saihaneesh [dot] allu [at] utdallas [dot] edu
Ph.D. Candidate
Intelligent Robotics and Vision Lab
Department of Computer Science
The University of Texas at Dallas
I'm currently pursuing my Ph.D. in Computer Science at The University of Texas at Dallas, where I'm part of the Intelligent Robotics and Vision Lab, under the guidance of Dr. Yu Xiang. My primary focus is at the intersection of robotics and computer vision, specifically applied to navigation, exploration, and mobile manipulation.
My journey in advanced technology began with a Bachelor’s degree in Electrical and Electronics Engineering from the National Institute of Technology, Warangal. I then pursued a Master’s in Control Systems at the Indian Institute of Technology, Delhi. During my master's program, I co-founded VECROS Technologies, where I led the development of GPS-denied navigation for multi-rotor aircraft systems and IoT-based BVLOS (Beyond Visual Line of Sight) operations.
Now, as a Ph.D. candidate, I am eager to push the boundaries of what’s possible in robotics and contribute to developing robot technologies that assist humans in their daily lives.
As a passionate educator, I have had the privilege of teaching and assisting in a variety of courses throughout my academic career. Below are some of the courses I've contributed to:
Yu Xiang, Sai Haneesh Allu, Rohith Peddi, Tyler Summers, Vibhav Gogate
Project Page |
arXiv
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024.
Ninad Khargonkar*, Sai Haneesh Allu*, Yangxiao Lu, Balakrishnan Prabhakaran, Yu Xiang
Project Page |
arXiv |
Video
In International Conference on Robotics and Automation (ICRA), 2024.
Abstract: The primary purpose of the study is to investigate various formation control algorithms as well as implementing them on an experimental platform with the ultimate goal of implementing target interception by choosing the best suited among the implemented algorithms. The open source nanoquadcopter platform Crazyflie 2.0 was chosen for the experimentation and Ardupilot flight stack along with DroneKit software in the loop were used for simulation purposes. The first phase consisted of study of virtual structure, leader-follower, and a graph theoretic method of formation control. Secondly, understanding the control architecture of Crazyflie 2.0, system setup, and operation of OptiTrack motion capture system, Robot Operating System, and DroneKit Software in the loop. Up next is the controller design of the above mentioned formation control algorithms and implementation on the chosen platforms, comparing their performance in formation sustenance. Comparison shows that graph theoretic method is best suitable for formation maintenance. Finally, target interception has been simulated using the graph theoretic method and further exploitation of velocity and trajectory-based formation controls are proposed as future work through optimization techniques.