387 research outputs found
Sliding modes in constrained systems control
Abstract—In this paper, a sliding-mode-based design framework
for fully actuated mechanical multibody system is discussed.
The framework is based on the possibility to represent complex
motion as a collection of tasks and to find effective mapping of
the system coordinates that allows decoupling task and constraint
control so one is able to enforce concurrently, or in certain time
succession, the task and the constraints. The approach seems naturally
encompassing the control of motion systems in interaction,
and it allows application to bilateral control, multilateral control,
etc. Such an approach leads to a more natural interpretation of
the system tasks, simpler controller design, and easier establishment
of the systems hierarchy. It allows a unified mathematical
treatment of task control in the presence of constraints required
to be satisfied by the system coordinates. In order to show the
applicability of the proposed techniques, simulation and experimental
results for high-precision systems in microsystem assembly
tasks and bilateral control systems are presented
Clustering-Based Robot Navigation and Control
In robotics, it is essential to model and understand the topologies of configuration spaces in order to design provably correct motion planners. The common practice in motion planning for modelling configuration spaces requires either a global, explicit representation of a configuration space in terms of standard geometric and topological models, or an asymptotically dense collection of sample configurations connected by simple paths, capturing the connectivity of the underlying space. This dissertation introduces the use of clustering for closing the gap between these two complementary approaches. Traditionally an unsupervised learning method, clustering offers automated tools to discover hidden intrinsic structures in generally complex-shaped and high-dimensional configuration spaces of robotic systems. We demonstrate some potential applications of such clustering tools to the problem of feedback motion planning and control.
The first part of the dissertation presents the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. We reinterpret this classical method for unsupervised learning as an abstract 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. Based on this new abstraction and a careful topological characterization of the associated hierarchical structure, a provably correct, computationally efficient hierarchical navigation framework is proposed for collision-free coordinated motion design towards a designated multirobot configuration via a sequence of hierarchy-preserving local controllers.
The second part of the dissertation introduces a new, robot-centric application of Voronoi diagrams to identify a collision-free neighborhood of a robot configuration that captures the local geometric structure of a configuration space around the robot’s instantaneous position. Based on robot-centric Voronoi diagrams, a provably correct, collision-free coverage and congestion control algorithm is proposed for distributed mobile sensing applications of heterogeneous disk-shaped robots; and a sensor-based reactive navigation algorithm is proposed for exact navigation of a disk-shaped robot in forest-like cluttered environments.
These results strongly suggest that clustering is, indeed, an effective approach for automatically extracting intrinsic structures in configuration spaces and that it might play a key role in the design of computationally efficient, provably correct motion planners in complex, high-dimensional configuration spaces
A cooperative multi-agent robotics system: design and modelling
This paper presents the development of the robotic multi-agent system SMART. In this system, the agent
concept is applied to both hardware and software entities. Hardware agents are robots, with three and
four legs, and an IP-camera that takes images of the scene where the cooperative task is carried out. Hardware
agents strongly cooperate with software agents. These latter agents can be classified into image processing,
communications, task management and decision making, planning and trajectory generation agents. To model, control and evaluate the performance of cooperative tasks among agents, a kind of PetriNet, called Work-Flow Petri Net, is used. Experimental results shows the good performance of the system
MOMA: Visual Mobile Marker Odometry
In this paper, we present a cooperative odometry scheme based on the
detection of mobile markers in line with the idea of cooperative positioning
for multiple robots [1]. To this end, we introduce a simple optimization scheme
that realizes visual mobile marker odometry via accurate fixed marker-based
camera positioning and analyse the characteristics of errors inherent to the
method compared to classical fixed marker-based navigation and visual odometry.
In addition, we provide a specific UAV-UGV configuration that allows for
continuous movements of the UAV without doing stops and a minimal
caterpillar-like configuration that works with one UGV alone. Finally, we
present a real-world implementation and evaluation for the proposed UAV-UGV
configuration
A Distributed Pipeline for Scalable, Deconflicted Formation Flying
Reliance on external localization infrastructure and centralized coordination
are main limiting factors for formation flying of vehicles in large numbers and
in unprepared environments. While solutions using onboard localization address
the dependency on external infrastructure, the associated coordination
strategies typically lack collision avoidance and scalability. To address these
shortcomings, we present a unified pipeline with onboard localization and a
distributed, collision-free motion planning strategy that scales to a large
number of vehicles. Since distributed collision avoidance strategies are known
to result in gridlock, we also present a decentralized task assignment solution
to deconflict vehicles. We experimentally validate our pipeline in simulation
and hardware. The results show that our approach for solving the optimization
problem associated with motion planning gives solutions within seconds in cases
where general purpose solvers fail due to high complexity. In addition, our
lightweight assignment strategy leads to successful and quicker formation
convergence in 96-100% of all trials, whereas indefinite gridlocks occur
without it for 33-50% of trials. By enabling large-scale, deconflicted
coordination, this pipeline should help pave the way for anytime, anywhere
deployment of aerial swarms.Comment: 8 main pages, 1 additional page, accepted to RA-L and IROS'2
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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