Using simulations to predict the genetic connectivity of the Mojave desert tortoise

The Mojave desert tortoise is a threatened species that is facing habitat fragmentation from human development. Understanding the impact of fragmentation on this species is critical for developing appropriate conservation actions, but the effects of habitat fragmentation are often delayed, making it difficult to assess the impacts of recent landscape change. One tool often used to predict the impacts of fragmentation are agent-based models, which simulate the behavior and life-history of individual “agents”. Agent-based models allow researchers to investigate the impacts of habitat fragmentation under many scenarios, which is useful for guiding conservation actions. However, because agent-based models are computationally intense, they are often limited to small spatial extents and low numbers of agents – while performing these simulations at large scales could lead to important insights, this is often infeasible.In this dissertation, I use a computationally efficient agent-based model to assess the impact of anthropogenic development on the range-wide genetic connectivity of the Mojave desert tortoise. In Chapter 1, I describe the quadtree R package, which implements the region quadtree data structure in C++ and makes it available to the R programming environment – using this data structure increases the speed of the agent-based model. In Chapter 2, I calibrate and validate an agent-based model for predicting desert tortoise genetic connectivity. In Chapter 3, I use the model to make range-wide projections of the influence of anthropogenic development on desert tortoise genetic connectivity

Path planning for unmanned aerial vehicles using visibility line-based methods

This thesis concerns the development of path planning algorithms for unmanned aerial vehicles (UAVs) to avoid obstacles in two- (2D) and three-dimensional (3D) urban environments based on the visibility graph (VG) method. As VG uses all nodes (vertices) in the environments, it is computationally expensive. The proposed 2D path planning algorithms, on the contrary, select a relatively smaller number of vertices using the so-called base line (BL), thus they are computationally efficient. The computational efficiency of the proposed algorithms is further improved by limiting the BL’s length, which results in an even smaller number of vertices. Simulation results have proven that the proposed 2D path planning algorithms are much faster in comparison with the VG and hence are suitable for real time path planning applications. While vertices can be explicitly defined in 2D environments using VG, it is difficult to determine them in 3D as they are infinite in number at each obstacle’s border edge. This issue is tackled by using the so-called plane rotation approach in the proposed 3D path planning algorithms where the vertices are the intersection points between a plane rotated by certain angles and obstacles edges. In order to ensure that the 3D path planning algorithms are computationally efficient, the proposed 2D path planning algorithms are applied into them. In addition, a software package using Matlab for 2D and 3D path planning has also been developed. The package is designed to be easy to use as well as user-friendly with step-by-step instructions

Planning for Autonomous Operation of Unmanned Surface Vehicles

The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. The autonomous operations of the USV over long time horizons requires a path planner to compute paths over long distances in complex marine environments consisting of hundreds of islands of complex shapes. The available free space in marine environment changes over time as a result of tides, environmental restrictions, and weather. Secondly, the maximum velocity and energy consumption of the USV is significantly influenced by the fluid medium flows such as strong currents. Finally, the USV have to operate in an unfamiliar, unstructured marine environment with obstacles of variable dimensions, shapes, and motion dynamics such as other unmanned surface vehicles, civilian boats, shorelines, or docks poses numerous planning challenges. The proposed Ph.D. dissertation explores the above mentioned problems by developing computationally efficient path and trajectory planning algorithms that enables the long term autonomous operation of the USVs. We have developed a lattice-based 5D trajectory planner for the USVs operating in the environment with the congested civilian traffic. The planner estimates collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. Secondly, we present a computationally efficient and optimal algorithm for long distance path planning in complex marine environments using A* search on visibility graphs defined over quad trees. Finally, we present an A* based path planning algorithm with newly developed admissible heuristics for computing energy efficient paths in environment with significant fluid flows. The effectiveness of the planning algorithms is demonstrated in the simulation environments by using systems identified dynamics model of the wave amplitude modular vessel (WAM-V) USV14