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Distance determination algorithms for convex and concave objects

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dc.contributor.author Carretero G., Juan Antonio
dc.date.accessioned 2018-11-14T00:12:30Z
dc.date.available 2018-11-14T00:12:30Z
dc.date.copyright 2001 en_US
dc.date.issued 2018-11-13
dc.identifier.uri https://dspace.library.uvic.ca//handle/1828/10297
dc.description.abstract Determining the minimum distance between two objects is a problem that has been solved using many different approaches. Most methods proposed so far are, in essence, limited to solve the problem amongst convex polyhedra. Thus, to deal with concave objects, these methods partition concave objects into convex sub-objects and solve the convex problem between all possible sub-object combinations. This adds a large computational expense, especially when the concave objects in the scene are complicated, or when concave quadratically bound objects are to be linearized. In this work, two optimization-based formulations are proposed to solve the minimum distance problem without the need for partitioning concave objects into convex sub-objects. The first one, referred to as the continuous approach, uses concepts of computational solid geometry in order to represent objects with concavities. On the other hand, in the second formulation, referred to as the combinatorial approach, the geometries of the objects are replaced by large sets of points arranged in surface meshes. Since the optimization problem is not unimodal (i.e., has more than one local minimum point), global optimization techniques are used. Simulated Annealing and Genetic Algorithms, with constraint handling techniques such as penalty and repair strategies are used in the continuous approach. In order to eliminate the computational expense of determining the feasibility of every trial point, the combinatorial approach replaces the objects' geometry by a set of points on the surface of each object. This reduces the minimum distance problem to an unconstrained combinatorial optimization problem where the combination of points (one on each object) that minimizes the distance between objects is the solution. Additionally, Genetic Algorithms with niche formation techniques were developed in order to allow the distance algorithm to track multiple minima. In a series of numerical examples, a preliminary implementation of the proposed algorithms has proven to be robust and equivalent, in terms of computational efficiency, to some conventional approaches. en_US
dc.language English eng
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject Robotics en_US
dc.subject Algorithms en_US
dc.subject Continuous approach en_US
dc.subject Combinatorial approach en_US
dc.title Distance determination algorithms for convex and concave objects en_US
dc.type Thesis en_US
dc.contributor.supervisor Nahon, Meyer A.
dc.degree.department Department of Mechanical Engineering en_US
dc.degree.level Doctor of Philosophy Ph.D. en_US
dc.description.scholarlevel Graduate en_US


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