As a computer scientist student and control engineer proficient in programming and design algorithms as well as control and mechanical engineering. With diverse background and skills in machine learning, robotics, algorithm design control system and instrumentation in design and implementation.
Area of interest:
Machine learning, robotics, distributed algorithms,sensor network, computer graphics, graph theory, computer graphics, computer vision, computational geometry, game development, advanced control systems, instrumentation
Coursework: Algorithmic Robotics, Bioinformatics - Sequence Analysis, Advanced Robotic Lab, Computer Networks, Statistical Machine Learning Computer Graphics, Introduction to Robotics (Manipulators), Design and Analysis of Algorithms, Computer Architecture Introduction to Computer Science (C++ programming), Automata.
Coursework: Adaptive Control, Optimal Control, Neural Networks, Multi variable Control, System Identification Pattern Recognition, Advanced Engineering mathematics. Robust Control and Linear Matrix Inequalities.
Coursework: Linear system control, Industrial control, Intelligent systems and fuzzy control, Telecommunication, Electronic system design 1 and 2, Electronic circuits, Fuzzy control, Digital control, Operational research, statistics and probability in engineering. Fundamental of Economy
Instructor of course in electrical engineering :
Designed PLC and HMI for a sugar product company.
Experted in WinCC.
Analyzed The process of product, transmit and distributed the electricity power. Statistical analysis in both performance and costs.
K-redundant Trees for Safe and Efficient Multi-robot Recovery:
This projects presents a self-stabilizing distributed algorithm to recover a large number of robots safely and efficiently in a goal location. Previously, we designed a distributed algorithm, called DMLST, to recover robots Our approach constructed a maximum-leaf spanning tree for physical routing, such that interior robots remained stationary and leaf robots move. In this paper, we extend our approach to k-DMLST recovery that provides k-connectivity in the network, meaning that each robot is connected to the goal location through k trees. This redundancy provides stronger network connectivity by reducing the probability of losing the parent during recovery. We also propose an efficient navigation algorithm for the motion of robots which guarantees forward progress during the recovery. k-DMLST recovery has been tested and compared with other methods in simulation, and implemented on a physical multi-robot system. A basic recovery algorithm fails in all experiments, and DMLST recovery is not successful in few trials However, k-DMLST recovery efficiently succeeds in all trials.
Massive Uniform Manipulation:
In this project we investigate control of mobile robots that move in a 2D workspace using three different system models. We focus on a model that uses broadcast control inputs specified in the global reference frame.In an obstacle-free workspace this system model is uncontrollable because it has only two controllable degrees of freedom—all robots receive the same inputs and move uniformly. We prove that adding a single obstacle can make the system controllable, for any number of robots. We provide a position control algorithm, and demonstrate through extensive testing with human subjects that many manipulation tasks can be reliably completed, even by novice users, under this system model, with performance benefits compared to the alternate models. We compare the sensing, computation, communication, time, and bandwidth costs for all three system models. Results are validated with extensive simulations and hardware experiments using over 100 robots.
Collective Transport of Complex Objects by Simple Robots :
In this project we investigate a simple decentralized strategy for collective transport in which each agent acts independently without explicit coordination. Using a physics-based model, we prove that this strategy is guaranteed to successfully transport a complex object to a target location, even though each agent only knows the target direction and does not know the object shape, weight, its own position, or the position and number of other agents. Using two robot hardware platforms, and a wide variety of complex objects, we validate the strategy through extensive experiments. Finally, we present a set of experiments to demonstrate the versatility of the simple strategy, including transport by 100 robots, transport of an actively moving object, adaptation to change in goal location, and dealing with partially observable goals.
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