Anticipatory Robot Navigation by Simultaneously Localizing and Building a Cognitive Map

This paper presents a method for a mobile robot to construct and localize relative to a “cognitive map”, where the cognitive map is assumed to be a representational structure that encodes both spatial and behavioral information. The localization is performed by applying a generic Bayes filter. The cognitive map was implemented within a behavior-based robotic system, providing a new behavior that allows the robot to anticipate future events using the cognitive map. One of the prominent advantages of this approach is elimination of the pose sensor usage (e.g., shaft encoder, compass, GPS, etc.), which is known for its limitations and proneness to various errors. A preliminary experiment was conducted in simulation and its promising results are discussed

Anticipatory Robot Control for a Partially Observable Environment Using Episodic Memories

This paper explains an episodic-memory based approach for computing anticipatory robot behavior in a partially observable environment. Inspired by biological findings on the mammalian hippocampus, here, the episodic memories retain a sequence of experienced observation, behavior, and reward. Incorporating multiple machine learning methods, this approach attempts to help reducing the computational burden of the partially observable Markov decision process (POMDP). In particular, the proposed computational reduction techniques include: 1) abstraction of the state space via temporal difference learning; 2) abstraction of the action space by utilizing motor schemata; 3) narrowing down the state space in terms of the goals by employing instance-based learning; 4) elimination of the value-iteration by assuming a unidirectional-linear-chaining formation of the state space; 5) reduction of the state-estimate computation by exploiting the property of the Poisson distribution; and 6) trimming the history length by imposing the cap on the number of episodes that are computed. Furthermore, claims 5) and 6) were empirically verified, and it was confirmed that the state estimation can be in fact computed in an O(n) time (where n is the number of the states), more efficient than a conventional Kalman-filter based approach of O(n2)

Anticipatory Robot Navigation by Simultaneously Localizing and Building a Cognitive Map

This paper presents a method for a mobile robot to construct and localize relative to a "cognitive map", where the cognitive map is assumed to be a representational structure that encodes both spatial and behavioral information. The localization is performed by applying a generic Bayes filter. The cognitive map was implemented within a behavior-based robotic system, providing a new behavior that allows the robot to anticipate future events using the cognitive map. One of the prominent advantages of this approach is elimination of the pose sensor usage (e.g., shaft encoder, compass, GPS, etc.), which is known for its limitations and proneness to various errors. A preliminary experiment was conducted in simulation and its promising results are discussed

Anticipatory Robot Navigation by Simultaneously Localizing and Building a Cognitive Map

This paper presents a method for a mobile robot to construct and localize relative to a “cognitive map”, where the cognitive map is assumed to be a representational structure that encodes both spatial and behavioral information. The localization is performed by applying a generic Bayes filter. The cognitive map was implemented within a behavior-based robotic system, providing a new behavior that allows the robot to anticipate future events using the cognitive map. One of the prominent advantages of this approach is elimination of the pose sensor usage (e.g., shaft encoder, compass, GPS, etc.), which is known for its limitations and proneness to various errors. A preliminary experiment was conducted in simulation and its promising results are discussed. 1

An intelligent multi-floor mobile robot transportation system in life science laboratories

In this dissertation, a new intelligent multi-floor transportation system based on mobile robot is presented to connect the distributed laboratories in multi-floor environment. In the system, new indoor mapping and localization are presented, hybrid path planning is proposed, and an automated doors management system is presented. In addition, a hybrid strategy with innovative floor estimation to handle the elevator operations is implemented. Finally the presented system controls the working processes of the related sub-system. The experiments prove the efficiency of the presented system