Intelligent adaptive underwater sensor networks

Autonomous Underwater Vehicle (AUV) technology has reached a sufficient maturity level to be considered a suitable alternative to conventional Mine Countermeasures (MCM). Advantages of using a network of AUVs include time and cost efficiency, no personnel in the minefield, and better data collection. A major limitation for underwater robotic networks is the poor communication channel. Currently, acoustics provides the only means to send messages beyond a few metres in shallow water, however the bandwidth and data rate are low, and there are disturbances, such as multipath and variable channel delays, making the communication non-reliable. The solution this thesis proposes using a network of AUVs for MCM is the Synchronous Rendezvous (SR) method --- dynamically scheduling meeting points during the mission so the vehicles can share data and adapt their future actions according to the newly acquired information. Bringing the vehicles together provides a robust way of exchanging messages, as well as means for regular system monitoring by an operator. The gains and losses of the SR approach are evaluated against a benchmark scenario of vehicles having their tasks fixed. The numerical simulation results show the advantage of the SR method in handling emerging workload by adaptively retasking vehicles. The SR method is then further extended into a non-myopic setting, where the vehicles can make a decision taking into account how the future goals will change, given the available resource and estimation of expected workload. Simulation results show that the SR setting provides a way to tackle the high computational complexity load, common for non-myopic solutions. Validation of the SR method is based on trial data and experiments performed using a robotics framework, MOOS-IvP. This thesis develops and evaluates the SR method, a mission planning approach for underwater robotic cooperation in communication and resource constraint environment

Learning executable models of physical social agent behavior

Issued as final reportNational Science Foundation (U.S.

TAR: Trajectory adaptation for recognition of robot tasks to improve teamwork

One key to more effective cooperative interaction in a multi-robot team is the ability to understand the behavior and intent of other robots. Observed teammate action sequences can be learned to perform trajectory recognition which can be used to determine their current task. Previously, we have applied behavior histograms, hidden Markov models (HMMs), and conditional random fields (CRFs) to perform trajectory recognition as an approach to task monitoring in the absence of commu- nication. To demonstrate trajectory recognition of various autonomous vehicles, we used trajectory-based techniques for model generation and trajectory discrimination in experiments using actual data. In addition to recognition of trajectories, we in- troduced strategies, based on the honeybee’s waggle dance, in which cooperating autonomous teammates could leverage recognition during periods of communication loss. While the recognition methods were able to discriminate between the standard trajectories performed in a typical survey mission, there were inaccuracies and delays in identifying new trajectories after a transition had occurred. Inaccuracies in recog- nition lead to inefficiencies as cooperating teammates acted on incorrect data. We then introduce the Trajectory Adaptation for Recognition (TAR) framework which seeks to directly address difficulties in recognizing the trajectories of autonomous vehicles by modifying the trajectories they follow to perform them. Optimization techniques are used to modify the trajectories to increase the accuracy of recognition while also improving task objectives and maintaining vehicle dynamics. Experiments are performed which demonstrate that using trajectories optimized in this manner lead to improved recognition accuracy.Ph.D

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Sequential Single-Cluster Auctions for Multi-Robot Task Allocation

This thesis studies task allocation in multi-robot teams operating in dynamic environments. The multi-robot task allocation problem is a complex NP-Complete optimisation problem with globally optimal solutions often difficult to find. Because of this, the rapid generation of near optimal solutions to the problem that minimise task execution time and/or energy used by robots is highly desired. Our approach seeks to cluster together closely related tasks and then builds on existing distributed market-based auction architectures for distributing these sets of tasks among several autonomous robots. Dynamic environments introduce many challenges that are not found in closed systems. For instance, it is common for additional tasks to be inserted into a system after an initial solution to the task allocation problem is determined. Additionally, it is highly likely in long-term autonomous systems that individual robots may suffer some form of failure. The ability to alter plans to react to these types of challenges in a dynamic environment is required for the completion of all tasks. In our approach we allow the repeated formation and auctioning of task clusters with varying tasks. This allows us to react to and change the task allocation among robots during execution. Throughout this thesis we use empirical evaluation to study different approaches for forming clusters of tasks and the application of task clustering to distributed auctions for multi-robot task allocation problems. Our results show that allocating clusters of tasks to robots in solving these types of problems is a fast and effective method and produces near optimal solutions

Compilation of thesis abstracts, December 2006

NPS Class of December 2006This quarter’s Compilation of Abstracts summarizes cutting-edge, security-related research conducted by NPS students and presented as theses, dissertations, and capstone reports. Each expands knowledge in its field.http://archive.org/details/compilationofsis109452750