Toward an Intuitive and Iterative 6D Virtual Guide Programming Framework for Assisted Human–Robot Comanipulation

In human–robot comanipulation, virtual guides are an important tool used to assist the human worker as they constrain the movement of the robot to improve the task accuracy and to avoid undesirable effects, such as collisions with the environment. Consequently, the physical effort and cognitive overload are reduced during accomplishment of comanipulative tasks. However, the construction of virtual guides often requires expert knowledge and modeling of the task, which restricts the usefulness of virtual guides to scenarios with fixed constraints. Moreover, few approaches have addressed the implementation of virtual guides enforcing orientation constraints and, when done, these approaches have treated translation and orientation separately, and consequently there is no synchronization of the translational and rotational motions. To overcome these challenges and enhance the programming flexibility of virtual guides, we present a new framework that allows the user to create 6D virtual guides through XSplines which we define as a combination of Akima splines for the translation component and spherical cubic interpolation of quaternions for the orientation component. For complex tasks, the user is able to initially define a 3D virtual guide and then use this assistance in translational motion to concentrate only on defining the orientations along the path. It is also possible for the user to modify a particular point or portion of a guide while being assisted by it. We demonstrate in an industrial scenario that these innovations provide an intuitive solution to extend the use of virtual guides to 6 degrees of freedom and increase the human worker’s comfort during the programming phase of these guides in an assisted human–robot comanipulation context.

Effect of Grip Force and Training in Unstable Dynamics on Micromanipulation Accuracy

This paper investigates whether haptic error amplification using unstable dynamics can be used to train accuracy in micromanipulation. A preliminary experiment first examines the possible confounds of visual magnification and grip force. Results show that micromanipulation precision is not affected by grip force in both naive and experienced subjects. On the other hand, precision is increased by visual magnification of up to 10×, but not further for larger magnifications. The main experiment required subjects to perform small-range point-to-point movements in 3D space in an unstable environment which amplified position errors to the straight line between start and end point. After having trained in this environment, subjects performing in the free conditions show an increase in success rate and a decrease in error and its standard deviation relative to the control subjects. This suggests that this technique can improve accuracy and reliability of movements during micromanipulation.

Collaborative path planning for a robotic wheelchair

Generating a path to guide a wheelchair's motion faces two challenges. First, the path is located in the human environment and that is usually unstructured and dynamic. Thus, it is difficult to generate a reliable map and plan paths on it by artificial intelligence. Second, the wheelchair, whose task is to carry a human user, should move on a smooth and comfortable path adapted to the user's intentions. To meet these challenges, we propose that the human operator and the robot interact to create and gradually improve a guide path. This paper introduces design tools to enable an intuitive interaction, and reports experiments performed with healthy subjects in order to investigate this collaborative path learning strategy. We analyzed features of the optimal paths and user evaluation in representative conditions. This was complemented by a questionnaire filled out by the subjects after the experiments. The results demonstrate the effectiveness of this approach, and show the utility and complementarity of the tools to design ergonomic guide paths.