6,626 research outputs found
Inverse Optimal Planning for Air Traffic Control
We envision a system that concisely describes the rules of air traffic
control, assists human operators and supports dense autonomous air traffic
around commercial airports. We develop a method to learn the rules of air
traffic control from real data as a cost function via maximum entropy inverse
reinforcement learning. This cost function is used as a penalty for a
search-based motion planning method that discretizes both the control and the
state space. We illustrate the methodology by showing that our approach can
learn to imitate the airport arrival routes and separation rules of dense
commercial air traffic. The resulting trajectories are shown to be safe,
feasible, and efficient
Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms
Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-
Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot
We address the problem of autonomously learning controllers for
vision-capable mobile robots. We extend McCallum's (1995) Nearest-Sequence
Memory algorithm to allow for general metrics over state-action trajectories.
We demonstrate the feasibility of our approach by successfully running our
algorithm on a real mobile robot. The algorithm is novel and unique in that it
(a) explores the environment and learns directly on a mobile robot without
using a hand-made computer model as an intermediate step, (b) does not require
manual discretization of the sensor input space, (c) works in piecewise
continuous perceptual spaces, and (d) copes with partial observability.
Together this allows learning from much less experience compared to previous
methods.Comment: 14 pages, 8 figure
Real-time computation of distance to dynamic obstacles with multiple depth sensors
We present an efficient method to evaluate distances between dynamic obstacles and a number of points of interests (e.g., placed on the links of a robot) when using multiple depth cameras. A depth-space oriented discretization of the Cartesian space is introduced that represents at best the workspace monitored by a depth camera, including occluded points. A depth grid map can be initialized off line from the arrangement of the multiple depth cameras, and its peculiar search characteristics allows fusing on line the information given by the multiple sensors in a very simple and fast way. The real-time performance of the proposed approach is shown by means of collision avoidance experiments where two Kinect sensors monitor a human-robot coexistence task
Task-based adaptive multiresolution for time-space multi-scale reaction-diffusion systems on multi-core architectures
A new solver featuring time-space adaptation and error control has been
recently introduced to tackle the numerical solution of stiff
reaction-diffusion systems. Based on operator splitting, finite volume adaptive
multiresolution and high order time integrators with specific stability
properties for each operator, this strategy yields high computational
efficiency for large multidimensional computations on standard architectures
such as powerful workstations. However, the data structure of the original
implementation, based on trees of pointers, provides limited opportunities for
efficiency enhancements, while posing serious challenges in terms of parallel
programming and load balancing. The present contribution proposes a new
implementation of the whole set of numerical methods including Radau5 and
ROCK4, relying on a fully different data structure together with the use of a
specific library, TBB, for shared-memory, task-based parallelism with
work-stealing. The performance of our implementation is assessed in a series of
test-cases of increasing difficulty in two and three dimensions on multi-core
and many-core architectures, demonstrating high scalability
Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance
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