MSEAS Team of Deepak Subramani and Tapovan Lolla presented their results on “Optimal Path-Planning in Dynamic Environments” at the 2015 de Florez Design competition and won the “Honorable Mention cash award” in the Graduate Science category. The competition website is here.

Abstract: In the recent years there has been an increased interest in developing control and navigation strategies for multi-agent systems. Typical tasks include guiding a team of small autonomous agents (e.g., UAVs, UUVs) towards a given goal, or assigning a set of stationary or moving targets to a team of pursuing agents. For heterogeneous teams of kinematic agents or for agents whose motion is affected by the environment they operate in (for example, the motion of unmanned aerial or marine/submersible vehicles may be significantly affected by the prevailing local winds or sea currents). For such problems the Euclidean distance may not be an appropriate figure of merit to define suitable proximity relationships. Instead, time-to-intercept or fuel-to-consume may be a more appropriate cost to use. In this talk, we will discuss the optimal partitioning problem for a team of agents in the plane when the proximity relationships are dominated by such state-dependent metrics. We will introduce the concept of Zermelo-Voronoi partitions to analyze the particular case of a team of UAVs/UUVs operating in the presence of winds or currents. We provide a numerically efficient construction of these partitions by taking advantage of the structure of the underlying optimal control problem, which may include both regular and abnormal extremals. By exploiting the well-known duality between navigation in the presence of environmental disturbances and pursuit problems, we will then utilize these partitions to solve a certain class of group pursuit problems. We will finally briefly elaborate on some potential extensions.

Biography: Dr. Panagiotis Tsiotras is the Dean’s Professor at the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology (Georgia Tech) and the Director of the Dynamics and Control Systems Laboratory in the same department. He is also affiliated with the Institute for Robotics and Intelligent Machines (IRIM) at Georgia Tech. He has held visiting appointments with at Ecole des Mines Mines ParisTech), INRIA-Rocquencourt in France, JPL, and MIT. He received his PhD degree in Aeronautics and Astronautics from Purdue University in 1993, and he also holds degrees in Mechanical Engineering and Mathematics. He is a Fellow of AIAA.

PhD Candidates Deepak Subramani and Tapovan Lolla and Prof Pierre Lermusiaux attended an environment focussed meeting with Honorable Prakash Javadekar, the Minister for Environment, Forest and Climate Change in the Govt. of India at the MIT Tata Center on 4/16/15. The group’s research efforts with a focus on India specific goals and the new Tata Project (2015-17) were discussed during the meeting.

Oceanic fronts, similar to atmospheric fronts, occur at the interface of two fluid (water) masses of varying characteristics. In regions such as these where there are quantifiable physical, chemical, or biological changes in the ocean environment, it is possible—with the proper instrumentation—to track, or map, the front boundary.

In this paper, the front is approximated as an isotherm that is tracked autonomously and adaptively in 2D (horizontal) and 3D space by an autonomous underwater vehicle (AUV) running MOOS-IvP autonomy. The basic, 2D (constant depth) front tracking method developed in this work has three phases: detection, classification, and tracking, and results in the AUV tracing a zigzag path along and across the front. The 3D AUV front tracking method presented here results in a helical motion around a central axis that is aligned along the front in the horizontal plane, tracing a 3D path that resembles a slinky stretched out along the front.

To test and evaluate these front tracking methods (implemented as autonomy behaviors), virtual experiments were conducted with simulated AUVs in a spatiotemporally dynamic MIT MSEAS ocean model environment of the Mid-Atlantic Bight region, where a distinct temperature front is present along the shelfbreak. A number of performance metrics were developed to evaluate the performance of the AUVs running these front tracking behaviors, and the results are presented herein.

This paper presents a novel way to approach the problem of how to adaptively sample the ocean using fleets of underwater gliders. The technique is particularly suited for those situations where the covariance of the field to sample is unknown or unreliable but some information on the variance is known. The proposed algorithm, which is a variant of the well-known fuzzy C-means clustering algorithm, is able to exploit the presence of non-maneuverable assets, such as fixed buoys. We modified the fuzzy C-means optimization problem statement by including additional constraints. Then we provided an algorithmic solution to the new, constrained problem.