Florida International University
Tauhidul Alam is a Ph.D. candidate of Computer Science at the Florida International University’s School of Computing and Information Sciences. He is a member of 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, Bangladesh in 2008. His research interests lie in the areas of Robotics, Motion Planning, and Cyber-Physical Systems. His research aims to solve fundamental tasks of robotics such as localization, navigation, coverage, and patrolling of mobile robots with limited sensing and computation.
Recent advances in technology have led to the development of robots with abundant sensors, actuators with large degrees of freedom, high-performance computing abilities, and high-speed communication devices.
These robots use a large volume of information from sensors to solve different tasks such as localization, navigation, coverage, monitoring, and patrolling. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sensors may not work, the real-time processing power of a robot may be inadequate, or the communication among robots may be impeded by natural or adversarial conditions. In these cases, we have to rely on simple robots with limited sensing, minimal onboard processing, moderate communication, and little memory capabilities. 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 simple 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 the simple robot. The proposed solution finds a navigation plan from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined optimally so that the actual coverage distribution can become as close as possible to a target coverage distribution. Third, a sampling-based method is developed to find a joint trajectory for multiple simple robots to visit all the locations of an environment. Our proposed work also generates overlapping and collision-free trajectories of robots to persistently monitor the target regions in a given environment. Finally, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment.