30,570 research outputs found
A Model-Predictive Motion Planner for the IARA Autonomous Car
We present the Model-Predictive Motion Planner (MPMP) of the Intelligent
Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses
a path planner to compute a path from its current position to the desired
destination. Using this path, the current position, a goal in the path and a
map, IARA's MPMP is able to compute smooth trajectories from its current
position to the goal in less than 50 ms. MPMP computes the poses of these
trajectories so that they follow the path closely and, at the same time, are at
a safe distance of eventual obstacles. Our experiments have shown that MPMP is
able to compute trajectories that precisely follow a path produced by a Human
driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of
up to 32.4 km/h (9 m/s).Comment: This is a preprint. Accepted by 2017 IEEE International Conference on
Robotics and Automation (ICRA
Optimal control of the heave motion of marine cable subsea-unit systems
One of the key problems associated with subsea operations involving tethered subsea units is the motions of support vessels on the ocean surface which can be transmitted to the subsea unit through the cable and increase the tension. In this paper, a theoretical approach for heave compensation is developed. After proper modelling of each element of the system, which includes the cable/subsea-unit, the onboard winch, control theory is applied to design an optimal control law. Numerical simulations are carried out, and it is found that the proposed active control scheme appears to be a promising solution to the problem of heave compensation
Data-Driven Approach to Simulating Realistic Human Joint Constraints
Modeling realistic human joint limits is important for applications involving
physical human-robot interaction. However, setting appropriate human joint
limits is challenging because it is pose-dependent: the range of joint motion
varies depending on the positions of other bones. The paper introduces a new
technique to accurately simulate human joint limits in physics simulation. We
propose to learn an implicit equation to represent the boundary of valid human
joint configurations from real human data. The function in the implicit
equation is represented by a fully connected neural network whose gradients can
be efficiently computed via back-propagation. Using gradients, we can
efficiently enforce realistic human joint limits through constraint forces in a
physics engine or as constraints in an optimization problem.Comment: To appear at ICRA 2018; 6 pages, 9 figures; for associated video, see
https://youtu.be/wzkoE7wCbu
Provably Safe Robot Navigation with Obstacle Uncertainty
As drones and autonomous cars become more widespread it is becoming
increasingly important that robots can operate safely under realistic
conditions. The noisy information fed into real systems means that robots must
use estimates of the environment to plan navigation. Efficiently guaranteeing
that the resulting motion plans are safe under these circumstances has proved
difficult. We examine how to guarantee that a trajectory or policy is safe with
only imperfect observations of the environment. We examine the implications of
various mathematical formalisms of safety and arrive at a mathematical notion
of safety of a long-term execution, even when conditioned on observational
information. We present efficient algorithms that can prove that trajectories
or policies are safe with much tighter bounds than in previous work. Notably,
the complexity of the environment does not affect our methods ability to
evaluate if a trajectory or policy is safe. We then use these safety checking
methods to design a safe variant of the RRT planning algorithm.Comment: RSS 201
Coordinated Robot Navigation via Hierarchical Clustering
We introduce the use of hierarchical clustering for relaxed, deterministic
coordination and control of multiple robots. Traditionally an unsupervised
learning method, hierarchical clustering offers a formalism for identifying and
representing spatially cohesive and segregated robot groups at different
resolutions by relating the continuous space of configurations to the
combinatorial space of trees. We formalize and exploit this relation,
developing computationally effective reactive algorithms for navigating through
the combinatorial space in concert with geometric realizations for a particular
choice of hierarchical clustering method. These constructions yield
computationally effective vector field planners for both hierarchically
invariant as well as transitional navigation in the configuration space. We
apply these methods to the centralized coordination and control of
perfectly sensed and actuated Euclidean spheres in a -dimensional ambient
space (for arbitrary and ). Given a desired configuration supporting a
desired hierarchy, we construct a hybrid controller which is quadratic in
and algebraic in and prove that its execution brings all but a measure zero
set of initial configurations to the desired goal with the guarantee of no
collisions along the way.Comment: 29 pages, 13 figures, 8 tables, extended version of a paper in
preparation for submission to a journa
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