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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Clustering-Based Robot Navigation And Control, Omur Arslan Aug 2016

Clustering-Based Robot Navigation And Control, Omur Arslan

Departmental Papers (ESE)

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 ...


Clustering-Based Robot Navigation And Control, Omur Arslan, Dan P. Guralnik, Daniel E. Koditschek May 2016

Clustering-Based Robot Navigation And Control, Omur Arslan, Dan P. Guralnik, Daniel E. Koditschek

Departmental Papers (ESE)

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. In this short note, we present an overview of our recent results that utilize clustering for closing the gap between these two complementary approaches. Traditionally an unsupervised learning method, clustering offers automated tools to discover hidden intrinsic structures ...


Exact Robot Navigation Using Power Diagrams, Omur Arslan, Daniel E. Koditschek May 2016

Exact Robot Navigation Using Power Diagrams, Omur Arslan, Daniel E. Koditschek

Departmental Papers (ESE)

We reconsider the problem of reactive navigation in sphere worlds, i.e., the construction of a vector field over a compact, convex Euclidean subset punctured by Euclidean disks, whose flow brings a Euclidean disk robot from all but a zero measure set of initial conditions to a designated point destination, with the guarantee of no collisions along the way. We use power diagrams, generalized Voronoi diagrams with additive weights, to identify the robot’s collision free convex neighborhood, and to generate the value of our proposed candidate solution vector field at any free configuration via evaluation of an associated convex ...


Voronoi-Based Coverage Control Of Heterogeneous Disk-Shaped Robots, Omur Arslan, Daniel E. Koditschek May 2016

Voronoi-Based Coverage Control Of Heterogeneous Disk-Shaped Robots, Omur Arslan, Daniel E. Koditschek

Departmental Papers (ESE)

In distributed mobile sensing applications, networks of agents that are heterogeneous respecting both actuation as well as body and sensory footprint are often modelled by recourse to power diagrams — generalized Voronoi diagrams with additive weights. In this paper we adapt the body power diagram to introduce its “free subdiagram,” generating a vector field planner that solves the combined sensory coverage and collision avoidance problem via continuous evaluation of an associated constrained optimization problem. We propose practical extensions (a heuristic congestion manager that speeds convergence and a lift of the point particle controller to the more practical differential drive kinematics) that ...


Coordinated Robot Navigation Via Hierarchical Clustering, Omur Arslan, Dan P. Guralnik, Daniel E. Koditschek Mar 2016

Coordinated Robot Navigation Via Hierarchical Clustering, Omur Arslan, Dan P. Guralnik, Daniel E. Koditschek

Departmental Papers (ESE)

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 ...