Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agentbased control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.
The research described in this paper, focuses on a virtual reality headset system that integrates the Oculus Rift VR headset with a low cost Unmanned Aerial Vehicle (UAV) to allow for drone teleoperation and telepresence using the Robot Operating System (ROS). We developed a system that allows the pilot to fly an AR Drone through natural head movements translated to a set of flight commands. The system is designed to be easy to use for the purposes of training drone pilots. The user simply has to move their head and these movements are translated to the quadrotor which then turns in that direction. Altitude control is implemented using a Wii Nunchuck joystick for altitude adjustment. The users use the Oculus Rift headset a 2D video stream from the AR Drone, which is then turned into a 3D image stream and presented to them on the headset. CCS CONCEPTS Software engineering → Distributed Computing;
On behalf of nomadic users and through the use of computing pocket devices, agents can efficiently operate in wireless networks, cooperate to resolve complex tasks and negotiate to reach agreements while attempting to maximize their utilities. However, the negotiation protocols intelligent agents use are hardly considering several of the requirements evolved after the increasing reliance on mobility. In this paper we present a new negotiation protocol that avoids the use of mediating agents and applies a voting-like mechanism to handle service requests of nomadic users in wireless networks. We examine our approach in a scenario where it is essential for a multi-agent system to establish a chain of mutually attracted agents seeking to fulfill different bartering desires. We compare the results obtained with those produced after using an adjusted version of the Strategic Negotiation Model.
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