Variations in material concentration resulting from a biochemical or radiological contaminant leakage, such as an oil spill in the ocean or a radioactive dispersal in the atmosphere, is dominated by turbulent mixing. The result is a highly anisotropic and unsteady sensory landscape where sensor measurements become the sporadic and intermittent which renders gradient based search strategies highly ineffective. This work develops information based search strategies for autonomous robots to search and localize the source of a biochemical contaminant dispersed in turbulent media. The approach has been validated using state-of-the-art 3D computational fluid models of the 2010 Deep Water Horizon oil spill developed by Dr. Alex Fabregat Tomás at CUNY.
Distributed autonomous assembly of general two (2D) and three dimensional (3D) structures is a complex task requiring robots to have the ability to: 1) sense and manipulate assembly components; 2) interact with the desired structure at all stages of the assembly process; 3) satisfy a variety of precedence constraints to ensure assembly correctness; and 4) ensure the stability and structural integrity of the desired structure throughout the assembly process. While the distributed assembly problem represents a class of tightly-coupled tasks that is of much interest in multi-robot systems, it is also highly relevant to the development of next generation intelligent, flexible, and adaptive manufacturing and automation. In this work, we address the assembly of a three dimensional structures by a team of robots. Specifically, we address the challenges of partitioning a complex assembly task into N loosely coupled tasks each executed by a single robot. Furthermore, we have developed online sensing capabilities that enable the team to determine the state of the structure during assembly to allow for the identification and correction of assembly errors.
Two mobile manipulators assembling a 3-D structure. A planning algorithm partitions the assembly task into N subcomponents that can be executed by individual robots with minimal communication with other robots. See IROS 2011 paper for details.
This project provided undergraduate and graduate students a unique opportunity to work with an interdisciplinary and international team of researchers on the design and control of multi-agent robotic systems. The technical focus of the collaboration was centered around the design of robust multi-robot coordination strategies for execution of large scale cooperative tasks. Advances in embedded processor and sensor technology in the last thirty years have accelerate the demand for teams of robots in various application domains. Multi-agent robotic systems are particularly well-suited to execute tasks that cover wide geographic ranges, require significant parallelization, and/or depend capabilities that are varied in both quantity and difficulty. Example applications include littoral exploration and surveillance, rainforest health monitoring, autonomous transportation systems, warehouse automation, and hazardous waste clean-up.
Daniel Mox, B.S./M.S. 2015, Drexel University, Ph.D. student at the University of Pennsylvania
Dennis Larkin, M.S. 2015, Drexel University
Emily LeBlanc, B.S. 2014, Temple University, Ph.D. student at Drexel University
James Milligan, B.S./M.S. 2013, Drexel University, SRI