Cable robots have seen considerable recent interest ensuing from their ability to combine a large workspace with significant payload capacity. However, the cables can apply forces to the end-effector only when they are in tension, and thus form a subclass of control problems requiring unilateral control inputs. Furthermore, actuation redundancy occurs when surplus cables are introduced within the system. On one hand, such redundancy needs to be carefully resolved for accurate tracking of the task. On the other hand, it allows the redistribution of the actuation forces to satisfy some secondary criteria. In this paper, we apply such redundancy for enhanced trajectory tracking by actively controlling the task stiffness of the end-effector. The scheme allow us to specify a lower bound of the task stiffness, which is intended to provide improved trajectory tracking and disturbance rejection performance. Finally, we illustrate the improved control performance within a virtual prototype cosimulation framework.

Variability in motor rehabilitation program outcomes can be attributed not only to individual components (human patient/rehabilitation equipment) but also to their system-level interactions. Thus, effective deployment of a rehabilitation program depends upon: 1) suitable therapist selection of user-device ergonomics; 2) adjustable device settings; and 3) exercise regimen parameters; to achieve desired system-level motor performance. In this paper, we discuss aspects of creation of a virtual design environment, leveraging tools from musculoskeletal analysis, optimization, and simulation-based design, to permit therapists to rapidly evaluate and systematically customize rehabilitation programs. Specifically, this framework is intended to facilitate 1) parametric study of ergonomic/device/regimen settings on musculoskeletal performance; 2) use of design tools such as optimization for decision support in arriving at the best program; and 3) scaffolded examination of linkage between form and function by iterative what-if type of analyses. We use two case studies (bicep-curling and motor rehabilitative driving) to highlight benefits of such simulation-based rehabilitation program evaluation.

In this paper, we evaluate the performance of two candidate formulations for distributed motion planning of robot collectives within an Artificial Potential Field (APF) framework. We exploit the parallel between the formulation of motion planning for group of robots coupled by constraints and the forward dynamics simulation of constrained multibody systems to develop the candidate approaches. We compare and contrast these approaches on the basis of ease of formulation, distribution of computation and overall computational accuracy. Traditionally penalty formulations have enjoyed a prominent position in motion planning of robot collectives due to their ease of formulation, decentralization and scalability. However, the instabilities introduced in the form of “formulation stiffness” at the algorithm development stage have the potential to hinder the subsequent control. Representative results from the distributed motion planning for a group of 3 point-mass robots moving in formation to a desired target location are used to highlight the differences.