Evolutionary Computation for Sensor Planning: The Task Distribution Plan
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Evolutionary Computation for Sensor Planning: The Task Distribution Plan Enrique Dunn Departamento de Electr´onica y Telecomunicaciones, Divisi´on de F´ısica Aplicada, Centro de Investigaci´on Cient´ıfica y de Educaci´on Superior de Ensenada, 22860 Ensenada, BC, Mexico Email: [email protected]
Gustavo Olague Departamento de Ciencias de la Computaci´on, Divisi´on de F´ısica Aplicada, Centro de Investigaci´on Cient´ıfica y de Educaci´on Superior de Ensenada, 22860 Ensenada, BC, Mexico Email: [email protected] Received 29 June 2002 and in revised form 29 November 2002 Autonomous sensor planning is a problem of interest to scientists in the fields of computer vision, robotics, and photogrammetry. In automated visual tasks, a sensing planner must make complex and critical decisions involving sensor placement and the sensing task specification. This paper addresses the problem of specifying sensing tasks for a multiple manipulator workcell given an optimal sensor placement configuration. The problem is conceptually divided in two different phases: activity assignment and tour planning. To solve such problems, an optimization methodology based on evolutionary computation is developed. Operational limitations originated from the workcell configuration are considered using specialized heuristics as well as a floating-point representation based on the random keys approach. Experiments and performance results are presented. Keywords and phrases: sensor planning, evolutionary computing, combinatorial optimization, random keys.
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INTRODUCTION
Sensor planning is a growing research area, which studies the development of sensing strategies for computer vision tasks [1]. The goal of such planning is to determine, as autonomously as possible, a group of sensing actions that lead to the fulfillment of the vision task objectives. This is important because there are environments (i.e., dynamic environments with physical and temporal constraints) and tasks (i.e., scene exploration, highly accurate reconstruction) where the specification of an adequate sensing strategy is not a trivial endeavor. Moreover, an effective planner must make considerations that require complex spatial and temporal reasoning based on a set of mathematical models dependent of the vision task goals [2]. Indeed, difficult numerical and combinatorial problems arise, presenting a rich variety of research opportunities. Our approach is to state such problems in optimization terms and apply evolutionary computation (EC) methodologies in their solution [3]. The problem of visual inspection of a complex threedimensional object requires the acquisition of multiple object images from different viewpoints [4]. Accordingly, to formulate a sensing strategy, an effective planner must consider how the spatial distribution of viewpoints affects a specific
task goal, what an adequate configuration for an individual sensor is, how the sensing actions will be executed. These are the kind of general considerations that call for the use of a flexible computing paradigm like EC. This work
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