280 research outputs found
Motion Planning for Variable Topology Trusses: Reconfiguration and Locomotion
Truss robots are highly redundant parallel robotic systems that can be
applied in a variety of scenarios. The variable topology truss (VTT) is a class
of modular truss robots. As self-reconfigurable modular robots, a VTT is
composed of many edge modules that can be rearranged into various structures
depending on the task. These robots change their shape by not only controlling
joint positions as with fixed morphology robots, but also reconfiguring the
connectivity between truss members in order to change their topology. The
motion planning problem for VTT robots is difficult due to their varying
morphology, high dimensionality, the high likelihood for self-collision, and
complex motion constraints. In this paper, a new motion planning framework to
dramatically alter the structure of a VTT is presented. It can also be used to
solve locomotion tasks that are much more efficient compared with previous
work. Several test scenarios are used to show its effectiveness. Supplementary
materials are available at https://www.modlabupenn.org/vtt-motion-planning/.Comment: 20 pages, 36 figure
Fast Penetration Depth Estimation for Elastic Bodies
We present a fast penetration depth estimation algorithm between deformable polyhedral objects. We assume the continuum of non-rigid models are discretized using standard techniques, such as finite element or finite difference methods. As the objects deform, the pre-computed distance fields are deformed accordingly to estimate penetration depth, allowing enforcement of non-penetration constraints between two colliding elastic bodies. This approach can automatically handle self-penetration and inter-penetration in a uniform manner. We demonstrate its effectiveness on moderately complex simulation scenes
Sampling-Based Motion Planning: A Comparative Review
Sampling-based motion planning is one of the fundamental paradigms to
generate robot motions, and a cornerstone of robotics research. This
comparative review provides an up-to-date guideline and reference manual for
the use of sampling-based motion planning algorithms. This includes a history
of motion planning, an overview about the most successful planners, and a
discussion on their properties. It is also shown how planners can handle
special cases and how extensions of motion planning can be accommodated. To put
sampling-based motion planning into a larger context, a discussion of
alternative motion generation frameworks is presented which highlights their
respective differences to sampling-based motion planning. Finally, a set of
sampling-based motion planners are compared on 24 challenging planning
problems. This evaluation gives insights into which planners perform well in
which situations and where future research would be required. This comparative
review thereby provides not only a useful reference manual for researchers in
the field, but also a guideline for practitioners to make informed algorithmic
decisions.Comment: 25 pages, 7 figures, Accepted for Volume 7 (2024) of the Annual
Review of Control, Robotics, and Autonomous System
Simultaneous Task Subdivision and Assignment in the FRACTAL Multi-robot System
This paper presents a negotiation protocol for simultaneous task subdivision and assignment in a heterogeneous multi-robot system. The protocol is based on an abstraction of the concept of task that allows it to be applied independently on the actual task, and adopts Rubinsteins’s alternate offers protocol extended with a co-evolutionary step in search for the best (counter)-offer. The protocol has been tested on computer simulated application scenarios
Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies
Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation.
Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety.
In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors.
We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions
LIPIcs, Volume 248, ISAAC 2022, Complete Volume
LIPIcs, Volume 248, ISAAC 2022, Complete Volum
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