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.
Prior work has explored techniques that guide robots motions via synthesized constraints, i.e., potential fields and virtual fixtures. Potential fields, originally developed by Khatib @cite_23 , guide a user towards or away from a goal or obstacle @cite_17 . Virtual fixtures create virtual barriers or forbidden regions in the task space that provide assistance in protecting sensitive areas. Rosenberg @cite_31 provided early work in the development of virtual fixtures. A number of guidance and or preventative virtual fixture designs were compared using a Fitts' law paradigm for a remote teleoperation peg placement task. Later work @cite_13 has developed on-the-fly forbidden region generation techniques using real-time sensor data.
@cite_31 @cite_13 @cite_23 @cite_17
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Forbidden region virtual fixtures protect objects from unwanted contact with a robot. In this paper, we propose a method for creating forbidden region haptic virtual fixtures for teleoperation from streaming point clouds obtained by an RGB-D camera. Upon violating the protected area, the operator receives force feedback that opposes motion inside the forbidden region. Three architectures for creating virtual fixtures are presented and their advantages and disadvantages are described. The proposed methods have the ability to implement constraints and can handle dynamic environments in real-time. The effectiveness of the methods is demonstrated in experiments with a surgical robot. Potential-field-based control strategy for path planning and formation control of multi Quadrotor systems is proposed in this work. The potential field is used to attract the Quadrotor to the goal location as well as avoiding the obstacle. The algorithm to solve the so called local minima problem by utilizing the wall-following behavior is also proposed. The resulted path planning via potential function strategy is then used to design formation control algorithm. Using the virtual leader approach, the formation control strategy by means of potential function is formulated. Each Quadrotor is assigned attractive potential to reach its desired position in formation. Each Quadrotor is also treated as moving obstacle which induces repulsive potential to the others in order to prevent inter-robot collision. The desired position for each Quadrotor in certain configuration is calculated based on the virtual leaderâ��s current position. To get smooth movement, the virtual leader is set as unicycle robot. The repulsive potential is also attached to each agent in case it moves in the region inside the obstacleâ��s influence. The overall strategy has been successfully applied to the Quadrotorâ��s model of Parrot AR Drone 2.0 in Gazebo simulator programmed using Robot Operating System. This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the artificial potential field concept. Collision avoidance, tradi tionally considered a high level planning problem, can be effectively distributed between different levels of control, al lowing real-time robot operations in a complex environment. This method has been extended to moving obstacles by using a time-varying artificial patential field. We have applied this obstacle avoidance scheme to robot arm mechanisms and have used a new approach to the general problem of real-time manipulator control. We reformulated the manipulator con trol problem as direct control of manipulator motion in oper ational space—the space in which the task is originally described—rather than as control of the task's corresponding joint space motion obtained only after geometric and kine matic transformation. Outside the obstacles' regions of influ ence, we caused the end effector to move in a straight line with an... Robots in a human environment need to be compliant. This compliance requires that a preplanned movement can be adapted to an obstacle that may be moving or appearing unexpectedly. Here, we present a general framework for movement generation and mid-flight adaptation to obstacles. For robust motion generation, developed the framework of dynamic movement primitives which represent a demonstrated movement with a set of differential equations. These equations allow adding a perturbing force without sacrificing stability of the desired movement. We extend this framework such that arbitrary movements in end-effector space can be represented - which was not possible before. Furthermore, we include obstacle avoidance by adding to the equations of motion a repellent force - a gradient of a potential field centered around the obstacle. In addition, this article compares different potential fields and shows how to avoid obstacle-link collisions within this framework. We demonstrate the abilities of our approach in simulations and with an anthropomorphic robot arm.