Reach-to-grasp movements are widely observed in activities of daily living, particularly in tool manipulations. In order to reduce the complexity in redundancy resolution and facilitate upper-limb exoskeleton control in reach-to-grasp tasks, we studied joint coordination in the human arm during such movements. Experimental data were collected on reach-to-grasp movements in a 3-dimensional (3D) workspace for cylinder targets of different positions and grasping orientations. For comparison, reaching movements toward the same targets are also recorded. In the kinematic analysis, the redundant degree of freedom in human arm is represented by the swivel angle. The four grasping-relevant degrees of freedom (GR-DOFs), including the swivel angle and the three wrist joints, behave differently in reach-to-grasp movements comparing to how they behave in reaching movements. The ratio of active motion range (R-AMR) is proposed for quantitatively comparison the task-relevance of the GR-DOFs. Analysis on the R-AMR values shows that the task-relevant GR-DOFs are more actively used, while the task-irrelevant joints are left uncontrolled and maintain their neutral positions. Among the task-relevant GR-DOFs, the smaller joints (micro-structure) are more actively used than the larger joints (macro-structure). The coordination of the task-relevant GR-DOFs is shown to be synergistic. Analysis of the acceleration/deceleration at the GR-DOFs indicates different levels of voluntary control in three phases of the movements. The study of the characteristics of the joint coordination in reach-to-grasp movements provides guide-lines for simplifying the control of the upper limb exoskeleton.

According to the seven degrees of freedom (DOFs) human arm model composed of the shoulder, elbow, and wrist joints, positioning of the wrist in space and orientating the palm is a task requiring only six DOFs. Due to this redundancy, a given task can be completed by multiple arm configurations, and there is no unique mathematical solution to the inverse kinematics. The redundancy of a wearable robotic system (exoskeleton) that interacts with the human is expected to be resolved in the same way as that of the human arm. A unique solution to the system's redundancy was introduced by combining both kinematic and dynamic criteria. The redundancy of the arm is expressed mathematically by defining the swivel angle: the rotation angle of the plane including the upper and lower arm around a virtual axis connecting the shoulder and wrist joints which are fixed in space. Two different swivel angles were generated based on kinematic and dynamic constraints. The kinematic criterion is to maximize the projection of the longest principle axis of the manipulability ellipsoid for the human arm on the vector connecting the wrist and the virtual target on the head region. The dynamic criterion is to minimize the mechanical work done in the joint space for each two consecutive points along the task space trajectory. These two criteria were then combined linearly with different weight factors for estimating the swivel angle. Post processing of experimental data collected with a motion capturing system indicated that by using the proposed synthesis of redundancy resolution criteria, the error between the predicted swivel angle and the actual swivel angle adopted by the motor control system was less then five degrees. This result outperformed the prediction based on a single criteria.

Surgical procedures are traditionally performed by two or more surgeons along with staff nurses. One surgeon serves as the primary surgeon and the other serves as his/her assistant. Surgical robotics have redefined the dynamics in which the two surgeons interact with each other and with the surgical site. Raven IV is a new generation of the surgical robot system having four articulated robotic arms in a spherical configuration, each holding an articulated surgical tool. The system allows two surgeons to teleoperate the Raven IV collaboratively from two remote sites. The current research effort aims to configure the link architecture of each robotic arm, along with the position (port placement) and orientation of the Raven IV with respect to the patient, in order to optimize the common workspace reachable by all four robotic arms. The simulation results indicate that tilting the base of the robotic arms in the range of -20 to 20 deg while moving the ports closer together up to 50 mm apart leads to a preferred circular shape of the common workspace with an isotropy value of 0.5. A carefully configured system with multiple surgical robotic arms will enhance the interactive performance of the two surgeons.

A nonlinear system gives rise to many inherent difficulties when designing a feedback control. Motivated by a fixed-speed, fixed-altitude Unmanned Aerial Vehicle (UAV) that tracks an unpredictable target, we seek to control the turning rate of a planar Dubins vehicle. We introduce stochasticity in the problem by assuming the target performs a random walk, which both aides in the computation of a smooth value function and further accounts for all realizations of target kinematics. A Bellman equation based on an approximating Markov chain that is consistent with the stochastic kinematics is used to compute a control policy that minimizes the expected value of a cost function based on a nominal UAV-target distance. Our results indicate how uncertainty in the target motion affects the control law, and simulations illustrate that the control can further be applied to any continuous, smooth trajectory with no need for further computation.

This paper describes an approach to the modeling and control of multiagent populations composed of a large number of agents. The complexity of population modeling is avoided by assuming a stochastic approach, under which the agent distribution over the state space is modeled. The dynamics of the state probability density functions is determined, and a control problem of maximizing the probability of robotic presence in a given region is introduced. The Minimum Principle for the optimal control of partial differential equations is exploited to solve this problem, and it is applied to the mission control of a simulated large robotic population

Introduces a robotic task model (RTM) based on Petri nets, that establishes a framework for task evaluation from qualitative and quantitative viewpoints, as well as a methodology for the implementation of robotic task coordination. A testbed for the evaluation of the RTM and the details of its implementation over a network of distributed task executors is described.