3D virtual haptic cone for intuitive vehicle motion control

Haptic human-machine interfaces and interaction techniques have been shown to offer advantages over conventional approaches. This work introduces the 3D virtual haptic cone with the aim of improving human remote control of a vehicle\u27s motion. The 3D cone introduces a third dimension to the haptic control surface over existing approaches. This approach improves upon existing methods by providing the human operator with an intuitive method for issuing vehicle motion commands whilst simultaneously receiving real-time haptic information from the remote system. The presented approach offers potential across many applications, and as a case study, this work considers the approach in the context of mobile robot motion control. The performance of the approach in providing the operator with improved motion controllability is evaluated and the performance improvement determined.<br /

Haptic control methodologies for telerobotic stair traversal

Teleoperated mobile robots provide the ability for a human operator to safely explore and evaluate hazardous environments. This ability represents an important progression towards the preservation of human safety in the inevitable response to situations such as terrorist activities and urban search and rescue. The benefits of removing physical human presence from such environments are obvious, however challenges inhibiting task performance when remotely operating a mobile robotic system need to be addressed. The removal of physical human presence from the target environment introduces telepresence as a vital consideration in achieving the desired objective. Introducing haptic human-robotic interaction represents one approach towards improving operator performance in such a scenario. Teleoperative stair traversal proves to be a challenging task when undertaking threat response in an urban environment. This article investigates the teleoperation of an articulated track mobile robot designed for traversing stairs in a threat response scenario. Utilising a haptic medium for bilateral human-robotic interaction, the haptic cone methodology is introduced with the aim of providing the operator with a vision-independent, intuitive indication of the current commanded robot velocity. The haptic cone methodology operates synergistically with the introduced fuzzy-haptic augmentation for improving teleoperator performance in the stair traversal scenario.<br /

Fuzzy haptic augmentation for telerobotic stair climbing

Teleoperated robotic systems provide a valuable solution for the exploration of hazardous environments. The ability to explore dangerous environments from the safety of a remote location represents an important progression towards the preservation of human safety in the inevitable response to such a threat. While the benefits of removing physical human presence are clear, challenges associated with remote operation of a robotic system need to be addressed. Removing direct human presence from the robot\u27s operating environment introduces telepresence as an important consideration in achieving the desired objective. The introduction of the haptic modality represents one approach towards improving operator performance subject to reduced telepresence. When operating in an urban environment, teleoperative stair climbing is not an uncommon scenario. This work investigates the operation of an articulated track mobile robot designed for ascending stairs under teleoperative control. In order to assist the teleoperator in improved navigational capabilities, a fuzzy expert system is utilised to provide the teleoperator with intelligent haptic augmentation with the aim of improving task performance. <br /

Soft computing paradigms for learning fuzzy controllers with applications to robotics

Three soft computing paradigms for automated learning in robotic systems are briefly described. The first employs Genetic Programming (GP) to evolve rules for fizzy-behaviors to be used in mobile robot control. The second paradigm develops a two-level hierarchical fuzzy control structure for flexible manipulators. It incorporates Genetic Algorithms (GA) in a learning scheme to adapt to various environmental conditions. The third paradigm concentrates on a methodology that uses a Neural Network (NN) to adapt a fuzzy logic controller (FLC) in manipulator control tasks. Simulation results of fuzzy controllers learned with the aid of these soft computing paradigms are presented.

Hybrid Fuzzy Control Schemes for Robotic Systems

Three hybrid fuzzy control schemes for robotics applications are described. The first scheme concentrates on a control architecture which incorporates fuzzy logic theory into the framework of behavior control for mobile robot navigation. The second scheme develops a two-level hierarchical fuzzy control structure for flexible manipulators. It incorporates Genetic Algorithms (GA) in a learning scheme to adapt to various environmental conditions. The third scheme concentrates on a methodology that uses a Neural Network (NN) to adapt a fuzzy logic controller (FLC) in manipulator trajectory following tasks. 1 Introduction Control of robotic systems poses many challenging problems, particularly in unstructured environments. At present, the majority of robots used in industry operate in structured environments and perform simple repetitive tasks that require only end-effector positioning or motion along fixed paths. However, operations in unstructured environments require robots to perform m..

Towards visual and vocal mimicry recognition in human-human interactions

During face-to-face interpersonal interaction, people have a tendency to mimic each other. People not only mimic postures, mannerisms, moods or emotions, but they also mimic several speech-related behaviors. In this paper we describe how visual and vocal behavioral information expressed between two interlocutors can be used to detect and identify visual and vocal mimicry. We investigate expressions of mimicry and aim to learn more about in which situation and to what extent mimicry occurs. The observable effects of mimicry can be explored by representing and recognizing mimicry using visual and vocal features. In order to automatically analyze how to extract and integrate this behavioral information into a multimodal mimicry detection framework for improving affective computing, this paper addresses the main challenge: mimicry representation in terms of optimal behavioral feature extraction and automatic integration in both audio and video modalities

On Autonomous Systems: From Reflexive, Imperative and Adaptive Intelligence to Autonomous and Cognitive Intelligence

Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and technologies of autonomous systems put emphases on humansystem interactions and humans in-the-loop. This paper explores the intelligence and system foundations of autonomous systems. It focuses on what structural and behavioral properties constitute the intelligence power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A Hierarchical Intelligence Model (HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. A set of properties of system autonomy is formally analyzed towards a wide range of autonomous system applications in computational intelligence and systems engineering. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works