Search Constraints

Start Over
Filtering by: Creator/Author Sathyan, Anoop Remove constraint Creator/Author: Sathyan, Anoop

Search Results

  • An Efficient Genetic Fuzzy Approach to UAV Swarm Routing

    Article thumbnail: An Efficient Genetic Fuzzy Approach to UAV Swarm Routing
    Type:
    Article
    Description/Abstract:
    Fuzzy logic is used in a variety of applications because of its universal approximator attribute and non-linear characteristics. But, it takes a lot of trial and error to come up with a set of membership functions and rule-base that will effectively work for a specific application. This process could be simplified by using a heuristic search algorithm like Genetic Algorithm (GA). In this paper, genetic fuzzy is applied to the task assignment for cooperating Unmanned Aerial Vehicles (UAVs) classified as the polygon visiting multiple traveling salesman problem (PVMTSP). The PVMTSP finds a lot of applications including UAV swarm routing. We propose a method of genetic fuzzy clustering that would be specific to PVMTSP problems and hence more efficient compared to k-means and c-means clustering. We developed two different algorithms using genetic fuzzy. One evaluates the distance covered by each UAV to cluster the search-space and the other uses a cost function that approximates the distance covered thus resulting in a reduced computational time. We compare these two approaches to each other as well as to an already benchmarked fuzzy clustering algorithm which is the current state-of-the-art. We also discuss how well our algorithm scales for increasing number of targets. The results are compared for small and large polygon sizes.
    Creator/Author:
    Cohen, Kelly; Ernest, Nicholas D., and Sathyan, Anoop
    Submitter:
    Kelly Cohen
    Date Uploaded:
    02/13/2017
    Date Modified:
    04/05/2017
    Date Created:
    2015-12
    License:
    All rights reserved

  • Comparison of Approximate Approaches to Solving the Travelling Salesman Problem and its Application to UAV Swarming

    Article thumbnail: Comparison of Approximate Approaches to Solving the Travelling Salesman Problem and its Application to UAV Swarming
    Type:
    Article
    Description/Abstract:
    Comparison of approximate approaches to solving the Travelling Salesman Problem and its application to UAV swarming. International Journal of Unmanned Systems Engineering. 3(1): 1-16. The Travelling Salesman Problem (TSP) is a widely researched Non-deterministic Polynomial-time hard optimization problem with a range of important applications in a wide spectrum of disciplines including aerospace engineering. In this paper, a comparison of different approaches to solve the TSP and also its application towards swarming of UAVs is considered. The objective of the TSP is to determine the optimal route associated with the shortest tour connecting all targets just once. Genetic Algorithms (GA) are one of the most widely applied techniques for solving this class of optimization problems. Two other techniques, 2-opt and Particle Swarm Optimization, are used and the results are compared with those obtained using GA. The comparison is made for different numbers of targets, using salient figures of merit such as computational time required and the cost function which is the minimum solution (distance) obtained. Results show that the 2-opt approach with the closest neighbour as initial starting point for the search yields superior performance. In the Multiple Travelling Salesman Problem, we propose a cluster-first approach which allocates each specific UAV to a subset of targets. The 200 targets are divided into four clusters corresponding to the four UAVs and then TSP algorithms like 2-opt and GA are employed to solve each cluster. This approach drastically reduces the computational time and also gives much better results than the conventional technique of directly applying GA over the 200 targets.
    Creator/Author:
    Cohen, Kelly; Boone, Nathan, and Sathyan, Anoop
    Submitter:
    Kelly Cohen
    Date Uploaded:
    02/03/2017
    Date Modified:
    04/05/2017
    Date Created:
    2015-01
    License:
    All rights reserved