Research

Bouncing Robot Localization:

Localization of a simple bouncing robot equipped with only a contact sensor and a clock, is determining it's pose in a given environment, typically within a given map or a similar representation. We make the limits of localization accuracy precise by establishing the fundamental limits imposed by symmetry as revealed by the robot's sensors. Our approach is based on finding attracting cycles and transient trajectories of the robot path as it bounces within an environment filled with obstacles. Based on the cycles and the transients, space-efficient and automata-based combinatorial filters are synthesized to solve localization problems modulo symmetries.

Stochastic Multi-Robot Patrolling:

Multi-robot patrolling is the problem of repeatedly visiting a group of regions of interest in an environment with multiple robots to prevent intrusion. Previous works provide non-deterministic patrolling schemes which only work for perimeter patrolling and require coordination and synchronization. We have developed algorithms to find different distributed strategies for patrollers in the form of Markov chains which use convex optimization to minimize the average commute time for an environment, a subset of the environment, or a specific region of an environment. We use these strategies in a game theoretical framework to obtain an optimal mixed strategy for patrollers to avoid adversarial attack. Our approach is scalable and applicable to any type of environment represented as a graph.

Our approach is tested on different types of graphs, including line, tree, mesh, complete, and randomly generated graphs. In each of the above graphs, the thickness of each edge corresponds to the optimal edge weight value, or the probability of that edge being chosen by a patroller. For example, a wider edge connection between two vertices represents a high probability of that edge being chosen for travel, and vice versa.

In an adversarial environment, wireless communication between the robots may be jammed, and their sensor ranges may be limited to visibility. This increases the difficulty of the problem, but solutions will be widely applicable regardless of the environment. We propose a method of finding patrolling policies for multiple robots that monitor any polygonal environment using limited visibility regions and non-deterministic patrolling paths. First, visibility regions are calculated for a subset of locations that cover the whole environment or a part of the environment. Then, we find distributed patrolling policies in the form of Markov chains, using convex optimization to minimize the average expected commute time for the subset of the locations permitting each robot to cover the whole environment independently. We also find centralized and Markov chain based patrolling policies that minimize the average expected commute time for the subset of locations permitting each robot to cover a part of the environment while also communicating with a central base station. Finally, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary and the maximum unguarded time for a location in our proposed patrolling scenarios.

Related Paper:

T. Alam, M. M. Rahman, P. Carrillo, L. Bobadilla, and B. Rapp, "Stochastic Multi-Robot Patrolling with Limited Visibility", Journal of Intelligent and Robotic Systems, 97(2): 411-429, February 2020. [Preprint: PDF]
T. Alam, M. Edwards, L. Bobadilla, and D. Shell, "Distributed Multi-Robot Area Patrolling in Adversarial Environments," in the International Workshop on Robotic Sensor Networks (RSN), Seatlle, WA, USA, 2015. [PDF]
T. Alam, M. M. Rahman, L. Bobadilla, and R. Rapp, "Multi-Vehicle Patrolling with Limited Visibility and Communication Constraints", in Proceedings of IEEE Conference on Military Communications (MILCOM), pp. 465-470, Baltimore, MD, USA, October 2017. [PDF]

Data-Driven Planning for Underactuated Underwater Vehicles:

The dynamics and underlying phenomena of marine environments vary both spatially and temporally. Thus, the sensing, modeling, sampling, and prediction of marine environments are challenging tasks. To properly analyze and understand the environmental processes, we must observe them over long periods. To achieve this, we investigate the long-term trajectories of inexpensive and minimally-actuated drifting vehicles called drifters. These vehicles drift passively with ambient ocean currents. Vertical actuation (buoyancy) enables them to alter their depth and achieve controllability by the use of different current layers in the ocean. We aim to utilize these persistent assets to explore oceanic regions exhaustively; patrolling the surface, covering a 3-D phenomenon (e.g., algal bloom), or examining the shallow seafloor with downward-facing cameras. We present a data-driven planning approach to address the deployment and localization problems for the drifters.

Related Paper:

T. Alam, G. M. Reis, L. Bobadilla, and R. N. Smith, "A Data-Driven Deployment and Planning Approach for Underactuated Vehicles in Marine Environments", IEEE Journal of Oceanic Engineering, 2020 (Early Access). [Preprint: PDF].
T. Alam, G. M. Reis, L. Bobadilla, and R. N. Smith, "A Data-Driven Deployment Approach for Persistent Monitoring in Aquatic Environments", in Proceedings of IEEE International Conference on Robotic Computing (IRC), pp. 147-154, Laguna Hills, CA, USA, February 2018. [PDF]
T. Alam, G. M. Reis, L. Bobadilla, and R. N. Smith, "An Underactuated Vehicle Localization Method in Marine Environments", in Proceedings of MTS/IEEE OCEANS Conference, pp. 1-8, Charleston, SC, USA, October 2018. Best Student Paper Finalist. [arXiv]

Privacy-Preserving Robotics:

We consider the problem of multiple robots that must cooperate within a shared environment, but which wish

to limit the information they disclose during their coordination efforts. Specifically, we examine the problems of privacy-preserving rendezvous and persistent monitoring. In the former,the robots construct a joint plan to have them meet, without either knowing beforehand where or when the meeting will occur. In the latter, multiple robots dynamically cover a region of space—they plan collective motions which are collision-free but with the assurance that agents remain ignorant of the paths of others. Accordingly, the tasks are sort of inverses in that the robots must collectively determine whether their joint paths collide or not, then, using this, achieve their collective task. Other than what is learned by the outcome of the joint-collision determination, the robots possess no details of the other paths. Our approach builds on garbled circuits and homomorphic encryption to realize basic secure path intersection primitives.

See my recent project details here

Related Paper:

1. L. Li, A. Bayuelo, L. Bobadilla, T. Alam, and D. A. Shell, "Coordinated Multi-Robot Planning While Preserving Individual Privacy", in Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 2188-2194, Montreal, Canada, May 2019. [PDF]