Solving Gravity Anomaly Matching Problem Under Large Initial Errors in Gravity Aided Navigation by Using an Affine Transformation Based Artificial Bee Colony Algorithm

Gravity aided inertial navigation system (GAINS), which uses earth gravitational anomaly field for navigation, holds strong potential as an underwater navigation system. The gravity matching algorithm is one of the key factors in GAINS. Existing matching algorithms cannot guarantee the matching accuracy in the matching algorithms based gravity aided navigation when the initial errors are large. Evolutionary algorithms, which are mostly have the ability of global optimality and fast convergence, can be used to solve the gravity matching problem under large initial errors. However, simply applying evolutionary algorithms to GAINS may lead to false matching. Therefore, in order to deal with the underwater gravity matching problem, it is necessary to improve the traditional evolutionary algorithms. In this paper, an affine transformation based artificial bee colony (ABC) algorithm, which can greatly improve the positioning precision under large initial errors condition, is developed. The proposed algorithm introduces affine transformation to both initialization process and evolutionary process of ABC algorithm. The single-point matching strategy is replaced by the strategy of matching a sequence of several consecutive position vectors. In addition, several constraints are introduced to the process of evolution by using the output characteristics of the inertial navigation system (INS). Simulations based on the actual gravity anomaly base map have been performed for the validation of the proposed algorithm

Application of traveling salesman problem in generating a collision-free tool path in drilling

In machining, the tool path is generated according to the workpiece geometry and arrangement of holes. Majority of Computer Aided Manufacturing (CAM) software offer a set of predefined strategies to choose from. These tool paths are mostly far from being the optimum path, specifically for complex geometries with non-flat surfaces. This thesis introduces a new algorithm based on Travelling Salesman Problem (TSP). The proposed local search algorithm generates an optimum collision free tool path in drilling operations. The developed optimization algorithm considers multiple constraints such as location of tool origin and presence of obstacles. Furthermore, a discussion on stopping criteria for the developed algorithm is presented. Obtained results confirm the proposed algorithm is capable of providing optimum collision free path with more than 50% reduction (in given examples) in path length compared to the HSMWorks software

Kinematic arrangement optimization of a quadruped robot with genetic algorithms

Quadruped robots are capable of performing a multitude of tasks like walking, running carrying and jumping. As research on quadruped robots grows, so does the variety of the designs available. These designs are often inspired by nature and finalized around technical constraints that are different for each project. A load carrying robot design will take its inspiration from a mule, while a running robot will use a cheetah-like design. However, this technique might be too broad when approaching a designing process for a quadruped robot aimed to accomplish certain tasks with varying degrees of importance. In order to reach an efficient design with precise link lengths and joint positions, for some specific task at hand, a complex series of problems have to be solved. This thesis proposes to use genetic algorithms to handle the designing process. An approach that mimics the evolutionary process of living beings, genetic algorithms can be used to reach quadruped designs which are optimized for a given task. The task-specific nature of this process is expected to result in more efficient designs than simply mimicking 4 animal structures, since animals are evolved to be efficient in a bigger variety of tasks. To explore this, genetic algorithms are used to optimize the kinematic structure of quadruped robots designed for the tasks of vertical jumping and trotting. The robots are optimized for these two tasks separately and then together. Algorithm results are compared to a relatively more conventional quadruped design