The purpose of this work is to move a step toward the automation of industrial plants through full exploitation of autonomous robots. A planning algorithm is proposed to move different objects in desired configurations with heterogeneous robots such as manipulators, mobile robots and conveyor belts. The proposed approach allows different objects to be handled by different robots simultaneously in an efficient way and avoiding collisions with the environment and self-collisions between robots. In particular, the integrated system will be capable of planning paths for a set of objects from various starting points in the environment (e.g. shelves) to their respective final destinations. The proposed approach unifies the active (e.g., grasping by a hand) and passive (e.g., holding by a table) steps involved in moving the objects in the environment by treating them as end-effectors with constraints and capabilities. Time varying graphs will be introduced to model the problem for simultaneous handling of objects by different end-effectors. Optimal exploration of such graphs will be used to determine paths for each object with time constraints. Results will be validated through simulations.

Microrobots are promising tools for micromanipulation and minimally invasive interventions. Robust electromagnetic control of microrobots can be achieved through precisely modeled magnetic steering systems and accurate localization. Error-free modeling and position information, however, are not realistic assumptions, and microrobots need to be controlled under force and localization uncertainties. In this paper, methods to account for these types of uncertainties are presented. Initially, the uncertainties in electromagnetic force generation of a new class of manipulation systems are quantified. Subsequently, a drag-force uncertainty model for linear dynamics is proposed. This model can be employed for microrobots whose fluid dynamics are not well understood. A set of performance measures is introduced in the design of controllers, and a PID and a robust H∞controller are synthesized and evaluated through simulations. To demonstrate the capabilities of the synthesized controllers under localization and force uncertainties, low update rates are considered. The H∞controller can provably respect the performance measures under higher uncertainties than the PID controller, and its performance is further quantified through experiments in a prototype electromagnetic control system.

This paper presents a methodology to accurately record human finger postures during grasping. The main contribution consists of a kinematic model of the human hand reconstructed via magnetic resonance imaging of one subject that (i) is fully parameterized and can be adapted to different subjects, and (ii) is amenable to in-vivo joint angle recordings via optical tracking of markers attached to the skin. The principal novelty here is the introduction of a soft-tissue artifact compensation mechanism that can be optimally calibrated in a systematic way. The high-quality data gathered are employed to study the properties of hand postural synergies in humans, for the sake of ongoing neuroscience investigations. These data are analyzed and some comparisons with similar studies are reported. After a meaningful mapping strategy has been devised, these data could be employed to define robotic hand postures suitable to attain effective grasps, or could be used as prior knowledge in lower-dimensional, real-time avatar hand animation.

Electromagnetic systems for in vivo microrobot steering have the potential to enable new types of localized and minimally invasive interventions. Accurate control of microrobots in natural fluids requires precise, high-bandwidth localization and accurate knowledge of the steering system's parameters. However, current in vivo imaging methodologies, such as fluoroscopy, must be used at low update rates to minimize radiation exposure. Low frame rates introduce localization uncertainties. Additionally, the parameters of the electromagnetic steering system are estimated with inaccuracies. These uncertainties can be addressed with robust H∞control, which is investigated in this paper. The controller is based on a linear uncertain dynamical model of the steering system and microrobot. Simulations show that the proposed control scheme accounts for modeling uncertainties, and that the controller can be used for servoing in low viscosity fluids using low frame rates. Experiments in a prototype electromagnetic steering system support the simulations.

In this paper we consider the motion planning problem for a 3D-Dubins system consisting of a pair of Dubins vehicles moving in a 3D space while maintaining constant distance. We provide a motion planning algorithm and sufficient conditions on the initial and final configurations that guarantee the existence of admissible controls, moving a first step towards the complete controllability characterization of such type of systems. Results obtained in this paper are particularly relevant in order to solve formation control problem for multiple robots as aerial or underwater vehicles, which move in 3D spaces. Simulation results demonstrate the feasibility of the motion planning algorithm proposed in this paper.