Fuzzy behavior hierarchies for multi-robot control

Abstract

Hierarchical approaches and methodologies are commonly used for control system design and synthesis. Well-known model-based techniques are often applied to solve problems of complex and large-scale control systems. The general philosophy of decomposing control problems into modular and more manageable subsystem control problems applies equally to the growing domain of intelligent and autonomous systems. However, for this class of systems, new techniques for subsystem coordination and overall system control are often required. This article presents an approach to hierarchical control design and synthesis for the case where the collection of subsystems is comprised of fuzzy logic controllers and fuzzy knowledge-based decision systems. The approach is used to implement hierarchical behavior-based controllers for autonomous navigation of one or more mobile robots. Theoretical details of the approach are presented, followed by discussions of practical design and implementation issues. Example implementations realized on various physical mobile robots are described to demonstrate how the techniques may be applied in practical applications involving homogeneous and heterogeneous robot teams.

Robot coordination using task-priority and sliding-mode techniques

“…In that case, more sophisticated solutions in the literature for robot coordination [17,18,19] would be needed.…”

“…In this case, (19) will alternatively contain a row with different sign in L g φ i . This fact can be interpreted as adding conditions (19) for the (always active) equality constraints.…”

“…In this case, (19) will alternatively contain a row with different sign in L g φ i . This fact can be interpreted as adding conditions (19) for the (always active) equality constraints. This is well-known in conventional sliding-mode control literature to which the reader is referred for further details [10].…”

“…The objective of this work is generalizing the above ideas to deal with MRSs as well as incorporating constraint prioritization situations. Other ways to tackle constraint-satisfaction and coordination problems in robotic systems are artificial intelligence [17], neural networks [18], fuzzy logic [19], genetic algorithms [20], etc.…”

“…The proposed method only requires a few program lines, has a short computation time and simplifies the user interface since the method directly deals with the fulfillment of the constraints specified by the robot end-user. However, since the proposed approach does not include high-level planning, other more sophisticated solutions in the literature for robot coordination [17,18,19] could lead to a better performance in certain cases, e.g., when facing trap situations [12], at the expense, however, of a much higher computational cost.…”