This paper focuses on enabling multi-robot teams to cooperatively perform tasks without the use of radio or acoustic communication One key to more effective cooperative interaction in a multi-robot team is the ability to understand the behavior and intent of other robots This is similar to the honey bee “waggle dance” in which a bee can communicate the orientation and distance of a food source In this similar manner, our heterogenous multi-robot team uses a specific behavior to indicate the location of mine-like objects (MLOs) Observed teammate action sequences can be learned to perform behavior recognition and task-assignment in the absence of communication We apply Conditional Random Fields (CRFs) to perform behavior recognition as an approach to task monitoring in the absence of communication in a challenging underwater environment In order to demonstrate the use of behavior recognition of an Autonomous Underwater Vehicle (AUV) in a cooperative task, we use trajectory based techniques for model generation and behavior discrimination in experiments using simulated scenario data Results are presented demonstrating heterogenous teammate cooperation between an AUV and an Autonomous Surface Vehicle (ASV) using behavior recognition rather than radio or acoustic communication in a mine clearing task

https://www.cc.gatech.edu/grads/m/mnovitzk/publications/ROBIO2012.pdf

Bio-inspired multi-robot communication through behavior recognition

This paper shows a design-to-simulation approach for tackling the autonomous underwater vehicle navigation problem. Simultaneous Localization and Mapping (SLAM) is a primary research topic in robotics. Efficiently solving the problem of robotic navigation allows for robotic platforms to truly operate autonomously without the need for human in the loop interaction. This problem becomes even more important in underwater environments where traditional navigational aids such as GPS are denied due to the nature of the environment. Autonomous navigation provides the ability to address a much wider array of problems, especially in large scale deployments of AUVs in ocean environments. The goal is to provide Yellowfin, a low-cost, highly-portable AUV for use in littoral and open water environments, a robust and efficient autonomous navigation package. Use of a high frequency imaging sonar for exteroception in the underwater environment is demonstrated as well as simulation results of Extended Kalman Filters and Smoothing and Mapping algorithms for SLAM.

http://ieeexplore.ieee.org/iel5/6093765/6106891/06107019.pdf

Towards autonomous navigation with the Yellowfin AUV

This paper focuses on multi-robot teams working cooperatively in an underwater application. Multi-robot teams working cooperatively to perform multiple tasks simultaneously have the potential to be more robust to failure and efficient when compared to single robot solutions. One key to more effective interaction is the ability to identify the behavior of other agents. However, the underwater environment presents specific challenges to teammate behavior identification. Current decentralized collaboration approaches, such as auction-based methods, degrade in poor communication environments. Sensor information regarding teammates can be leveraged to perform behavior recognition and task-assignment in the absence of communication. This work illustrates the use of Conditional Random Fields (CRFs) to perform behavior recognition as an approach to task monitoring in the absence of robust communication in a challenging underwater environment. In order to demonstrate the feasibility of performing behavior recognition of an AUV in the underwater domain, we use trajectory based techniques for model generation and behavior discrimination in experiments using simulated trajectories and real sonar data. Results are presented with comparison of a CRF method to one using Hidden Markov Models.

/pdf/conditional-random-fields-for-behavior-recognition-of-ci9krly5dm.pdf

Conditional Random Fields for Behavior Recognition of Autonomous Underwater Vehicles

SUMMARY This paper focuses on behavior recognition in an underwater application as a substitute for communicating through acoustic transmissions, which can be unreliable. The importance of this work is that sensor information regarding other agents can be leveraged to perform behavior recognition, which is activity recognition of robots performing specific programmed behaviors, and task-assignment. This work illustrates the use of Behavior Histograms, Hidden Markov Models (HMMs), and Conditional Random Fields (CRFs) to perform behavior recognition. We present challenges associated with using each behavior recognition technique along with results on individually selected test trajectories, from simulated and real sonar data, and real-time recognition through a simulated mission.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0263574713001306

AUV behavior recognition using behavior histograms, HMMs, and CRFs

/pdf/tar-trajectory-adaptation-for-recognition-of-robot-tasks-to-4mygl980j0.pdf

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

/pdf/design-and-development-of-the-yellowfin-uuv-for-homogenous-24fmcocezn.pdf

Evan Sequin

Papers

Design and Development of the Yellowfin UUV for Homogenous Collaborative Missions