Florida International University
Tauhidul Alam is a Ph.D. candidate in the School of Computing and Information Sciences at Florida International University (FIU). He is a member of the Motion, Robotics, and Automation lab, where he works under the supervision of Dr. Leonardo Bobadilla. He received his B.Sc. in Computer Science and Engineering from Chittagong University of Engineering and Technology (CUET), Bangladesh in 2008. His research interests span the areas of Robotics, Motion Planning, and Cyber-Physical Systems. In particular, his research aims to devise solutions to the fundamental robotic tasks of localization, navigation, coverage, and patrolling of mobile robots with limited sensing, actuation, computation, and communication.
The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing abilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system tools. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios.
First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment
Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated.
Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios.
Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long-term behavior of an aquatic environment from a flow point-of-view.
In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics systems.