32 research outputs found
Anytime informed path re-planning and optimization for robots in changing environments
In this paper, we propose a path re-planning algorithm that makes robots able
to work in scenarios with moving obstacles. The algorithm switches between a
set of pre-computed paths to avoid collisions with moving obstacles. It also
improves the current path in an anytime fashion. The use of informed sampling
enhances the search speed. Numerical results show the effectiveness of the
strategy in different simulation scenarios.Comment: Submitted to IROS 2021. "This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessible
Probabilistic approach to physical object disentangling
Physically disentangling entangled objects from each other is a problem
encountered in waste segregation or in any task that requires disassembly of
structures. Often there are no object models, and, especially with cluttered
irregularly shaped objects, the robot can not create a model of the scene due
to occlusion. One of our key insights is that based on previous sensory input
we are only interested in moving an object out of the disentanglement around
obstacles. That is, we only need to know where the robot can successfully move
in order to plan the disentangling. Due to the uncertainty we integrate
information about blocked movements into a probability map. The map defines the
probability of the robot successfully moving to a specific configuration. Using
as cost the failure probability of a sequence of movements we can then plan and
execute disentangling iteratively. Since our approach circumvents only
previously encountered obstacles, new movements will yield information about
unknown obstacles that block movement until the robot has learned to circumvent
all obstacles and disentangling succeeds. In the experiments, we use a special
probabilistic version of the Rapidly exploring Random Tree (RRT) algorithm for
planning and demonstrate successful disentanglement of objects both in 2-D and
3-D simulation, and, on a KUKA LBR 7-DOF robot. Moreover, our approach
outperforms baseline methods
Maximum thick paths in static and dynamic environments
AbstractWe consider the problem of finding a large number of disjoint paths for unit disks moving amidst static or dynamic obstacles. The problem is motivated by the capacity estimation problem in air traffic management, in which one must determine how many aircraft can safely move through a domain while avoiding each other and avoiding “no-fly zones” and predicted weather hazards. For the static case we give efficient exact algorithms, based on adapting the “continuous uppermost path” paradigm. As a by-product, we establish a continuous analogue of Menger's Theorem.Next we study the dynamic problem in which the obstacles may move, appear and disappear, and otherwise change with time in a known manner; in addition, the disks are required to enter/exit the domain during prescribed time intervals. Deciding the existence of just one path, even for a 0-radius disk, moving with bounded speed is NP-hard, as shown by Canny and Reif [J. Canny, J.H. Reif, New lower bound techniques for robot motion planning problems, in: Proc. 28th Annu. IEEE Sympos. Found. Comput. Sci., 1987, pp. 49–60]. Moreover, we observe that determining the existence of a given number of paths is hard even if the obstacles are static, and only the entry/exit time intervals are specified for the disks. This motivates studying “dual” approximations, compromising on the radius of the disks and on the maximum speed of motion.Our main result is a pseudopolynomial-time dual-approximation algorithm. If K unit disks, each moving with speed at most 1, can be routed through an environment, our algorithm finds (at least) K paths for disks of radius somewhat smaller than 1 moving with speed somewhat larger than 1