Teleoperation of heavy machinery in industry often requires operators to be in close proximity to the plant and issue commands on a per-actuator level using joystick input devices. However, this is non-intuitive and makes achieving desired job properties a challenging task requiring operators to complete extensive and costly training. Despite this, operator fatigue is common with implications for personal safety, project timeliness, cost, and quality. While full automation is not yet achievable due to unpredictability and the dynamic nature of the environment and task, shared control paradigms allow operators to issue high-level commands in an intuitive, task-informed control space while having the robot optimize for achieving desired job properties. In this paper, we compare a number of modes of teleoperation, exploring both the number of dimensions of the control input as well as the most intuitive control spaces. Our experimental evaluations of the performance metrics were based on quantifying the difficulty of tasks based on the well known Fitts' law as well as a measure of how well constraints affecting the task performance were met. Our experiments show that higher performance is achieved when humans submit commands in low-dimensional task spaces as opposed to joint space manipulations.
From the concrete spraying literature, a task space control framework was developed by Honegger and Codourey @cite_18 based on the inverse-kinematics method. The system allows the operator to control the end-effector of the concrete spraying unit using a 6-DoF space mouse. Later work by Girmscheid and Moser @cite_12 developed a sliding autonomy approach for a high-DoF concrete spraying unit. They define three levels of autonomy: (1) A manual mode, consisting of no autonomy and implementing common joint-level control, (2) A semi-automated mode that, using a pre-collected laser scan of a tunnel cavity and computed geometry, allows the operator to command only the position on the wall to spray whilst the system accounts for motion constraints, and (3) An automated mode that plans and executes an entire trajectory. The latter mode, they note, is only in development for tunnel-boring machine projects where the excavated tunnel surface is regular and very smooth. Modes of teleoperation compared in this work are inspired by these control techniques developed for concrete spraying applications.
@cite_18 @cite_12
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Optimized shotcrete application techniques are required in particular tunneling projects. Today, any spraying can be done by hand or by manipulator. With the development in the material technologies, the range of possible operation was enlarged. With automation of the application, an important contribution to improve performance and quality and to reduce rebound may be achieved. At the Swiss Federal Institute of Technology, Zurich, systematic research is done to develop the fully automated process control, focusing on the wet shotcrete method. With the development of the fully automated shotcrete robot, the user will have a very effective tool at his or her disposal to spray concrete shells (fully automated). With the new robot, the user may choose from three different modes: manual, semiautomated, and fully automated spraying. Especially the fully automated mode facilitates higher performance with less danger to the worker's health. Quality control is inherent in the application process in regard to layer thickness, compaction, and homogeneity. In tunneling and mining construction work, heavy and large manipulators are used to spray liquid concrete on the walls. These manipulators are usually operated manually with simple units allowing control of all actuators independently. In cooperation between industry and university, a novel control system has been developed for the redundant heavy manipulator Robojet(R) that supports the operator by calculating the inverse kinematic model and controlling all actuators automatically. The paper presents the solution of the redundant inverse kinematics of the Robojet using the null-space method and its implementation in a control system. We present an approach to control a 6-degree-of-freedom (DOF) manipulator using an uncalibrated visual servoing (VS) approach that addresses the challenges of choosing proper image features for target objects and designing a VS controller to enhance the tracking performance. The main contribution of this paper is the definition of a new virtual visual space (image space). A novel stereo camera model employing virtual orthogonal cameras is used to map 6-D poses from Cartesian space to this virtual visual space . Each component of the 6-D pose vector defined in this virtual visual space is linearly independent, leading to a full-rank @math image Jacobian matrix , which allows avoiding classical problems, such as image space singularities and local minima. Furthermore, the control for rotational and translational motion of robot is decoupled due to the diagonal image Jacobian. Finally, simulation results with an eye-to-hand robotic system confirm the improvement in controller stability and motion performance with respect to conventional VS approaches. Experimental results on a 6-DOF industrial robot are provided to illustrate the effectiveness of the proposed method and the feasibility of using this method in practical scenarios. This article describes the use of predictive control for the decentralized cooperative control of unmanned aerial vehicles in an unfamiliar three-dimensional environment. It is assumed that each vehicle is equipped with an autopilot and a trajectory control unit. The autopilot insures the stability of the vehicle. The setpoints of the autopilot are calculated by the trajectory control unit, thus forming a cascade control structure. The trajectory control unit relies on a predictive control algorithm to calculate the optimal commands (autopilot setpoints) such that the vehicle will reach fixed targets at known positions while avoiding static obstacles that are detected en route. The advantage in using predictive control is that it offers great flexibility in the objective function to optimize while respecting constraints such as command limits, limits on the displacement that a vehicle can carry out, and the constraints that allow obstacle avoidance. The principle proposed in order to avoid static obstacles (that are assumed ellipsoid) is to verify that the predicted vehicle trajectory does not intersect these obstacles. With the objective to increase performance, cooperation between vehicles must also be privileged. Thus, if some vehicles are within the communication range, they can share the position and shape of the obstacles they have detected. Simulations illustrate the method and higlight the benefits of cooperation.