Improving Robotic Teleoperation through Human-Robot Interaction and Robotic Autonomy

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Teleoperation's overall goal is to give the operator situational awareness (i.e., a sense of being there) of the remote environment and the ability to interact conveniently. Operators of telemanipulators in nuclear plants perform inspections and maintenance tasks, including complex manipulations. Several studies reveal that the unstructured nature of the environment, task complexity, and the conservative nature of the nuclear industry cause teleoperators to prefer to be in control and fully aware of the multiple systems' current and future behaviours at all times, limiting the deployment autonomous teleoperation or robotics. As a result, the operators of these mechanical systems interact with several mediums, such as various displays, robotic telemanipulation systems, information systems, and other colleagues, at once. These interactions impose overwhelming levels of information processing on the operator, leading to a high cognitive workload, which impacts performance and safety in teleoperation.This PhD research addresses the automation challenge in teleoperation, focusing on enhancing operator performance and safety. It acknowledges the preference for operator control in complex, unstructured environments, hindering the deployment of autonomous systems. It proposes integrating adaptive robotic assistance and responsive interfaces that dynamically respond to an operator's mental workload (MWL) demands, task complexity and structure. The following objectives will be met to achieve this aim:To identify the objective performance and mental workload metrics for teleoperation.To determine the sources of physical and mental workload using task analysis.To develop methods for assessment of performance and mental and physical workloads in teleoperation.To design robotic assistance schemes based on physical and mental demands to guide operators through teleoperation task completions.Several pieces of literature have demonstrated how haptic guidance based on the sense of touch can be deployed for robotic assistance. For example, haptic shared control and haptic traded control, which the operators and the system share and take turns in the control task, respectively. This work will explore how these assistance schemes could be deployed to respond to an operator's physical and cognitive demands within structured environments for repetitive and predictable tasks. It will combine some physiological, subjective and performance measures to give an improved assessment and evaluation of the operator's mental workload to understand its relationships with teleoperation tasks. With these improved systems, operators can perform their work more conveniently, faster, and accurately, increasing safety and reducing operating costs.