A Decomposition Strategy for Optimal Coverage of an Area of Interest using Heterogeneous Team of UAVs

In this thesis, we study the problem of optimal search and coverage with heterogeneous team of unmanned aerial vehicles (UAVs). The team must complete the coverage of a given region while minimizing the required time and fuel for performing the mission. Since the UAVs have different characteristics one needs to equalize the ratio of the task to the capabilities of each agent to obtain an optimal solution. A multi-objective task assignment framework is developed for finding the best group of agents that by assigning the optimal tasks would carry out the mission with minimum total cost. Once the optimal tasks for UAVs are obtained, the coverage area (map) is partitioned according to the results of the multi-objective task assignment. The strategy is to partition the coverage area into separate regions so that each agent is responsible for performing the surveillance of its particular region. The decentralized power diagram algorithm is used to partition the map according to the results of the task assignment phase. Furthermore, a framework for solving the task assignment problem and the coverage area partitioning problem in parallel is proposed. A criterion for achieving the minimum number of turns in covering a region a with single UAV is studied for choosing the proper path direction for each UAV. This criterion is extended to develop a method for partitioning the coverage area among multiple UAVs that minimizes the number of turns. We determine the "best" team for performing the coverage mission and we find the optimal workload for each agent that is involved in the mission through a multi-objective optimization procedure. The search area is then partitioned into disjoint subregions, and each agent is assigned to a region having an optimum area resulting in the minimum cost for the entire surveillance mission

Decentralized Resource Allocation through Constrained Centroidal Voronoi Tessellations

The advancements in the fields of microelectronics facilitate incorporating team elements like coordination into engineering systems through advanced computing power. Such incorporation is useful since many engineering systems can be characterized as a collection of interacting subsystems each having access to local information, making local decisions, interacting with neighbors, and seeking to optimize local objectives that may well conflict with other subsystems, while also trying to optimize certain global objective. In this dissertation, we take advantage of such technological advancements to explore the problem of resource allocation through different aspects of the decentralized architecture like information structure in a team. Introduced in 1968 as a toy example in the field of team decision theory to demonstrate the significance of information structure within a team, the Witsenhausen counterexample remained unsolved until the analytical person-by-person optimal solution was developed within the past decade. We develop a numerical method to implement the optimal laws and show that our laws coincide with the optimal affine laws. For the region where the optimal laws are non-linear, we show that our laws result in the lowest costs when compared with previously reported costs. Recognizing that, in the framework of team decision theory, the difficulties arising from the non-classical information structure within a team currently limit its applicability in real-world applications, we move on to investigating Centroidal Voronoi Tessellations (CVTs) to solve the resource allocation problem. In one-dimensional spaces, a line communication network is sufficient to obtain CVTs in a decentralized manner, while being scalable to any number of agents in the team. We first solve the static resource allocation problem where the amount of resource is fixed. Using such static allocation solution as an initialization step, we solve the dynamic resource allocation problem in a truly decentralized manner. Furthermore, we allow for flexibility in agents\u27 embedding their local preferences through what we call a civility model. We end the dissertation by revisiting the application of Demand-response in smart grids and demonstrate the developed decentralized dynamic resource allocation method to solve the problem of power allocation in a group of building loads

A code excited linear predictive coder: using a moments algorithm

A speech coding algorithm was developed which was based on a new method of selecting the excitation signal from a codebook of residual error sequences. The residual error sequences in the codebook were generated from 512 frames of real speech signals. L.P.C. inverse filtering was used to obtain the residual signal. Each residual error signal was assigned an index. The index was generated using a moments algorithm. These indices were stored on a Graded Binary Tree. A Binary Search was then used to select the correct index. The use of a Graded Binary Tree in the coding algorithm reduced the search time. The algorithm faithfully reproduced the original speech when the test residual error signal was chosen from the training data. When the test residual error signal was outside the training data, synthetic speech of a recognisable quality was produced. Finally, the fundamentals of speech coders are discussed in detail and various developments are suggested