This paper presents a computational model for side-by-side walking for human-robot interaction (HRI). In this work we address the importance of future motion utility (motion anticipation) of the two walking partners.

This paper presents a novel behavior-based approach for mobile robot control using Fuzzy Discrete Event System (FDES). The method exploits the multi-valued feature of Fuzzy Logic (FL) and event-driven property of Discrete Event System (DES) to define activity of each behavior using fuzzy state vectors. State-based prediction of activity is accomplished using fuzzily defined event matrices. The method combines aspects of both command fusion and behavior arbitration. Furthermore, the proposed approach has the ability to define state-based observability and controllability to handle sensor uncertainty and environment dynamics. Observability describes decision vagueness associated with sensory data, whereas controllability specifies undesirable state-reach within the observed environment. The present work employs observability and controllability to modify the velocity commands and the sampling frequency depending on decision vagueness and undesirable state-reach. Real-time results of FDES-based mobile robot navigation are presented to validate the performance of the proposed method.

This paper describes a novel behavior-based approach for an object-pulling robot using Fuzzy Discrete Event System (FDES). The object-pulling task is a variant of objectpushing operation, where the robot anchors itself with an object and then navigates to a target location. The proposed behavior-based approach incorporates global and local motion planning for navigation. The global motion planning uses strategic behaviors that employ prior knowledge of the environment to generate a safe-path from the initial position of the robot to the end-point (goal), whereas the local motion planning uses reactive behaviors that deploy locally sensed sensory data for target-object detection and dynamic obstacle avoidance. The major contribution of this paper is the novel FDES-based behavior selection (or modulation) method that eliminates the conventional binary thresholding-based boolean event generation in Discrete Event System (DES) for behavior activation. The multi-valued logic approach of FDES reduces the possibility of wrong event generation, which in turn decreases the possibility of inappropriate behavior selection. Experimental results using a physical robot are also presented to authenticate the feasibility of the proposed FDES-based object-pulling operation.

Abstract: This paper presents a novel intelligent sensory information processing technique using a fuzzy discrete event system (FDES) for robotic control. The proposed method combines the predictive control approach of a discrete event system with the approximate reasoning aspect of fuzzy logic. It develops a supervisory control strategy for behavior‐based robotic control using distributed FDES. The application of distributed FDES eliminates the formation of complex fuzzy predicates and a large fuzzy rule‐base. The FDES‐based approach also provides means for analyzing behavior‐based decision‐making using the observability and controllability of an FDES. The observability of an FDES describes uncertainties in sensory data, and the controllability of an FDES exploits uncertain state transitions in a dynamic environment. Comprehensive experiments on behavior‐based mobile robot navigation are presented to authenticate the performance of the proposed methodology.

This paper presents a novel behavior-modulation technique using a fuzzy discrete event system (FDES) for behavior-based robotic control. The method exploits the multivalued feature of fuzzy logic (FL) and event-driven property of a discrete event system (DES) to generate the activity of a behavior using fuzzy state vectors. State-based prediction of an activity is accomplished using fuzzily defined event matrices. A central arbiter employs priority-based arbitration among the activity state vectors and generates new event matrices to modify the activity states of the behaviors. The method combines aspects of both command fusion and behavior arbitration. Furthermore, the proposed approach has the ability to define state-based observability and controllability to handle sensory uncertainty and environmental dynamics. Observability describes decision vagueness associated with sensory data, whereas controllability specifies undesirable state-reach within the observed environment. Real-time results of FDES-based mobile robot navigation are presented and compared against four different modulation methods to validate its superior performance

This paper presents a novel behavior modulation technique using fuzzy discrete event system (FDES) for mobile robots. The algorithm exploits the multi-valued feature of fuzzy logic (FL) and event-driven property of discrete event system (DES). Desirability (activity) of a behavior is expressed in terms of a fuzzy state vector. State-based prediction of the desirability is accomplished using fuzzy event matrices. A central arbiter employs priority based arbitration among the desirability state vectors and recommends new event matrices to modify the desirability states of the behaviors. The algorithm combines aspects of both command fusion and behavior arbitration. Furthermore, the proposed approach constitutes state-based observability and controllability to cope with the uncertainties of sensory data and dynamic environment. Simulation results of FDES-based mobile robot navigation are also presented to validate the performance of the proposed algorithm.

This paper presents a novel approach for integrating global and local path planning of indoor mobile robots using motor schema based behavior coordination with fuzzy logic based adaptive behavioral modulation. First, Voronoi diagram approach is exploited to determine the global safe-path. Then A* search algorithm is employed to identify the Voronoi vertices (junctions or nodes) along the optimal path between the starting point of the robot and target position. Locally located junctions then act as subgoals for local motion planning. Motor schema based behavior fusion generates the safe direction of motion. Subgoals and points on the safe path exhibit attractive forces whereas obstacles cause repulsive forces. A supervisory fuzzy module is developed to incorporate adaptive behavioral capabilities which exploit the weighted decision-making based on contextual behavior composition. It dynamically produces the force coefficients for context dependent modulation of the virtual forces to generate local-minima free trajectory without oscillation. Adapted vector field histogram is deployed in order to modify the safe path in the presence of unknown obstacles. Simulation of indoor navigation examples validates the performance of the algorithm.