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In this paper, a novel general class of optimality criteria is defined and proposed to solve multi-objective optimization problems by using evolutionary algorithms. These criteria, named p-optimality criteria, allow us to value (assess) the relative importance of those solutions with outstanding performance in very few objectives and poor performance in all others, regarding those solutions with an equilibrium (balance) among all the objectives. The optimality criteria avoid interrelating the relative values of the different objectives, respecting the integrity of each one in a rational way. As an example, a simple multi-objective approach based on the p-optimality criteria and genetic algorithms is designed, where solutions used to generate new solutions are selected according to the proposed optimality criteria. It is implemented and applied on several benchmark test problems, and its performance is compared to that of the nondominated sort genetic algorithm-II method, in order to analyze the contribution and potential of these new optimality criteria. [ABSTRACT FROM PUBLISHER]

Fixtures are devices used in manufacturing systems to locate, immobilize, and hold workpieces. There are many different numerical measures proposed in the literature to quantify the locating accuracy of a fixture, and its capability in firmly holding the workpiece in the presence of external loads. These numerical measures are useful in fixture layout design and the related area of grasp synthesis. As a number of numerical measures are available, a user may face the problem that how to choose an appropriate one as the optimality criterion in practice for fixture design. In this paper, we present a comparative study on several widely used optimality criteria, based on both theoretical analysis and simulation studies. First, we establish a set of inequalities, which describe the relations between several widely used numerical measures. Second, comparisons between optimality criteria are demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal design of multiparameter multisensor systems with suboptimal estimators. For error propagation, the approach makes use of the so-called unscented transformation, which is an efficient way to determine the mean and variance of random distributions that undergo nonlinear transformations. It is demonstrated that a performance improvement can be achieved compared to the designs based on the Fisher information. [ABSTRACT FROM AUTHOR]

Visual feedback can play a crucial role in a dynamic robotic task such as the interception of a moving target. To utilize the feedback effectively, there is a need to develop robot motion planning techniques that also take into account properties of the sensed data. We propose a motion planning framework that achieves this with the help of a space called the perceptual control manifold (PCM) defined on the product of the robot configuration space and an image-based feature space. We show how the task of intercepting a moving target can be mapped to the PCM, using image feature trajectories of the robot end-effector and the moving target. This leads to the generation of motion plans that satisfy various constraints and optimality criteria derived from the robot kinematics, the control system, and the sensing mechanism. Specific interception tasks are analyzed to illustrate this vision-based planning technique. [ABSTRACT FROM PUBLISHER]