An Evolutionary Algorithm for solving the Two-Dimensional Irregular Shape Packing Problem combined with the Knapsack Problem

This work presents an evolutionary algorithm to solve a joint problem of the Packing Problem and the Knapsack Problem, where the objective is to place items (with shape, value and weight) in a container (defined by its shape and capacity), maximizing the container's value, without intersections

Matching Misaligned Two-Resolution Metrology Data

Multi-resolution metrology devices co-exist in today's manufacturing environment, producing coordinate measurements complementing each other. Typically, the high-resolution device produces a scarce but accurate dataset, whereas the low-resolution one produces a dense but less accurate dataset. Research has shown that combining the two datasets of different resolutions makes better predictions of the geometric features of a manufactured part. A challenge, however, is how to effectively match each high-resolution data point to a low-resolution point that measures approximately the same physical location. A solution to this matching problem appears a prerequisite to a good final prediction. This dissertation solves this metrology matching problem by formulating it as a quadratic integer programming, aiming at minimizing the maximum inter-point-distance difference (maxIPDdiff) among all potential correspondences. Due to the combinatorial nature of the optimization model, solving it to optimality is computationally prohibitive even for a small problem size. In order to solve real-life sized problems within a reasonable amount of time, a two-stage matching framework (TSMF) is proposed. The TSMF approach follows a coarse-to-fine search strategy and consists of down-sampling the full size problem, solving the down-sampled problem to optimality, extending the solution of the down-sampled problem to the full size problem, and refining the solution using iterative local search. Many manufactured parts are designed with symmetric features; that is, many part surfaces are invariant (are mapped to themselves) to certain intrinsic reflections and/or rotations. Dealing with parts surfaces with symmetric features makes the metrology matching problem even more challenging. The new challenge is that, due to this symmetry, alignment performance metrics such as maxIPDdiff and root mean square error are not able to differentiate between (a) correct solutions/correspondences that are orientationally consistent with the underlying true correspondences and (b) incorrect but seemingly correct solutions that can be obtained by applying the surface's intrinsic reflections and/or rotations to a correct set of correspondences. To address this challenge, a filtering procedure is proposed to supplement the TSMF approach. Specifically, the filtering procedure works by generating a solution pool that contains a group of plausible candidate sets of correspondences and subsequently filtering this pool in order to select a correct set of correspondences from the pool. Numerical experiments show that the TSMF approach outperforms two widely-used point set registration alternatives, the iterative closest point (ICP) and coherent point drift methods (CPD), in terms of several performance metrics. Moreover, compared to ICP and CPD, the TSMF approach scales very well as the instance size increases, and is robust with respect to the initial misalignment degree between the two datasets. The numerical results also show that, when enhanced with the proposed filtering procedure, TSMF exhibits much better alignment performance than TSMF without filtering, CPD and ICP in terms of both orientation correctness of the selected solution and several other performance metrics. Furthermore, in terms of computational performance, TSMF (with and without filtering) can solve real-life sized metrology data matching problems within a reasonable amount of time. Therefore, they are both well suitable to serve as an off-line tool in the manufacturing quality control process

Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy

Includes bibliographical references.In this thesis the author investigated the use of a Q-learning based path planning algorithm to investigate how effective it is in saving energy. It is important to pursue any means to save energy in this day and age, due to the excessive exploitation of natural resources and in order to prevent drops in production in industrial environments where less downtime is necessary or other applications where a mobile robot running out of energy can be costly or even disastrous, such as search and rescue operations or dangerous environment navigation. The study was undertaken by implementing a Q-learning based path planning algorithm in several unstructured and unknown environments. A cell decomposition method was used to generate the search space representation of the environments, within which the algorithm operated. The results show that the Q-learning path planner paths on average consumed 3.04% less energy than the A* path planning algorithm, in a square 20% obstacle density environment. The Q-learning path planner consumed on average 5.79% more energy than the least energy paths for the same environment. In the case of rectangular environments, the Q-learning path planning algorithm uses 1.68% less energy, than the A* path algorithm and 3.26 % more energy than the least energy paths. The implication of this study is to highlight the need for the use of learning algorithm in attempting to solve problems whose existing solutions are not learning based, in order to obtain better solutions

RISK-BASED MULTIOBJECTIVE PATH PLANNING AND DESIGN OPTIMIZATION FOR UNMANNED AERIAL VEHICLES

Safe operation of unmanned aerial vehicles (UAVs) over populated areas requires reducing the risk posed by a UAV if it crashed during its operation. We considered several types of UAV risk-based path planning problems and developed techniques for estimating the risk to third parties on the ground. The path planning problem requires making trade-offs between risk and flight time. Four optimization approaches for solving the problem were tested; a network-based approach that used a greedy algorithm to improve the original solution generated the best solutions with the least computational effort. Additionally, an approach for solving a combined design and path planning problems was developed and tested. This approach was extended to solve robust risk-based path planning problem in which uncertainty about wind conditions would affect the risk posed by a UAV

Humanoid Robot NAO : developing behaviours for soccer humanoid robots

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Energy Based Multi-Model Fitting and Matching Problems

Feature matching and model fitting are fundamental problems in multi-view geometry. They are chicken-&-egg problems: if models are known it is easier to find matches and vice versa. Standard multi-view geometry techniques sequentially solve feature matching and model fitting as two independent problems after making fairly restrictive assumptions. For example, matching methods rely on strong discriminative power of feature descriptors, which fail for stereo images with repetitive textures or wide baseline. Also, model fitting methods assume given feature matches, which are not known a priori. Moreover, when data supports multiple models the fitting problem becomes challenging even with known matches and current methods commonly use heuristics. One of the main contributions of this thesis is a joint formulation of fitting and matching problems. We are first to introduce an objective function combining both matching and multi-model estimation. We also propose an approximation algorithm for the corresponding NP-hard optimization problem using block-coordinate descent with respect to matching and model fitting variables. For fixed models, our method uses min-cost-max-flow based algorithm to solve a generalization of a linear assignment problem with label cost (sparsity constraint). Fixed matching case reduces to multi-model fitting subproblem, which is interesting in its own right. In contrast to standard heuristic approaches, we introduce global objective functions for multi-model fitting using various forms of regularization (spatial smoothness and sparsity) and propose a graph-cut based optimization algorithm, PEaRL. Experimental results show that our proposed mathematical formulations and optimization algorithms improve the accuracy and robustness of model estimation over the state-of-the-art in computer vision

DISTORTION-CONTROLLED ISOTROPIC SWELLING AND SELF-ASSEMBLY OF TRIPLY-PERIODIC MINIMAL SURFACES

In the first part of this thesis, I propose a method that allows us to construct optimal swelling patterns that are compatible with experimental constraints. This is done using a greedy algorithm that systematically increases the perimeter of the target surface with the help of minimum length cuts. This reduces the areal distortion that comes from the changing Gaussian curvature of the sheet. The results of our greedy cutting algorithm are tested on surfaces of constant and varying Gaussian curvature, and are additionally validated with finite thickness simulations using a modified Seung-Nelson model. In the second part of the thesis, we focus on self-assembly methods as an alternate approach to program specific desired structures. More specifically, we develop theoretical design rules for triply-periodic minimal surfaces (TPMS) and show how their symmetry properties can be used to program a minimum number triangular particle-types that successfully coalesce into the TPMS shape. We finally simulate our design rules with Monte Carlo methods and study the robustness of the self-assembled structures upon changing different system parameters like elastic moduli