35,450 research outputs found
Physics-based Motion Planning with Temporal Logic Specifications
One of the main foci of robotics is nowadays centered in providing a great
degree of autonomy to robots. A fundamental step in this direction is to give
them the ability to plan in discrete and continuous spaces to find the required
motions to complete a complex task. In this line, some recent approaches
describe tasks with Linear Temporal Logic (LTL) and reason on discrete actions
to guide sampling-based motion planning, with the aim of finding
dynamically-feasible motions that satisfy the temporal-logic task
specifications. The present paper proposes an LTL planning approach enhanced
with the use of ontologies to describe and reason about the task, on the one
hand, and that includes physics-based motion planning to allow the purposeful
manipulation of objects, on the other hand. The proposal has been implemented
and is illustrated with didactic examples with a mobile robot in simple
scenarios where some of the goals are occupied with objects that must be
removed in order to fulfill the task.Comment: The 20th World Congress of the International Federation of Automatic
Control, 9-14 July 201
Multi-Agent Motion Planning and Object Transportation under High Level Goals
This paper presents a hybrid control framework for the motion planning of a
multi-agent system including N robotic agents and M objects, under high level
goals. In particular, we design control protocols that allow the transition of
the agents as well as the transportation of the objects by the agents, among
predefined regions of interest in the workspace. This allows us to abstract the
coupled behavior of the agents and the objects as a finite transition system
and to design a high-level multi-agent plan that satisfies the agents' and the
objects' specifications, given as temporal logic formulas. Simulation results
verify the proposed framework.Comment: To appear in the World Congress of the International Federation of
Automatic Control (IFAC), Toulouse, France, July 201
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
Robust Decentralized Abstractions for Multiple Mobile Manipulators
This paper addresses the problem of decentralized abstractions for multiple
mobile manipulators with 2nd order dynamics. In particular, we propose
decentralized controllers for the navigation of each agent among predefined
regions of interest in the workspace, while guaranteeing at the same time
inter-agent collision avoidance and connectivity maintenance for a subset of
initially connected agents. In that way, the motion of the coupled multi-agent
system is abstracted into multiple finite transition systems for each agent,
which are then suitable for the application of temporal logic-based high level
plans. The proposed methodology is decentralized, since each agent uses local
information based on limited sensing capabilities. Finally, simulation studies
verify the validity of the approach.Comment: Accepted for publication in the IEEE Conference on Decision and
Control, Melbourne, Australia, 201
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