Force-Sensor-Less Bilateral Teleoperation Control of Dissimilar Master-Slave System With Arbitrary Scaling

This study designs a high-precision bilateral teleoperation control for a dissimilar master-slave system. The proposed nonlinear control design takes advantage of a novel subsystem-dynamics-based control method that allows designing of individual (decentralized) model-based controllers for the manipulators locally at the subsystem level. Very importantly, a dynamic model of the human operator is incorporated into the control of the master manipulator. The individual controllers for the dissimilar master and slave manipulators are connected in a specific communication channel for the bilateral teleoperation to function. Stability of the overall control design is rigorously guaranteed with arbitrary time delays. Novel features of this study include the completely force-sensor-less design for the teleoperation system with a solution for a uniquely introduced computational algebraic loop, a method of estimating the exogenous operating force of an operator and the use of a commercial haptic manipulator. Most importantly, we conduct experiments on a dissimilar system in two degrees of freedom (DOFs). As an illustration of the performance of the proposed system, a force scaling factor of up to 800 and position scaling factor of up to 4 was used in the experiments. The experimental results show an exceptional tracking performance, verifying the real-world performance of the proposed concept.publishedVersionPeer reviewe

Teleoperación [de robots]: técnicas, aplicaciones, entorno sensorial y teleoperación inteligente

En este trabajo centraremos la atención en los sistemas robóticos teleoperados, especialmente analizaremos los sistemas teleoperados desde internet, veremos una clasificación de las metodologías de teleoperación, los diferentes sistemas de control y daremos una visión del estado del arte en este ámbito de conocimiento

Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery

This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator. These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator. Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction. A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time

Haptic communication for remote mobile and manipulator robot operations in hazardous environments

Nuclear decommissioning involves the use of remotely deployed mobile vehicles and manipulators controlled via teleoperation systems. Manipulators are used for tooling and sorting tasks, and mobile vehicles are used to locate a manipulator near to the area that it is to be operated upon and also to carry a camera into a remote area for monitoring and assessment purposes. Teleoperations in hazardous environments are often hampered by a lack of visual information. Direct line of sight is often only available through small, thick windows, which often become discoloured and less transparent over time. Ideal camera locations are generally not possible, which can lead to areas of the cell not being visible, or at least difficult to see. Damage to the mobile, manipulator, tool or environment can be very expensive and dangerous. Despite the advances in the recent years of autonomous systems, the nuclear industry prefers generally to ensure that there is a human in the loop. This is due to the safety critical nature of the industry. Haptic interfaces provide a means of allowing an operator to control aspects of a task that would be difficult or impossible to control with impoverished visual feedback alone. Manipulator endeffector force control and mobile vehicle collision avoidance are examples of such tasks. Haptic communication has been integrated with both a Schilling Titan II manipulator teleoperation system and Cybermotion K2A mobile vehicle teleoperation system. The manipulator research was carried out using a real manipulator whereas the mobile research was carried out in simulation. Novel haptic communication generation algorithms have been developed. Experiments have been conducted using both the mobile and the manipulator to assess the performance gains offered by haptic communication. The results of the mobile vehicle experiments show that haptic feedback offered performance improvements in systems where the operator is solely responsible for control of the vehicle. However in systems where the operator is assisted by semi autonomous behaviour that can perform obstacle avoidance, the advantages of haptic feedback were more subtle. The results from the manipulator experiments served to support the results from the mobile vehicle experiments since they also show that haptic feedback does not always improve operator performance. Instead, performance gains rely heavily on the nature of the task, other system feedback channels and operator assistance features. The tasks performed with the manipulator were peg insertion, grinding and drilling.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Haptic communication for remote mobile and manipulator robot operations in hazardous environments

Nuclear decommissioning involves the use of remotely deployed mobile vehiclesand manipulators controlled via teleoperation systems. Manipulators are used fortooling and sorting tasks, and mobile vehicles are used to locate a manipulatornear to the area that it is to be operated upon and also to carry a camera into aremote area for monitoring and assessment purposes.Teleoperations in hazardous environments are often hampered by a lack of visualinformation. Direct line of sight is often only available through small, thickwindows, which often become discoloured and less transparent over time. Idealcamera locations are generally not possible, which can lead to areas of the cell notbeing visible, or at least difficult to see. Damage to the mobile, manipulator, toolor environment can be very expensive and dangerous.Despite the advances in the recent years of autonomous systems, the nuclearindustry prefers generally to ensure that there is a human in the loop. This is dueto the safety critical nature of the industry. Haptic interfaces provide a meansof allowing an operator to control aspects of a task that would be difficult orimpossible to control with impoverished visual feedback alone. Manipulator endeffectorforce control and mobile vehicle collision avoidance are examples of suchtasks.Haptic communication has been integrated with both a Schilling Titan II manipulatorteleoperation system and Cybermotion K2A mobile vehicle teleoperationsystem. The manipulator research was carried out using a real manipulatorwhereas the mobile research was carried out in simulation. Novel haptic communicationgeneration algorithms have been developed. Experiments have beenconducted using both the mobile and the manipulator to assess the performancegains offered by haptic communication.The results of the mobile vehicle experiments show that haptic feedback offeredperformance improvements in systems where the operator is solely responsible forcontrol of the vehicle. However in systems where the operator is assisted by semiautonomous behaviour that can perform obstacle avoidance, the advantages ofhaptic feedback were more subtle.The results from the manipulator experiments served to support the results fromthe mobile vehicle experiments since they also show that haptic feedback does notalways improve operator performance. Instead, performance gains rely heavily onthe nature of the task, other system feedback channels and operator assistancefeatures. The tasks performed with the manipulator were peg insertion, grindingand drilling

Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery

This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator. These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator. Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction. A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time