References of "Robotics & Autonomous Systems"
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See detailOmnidirectional vision applied to Unmanned Aerial Vehicles (UAVs) attitude and heading estimation
Mondragon, Ivan F.; Campoy, Pascual; Martinez, Carol et al

in Robotics & Autonomous Systems (2010), 58(6), 809-819

This paper presents an aircraft attitude and heading estimator using catadioptric images as a principal sensor for UAV or as a redundant system for IMU (Inertial Measure Unit) and gyro sensors. First, we ... [more ▼]

This paper presents an aircraft attitude and heading estimator using catadioptric images as a principal sensor for UAV or as a redundant system for IMU (Inertial Measure Unit) and gyro sensors. First, we explain how the unified theory for central catadioptric cameras is used for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV's attitude. Then, we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Finally the tests and results using the UAV COLIBRI platform and the validation of them in real flights are presented, comparing the estimated data with the inertial values measured on board. (C) 2010 Elsevier B.V. All rights reserved. [less ▲]

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See detailNon-communicative multi-robot coordination in dynamic environments
Kok, J. R.; Spaan, M. T. J.; Vlassis, Nikos UL

in Robotics & Autonomous Systems (2005), 50(2-3), 99-114

Within a group of cooperating agents the decision making of an individual agent depends on the actions of the other agents. In dynamic environments, these dependencies will change rapidly as a result of ... [more ▼]

Within a group of cooperating agents the decision making of an individual agent depends on the actions of the other agents. In dynamic environments, these dependencies will change rapidly as a result of the continuously changing state. Via a context-specific decomposition of the problem into smaller subproblems, coordination graphs offer scalable solutions to the problem of multiagent decision making. In this work, we apply coordination graphs to a continuous (robotic) domain by assigning roles to the agents and then coordinating the different roles. Moreover, we demonstrate that, with some additional assumptions, an agent can predict the actions of the other agents, rendering communication superfluous. We have successfully implemented the proposed method into our UvA Trilearn simulated robot soccer team which won the RoboCup-2003 World Championship in Padova, Italy. (C) 2004 Elsevier B.V. All rights reserved. [less ▲]

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