Robotics

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Past Projects

 

Improving the Realism of Haptic Interaction for Teaching of Sensorimotor Skills
Investigators: Maxim Kolesnikov and Miloš Žefran
Recently, haptic simulators have shown great potential in teaching sensorimotor skills. This is especially true for areas where the traditional training technique is expensive, such as medical and dental training. The goal of this work is to address several key areas where improvement is needed to increase the realism of haptic interaction for teaching of sensorimotor skills. These key areas are haptic rendering algorithms, haptically augmented training video aids and collaborative haptic environments. See details

Support: NSF grant CMS-0600658 and UIC College of Dentistry

 

Distributed Switching Algorithms for Robotic Networks
Investigators: Carlos Caicedo Núñez and Miloš Žefran
The focus of this research project lies in robotic networks. In particular, we are interested in studying how can we solve global problems in a network when the robots only have access to local pieces of information. By sharing information with its neighbors, each robot can learn more about the environment, and can adjust its behavior as the information it is gathering gets more complete. Now, when there is more than one task that has to be addressed by the network, the local information must suffice for each agent to take the best possible decision, so it would not compromise in the long-term either of the objectives of the system. For more details click here.

Support: NSF grants IIS-0093581 and CCR-0330342

Localizing Vapor-Emitting Sources Using a Distributed Mobile Sensing Network
Investigators: Panos Tzanos and Miloš Žefran
Research project deals with localizing vapor-emitting sources using distributed mobile sensing networks. This entails developing a physical model of the vapor concentration, a motion control algorithm for the sensors, and a coordination algorithm to coordinate the motions of the sensors (see details).

Support: NSF grant CCR-0330342

Application of Hybrid Optimal Control to Multi-vehicle Path Planning
Investigators: Shangming Wei and Miloš Žefran
The project studies the path planning problem of a system consisting of multiple autonomous vehicles. The basic problem formulation is to move the vehicles from some initial states to some final states, while at the same time avoiding each other and the obstacles in the environment. The goal is to find energy-optimal paths for these vehicles. We have converted it into a hybrid optimal control problem and are trying to find fast and effective approaches to numerically solve the problem. Some developed techniques have been successfully applied in examples of some types of wheeled mobile robots (for example, unicycle and Hilare robot). See details.

Support: NSF grant IIS-0093581 and UIC Campus Research Board

Computational Approach to Dynamical Bipedal Walking
Investigators: Guobiao Song and Miloš Žefran
The goal of this research project is to establish a general framework and a pure computational implementation for stabilization of periodic orbits for hybrid systems with impact effects. Especially, this is applied to bipedal walking. We perform the robust controller design for two- and three-dimensional underactuated biped robots. We demonstrate that dynamics of the hybrid system along a periodic orbit can be decomposed into the transverse and tangential components for hybrid systems. The robust control synthesis problem of the resulting periodic transverse linearization can be cast as a semidefinite program (SDP) and thus efficiently solved by means of Linear Matrix Inequality (LMI).

Past research topics in this area include: research in the robust Lyapunov stability theory for hybrid systems, development of Matlab and Mathematica tools for robust control synthesis for a class of hybrid systems based on LMI, development of a computational optimizing approach in generating energy efficient walking gaits for underactuated dynamical bipedal walking, design and implementation of a novel control approach based on these theories and tools.

Support: NSF grant IIS-0093581 and UIC Campus Research Board

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