Robot Navigation in Unseen Spaces using an Abstract Map

Human navigation in built environments depends on symbolic spatial information which has unrealised potential to enhance robot navigation capabilities. Information sources such as labels, signs, maps, planners, spoken directions, and navigational gestures communicate a wealth of spatial information to the navigators of built environments; a wealth of information that robots typically ignore. We present a robot navigation system that uses the same symbolic spatial information employed by humans to purposefully navigate in unseen built environments with a level of performance comparable to humans. The navigation system uses a novel data structure called the abstract map to imagine malleable spatial models for unseen spaces from spatial symbols. Sensorimotor perceptions from a robot are then employed to provide purposeful navigation to symbolic goal locations in the unseen environment. We show how a dynamic system can be used to create malleable spatial models for the abstract map, and provide an open source implementation to encourage future work in the area of symbolic navigation. Symbolic navigation performance of humans and a robot is evaluated in a real-world built environment. The paper concludes with a qualitative analysis of human navigation strategies, providing further insights into how the symbolic navigation capabilities of robots in unseen built environments can be improved in the future.Comment: 15 pages, published in IEEE Transactions on Cognitive and Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see https://btalb.github.io/abstract_map/ for access to softwar

A reactive approach to classifier systems

IEEE International Conference on Systems, Man, and Cybernetics. San Diego, CA, 11-14 Oct. 1998The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and/or sequences of actions. Classifier Systems (CS) have proven their ability of continuous learning, however they have some problems in reactive systems. A modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminating between rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations

Applying classifier systems to learn the reactions in mobile robots

The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier systems (CSs) have proven their ability of continuous learning, however, they have some problems in reactive systems. A modified CS, namely a reactive classifier system (RCS), is proposed to overcome those problems. Two special mechanisms are included in the RCS: the non-existence of internal cycles inside the CS (no internal cycles) and the fusion of environmental message with the messages posted to the message list in the previous instant (generation list through fusion). These mechanisms allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminate between rules and, second, the discovery of new rules to obtain a successful operation in dynamic environments. DiVerent experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations.Publicad

An enhanced classifier system for autonomous robot navigation in dynamic environments

In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad

Comics, robots, fashion and programming: outlining the concept of actDresses

This paper concerns the design of physical languages for controlling and programming robotic consumer products. For this purpose we explore basic theories of semiotics represented in the two separate fields of comics and fashion, and how these could be used as resources in the development of new physical languages. Based on these theories, the design concept of actDresses is defined, and supplemented by three example scenarios of how the concept can be used for controlling, programming, and predicting the behaviour of robotic systems

Towards an Architecture for Semiautonomous Robot Telecontrol Systems.

The design and development of a computational system to support robot–operator collaboration is a challenging task, not only because of the overall system complexity, but furthermore because of the involvement of different technical and scientific disciplines, namely, Software Engineering, Psychology and Artificial Intelligence, among others. In our opinion the approach generally used to face this type of project is based on system architectures inherited from the development of autonomous robots and therefore fails to incorporate explicitly the role of the operator, i.e. these architectures lack a view that help the operator to see him/herself as an integral part of the system. The goal of this paper is to provide a human-centered paradigm that makes it possible to create this kind of view of the system architecture. This architectural description includes the definition of the role of operator and autonomous behaviour of the robot, it identifies the shared knowledge, and it helps the operator to see the robot as an intentional being as himself/herself