Real-Time Fuzzy Logic-based Hybrid Robot Path-Planning Strategies for a Dynamic Environment

Real-Time Fuzzy Logic-based Hybrid Robot Path-Planning Strategies for a Dynamic Environment

Napoleon H. Reyes (Massey University, New Zealand), Andre L.C. Barczak (Massey University, New Zealand), Teo Susnjak (Massey University, New Zealand), Peter Sincák (Technical University of Košice, Slovakia) and Ján Vašcák (Technical University of Košice, Slovakia)
Copyright: © 2013 |Pages: 27
DOI: 10.4018/978-1-4666-3942-3.ch006
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This chapter sets out to explore the intricacies behind developing a hybrid system for real-time autonomous robot navigation, with target pursuit and obstacle avoidance behaviour, in a dynamic environment. Three complete systems are described, namely, a cascade of four fuzzy systems, a hybrid fuzzy A* system, and a hybrid fuzzy A* with a Voronoi diagram. A highly reconfigurable integration architecture is presented, allowing for the harmonious interplay between the different component algorithms, with the option of engaging or disengaging from the system. The utilization of both global and local information about the environment is examined, as well as an additional optimal global path-planning layer. Moreover, how a fuzzy system design approach could take advantage of the presence of symmetry in the input space, cutting down the number of rules and membership functions, without sacrificing control precision is illustrated. The efficiency of all the algorithms is demonstrated by employing them in a simulation of a real-world system: the robot soccer game. Results indicate that the hybrid system can generate smooth, near-shortest paths, as well as near-shortest-safest paths, when all component algorithms are activated. A systematic approach to calibrating the system is also provided.
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Path planning algorithms in the context of robot soccer need to deal with two important problems, among many others: target pursuit and obstacle avoidance. In this context, the environment is well-known (e.g., the limits of the field), and any changes in the inputs are also well constrained. The targets can be the ball, or the goal, or a point within the field where the robot has to be strategically positioned. Changes are updated by the camera on top of the field, and a relatively simple computer vision algorithm keeps track of the positions (including angles) of the ball and the robots. The challenge is to find an optimal (or at least near-optimal one that does not get the robot stuck in corners and partial enclosures) path to arrive as fast as possible on the target coordinates, while avoiding other robots. The scope of this chapter is confined to near-optimal, path-planning and reactionary algorithms that utilize some local or global information about the space of traversal.

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