisory control techniques for behavior arbitration and has a suitable framework for decision analysis in terms of observability and controllability. Furthermore, it supports multi-level behavioral decomposition and uses previous state information of the system for persistent decision-making. As a result, the combination of FL and DES provides the opportunity to integrate several key features to achieve a high performance behavior coordinator. -- Finally the proposed architecture is experimentally tested using three different robotic applications, namely robotic navigation, robotic box pulling and robotic visual attention. The navigational experiments demonstrate that the performance measures of the FDES-based system are unaffected even under changing or complex environments. The FDES-based system is able to produce oscillation-free and collision-less navigation in the experiments. The FDES based concept is then extended to robotic object-pulling operation. These experiments demonstrate the multi-level behavioral decomposition feature of the proposed architecture. The object-pulling task is divided into anchoring and navigation subtasks. The robot, first, anchors the object and then navigates to the target location. The experimental results demonstrate that the FDES-based approach provides reliable execution of anchoring task and produces collision-free navigation to the target location. Finally, the FDES-based application is implemented to model visual attention system of a robot. This method integrates bottom-up bias (sensory feedback) with top-down influence (previous experience of the robot) to control pan-tilt motion of a camera mounted on a robot. The FDES based system is able to prevent abrupt changes in focus of attention and produces smooth transition in motion commands when visual attention changes between the objects. -- Overall the proposed FDES-based approach outperforms several other common techniques due to its key characteristics, such as combination of arbitration and command fusion, FL-based world state modeling, persistent decision-making, multilevel behavioral decomposition, decision analysis capability, and modularity of the system