1,800 research outputs found
Artificial Intelligence and Systems Theory: Applied to Cooperative Robots
This paper describes an approach to the design of a population of cooperative
robots based on concepts borrowed from Systems Theory and Artificial
Intelligence. The research has been developed under the SocRob project, carried
out by the Intelligent Systems Laboratory at the Institute for Systems and
Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the
project stands both for "Society of Robots" and "Soccer Robots", the case study
where we are testing our population of robots. Designing soccer robots is a
very challenging problem, where the robots must act not only to shoot a ball
towards the goal, but also to detect and avoid static (walls, stopped robots)
and dynamic (moving robots) obstacles. Furthermore, they must cooperate to
defeat an opposing team. Our past and current research in soccer robotics
includes cooperative sensor fusion for world modeling, object recognition and
tracking, robot navigation, multi-robot distributed task planning and
coordination, including cooperative reinforcement learning in cooperative and
adversarial environments, and behavior-based architectures for real time task
execution of cooperating robot teams
Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery
State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task behavior with autonomous-planning agents exploiting the knowledge. However, as autonomous systems are not immune to errors under perceptual difficulty, a human-in-the-loop is needed to bias autonomous-planning towards recovery conditions that resume the task and avoid similar errors. In this work, we explore interactive techniques allowing non-technical users to model task behaviors and perceive cooperatively with a service robot under robot-centric collaboration. We evaluate stylus and touch modalities that users can intuitively and effectively convey natural abstractions of high-level tasks, semantic revisions, and geometries about the world. Experiments are conducted with \u27pick-and-place\u27 tasks in an ideal \u27Blocks World\u27 environment using a Kinova JACO six degree-of-freedom manipulator. Possibilities for the architecture and interface are demonstrated with the following features; (1) Semantic \u27Object\u27 and \u27Location\u27 grounding that describe function and ambiguous geometries (2) Task specification with an unordered list of goal predicates, and (3) Guiding task recovery with implied scene geometries and trajectory via symmetry cues and configuration space abstraction. Empirical results from four user studies show our interface was much preferred than the control condition, demonstrating high learnability and ease-of-use that enable our non-technical participants to model complex tasks, provide effective recovery assistance, and teleoperative control
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Cooperative localization for autonomous underwater vehicles
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2009Self-localization of an underwater vehicle is particularly challenging due to the absence
of Global Positioning System (GPS) reception or features at known positions
that could otherwise have been used for position computation. Thus Autonomous
Underwater Vehicle (AUV) applications typically require the pre-deployment of a set
of beacons.
This thesis examines the scenario in which the members of a group of AUVs
exchange navigation information with one another so as to improve their individual
position estimates.
We describe how the underwater environment poses unique challenges to vehicle
navigation not encountered in other environments in which robots operate and how
cooperation can improve the performance of self-localization. As intra-vehicle communication
is crucial to cooperation, we also address the constraints of the communication
channel and the effect that these constraints have on the design of cooperation
strategies.
The classical approaches to underwater self-localization of a single vehicle, as
well as more recently developed techniques are presented. We then examine how
methods used for cooperating land-vehicles can be transferred to the underwater
domain. An algorithm for distributed self-localization, which is designed to take the
specific characteristics of the environment into account, is proposed.
We also address how correlated position estimates of cooperating vehicles can lead
to overconfidence in individual position estimates.
Finally, key to any successful cooperative navigation strategy is the incorporation
of the relative positioning between vehicles. The performance of localization
algorithms with different geometries is analyzed and a distributed algorithm for the
dynamic positioning of vehicles, which serve as dedicated navigation beacons for a
fleet of AUVs, is proposed.This work was funded by Office of Naval Research grants N00014-97-1-0202,
N00014-05-1-0255, N00014-02-C-0210, N00014-07-1-1102 and the ASAP MURI
program led by Naomi Leonard of Princeton University
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