The visibility-aware path planner addresses the problem of path planning for target visibility. It computes sequences of motions that afford a line of sight to a stationary visual target for sensors on a robotic platform. The visibility-aware planner uses a model of the visible region, namely, the region of the task space in which a line of sight exists to the target. The planner also takes the orientation of the sensor into account, utilizing a model of the field of view frustum. The planner applies a penalty to paths that cause the sensor to lose target visibility by exiting the visible region or rotating so the target is not in the field of view. The planner applies these penalties to the edges in a probabilistic roadmap, providing weights in the roadmap graph for graph-search based planning algorithms. This thesis presents two variants on the planner. The static multi-query planner precomputes penalties for all roadmap edges and performs a best-path search using Dijkstra's algorithm. The dynamic single-query planner uses an iterative test-and-reject search to find paths of acceptable penalty without the benefit of precomputation. Four experiments are presented which validate the planners and present examples of the path planning for visibility on 6-DOF robot manipulators. The algorithms are statistically tested with multiple queries. Results show that the planner finds paths with significantly lower losses of target visibility than existing shortest-path planners.

The visibility-aware path planner addresses the problem of path planning for target visibility. It computes sequences of motions that afford a line of sight to a stationary visual target for sensors on a robotic platform. The visibility-aware planner uses a model of the visible region, namely, the region of the task space in which a line of sight exists to the target. The planner also takes the orientation of the sensor into account, utilizing a model of the field of view frustum. The planner applies a penalty to paths that cause the sensor to lose target visibility by exiting the visible region or rotating so the target is not in the field of view. The planner applies these penalties to the edges in a probabilistic roadmap, providing weights in the roadmap graph for graph-search based planning algorithms. This thesis presents two variants on the planner. The static multi-query planner precomputes penalties for all roadmap edges and performs a best-path search using Dijkstra's algorithm. The dynamic single-query planner uses an iterative test-and-reject search to find paths of acceptable penalty without the benefit of precomputation. Four experiments are presented which validate the planners and present examples of the path planning for visibility on 6-DOF robot manipulators. The algorithms are statistically tested with multiple queries. Results show that the planner finds paths with significantly lower losses of target visibility than existing shortest-path planners.

The visibility-aware path planner addresses the problem of path planning for target visibility. It computes sequences of motions that afford a line of sight to a stationary visual target for sensors on a robotic platform. The visibility-aware planner uses a model of the visible region, namely, the region of the task space in which a line of sight exists to the target. The planner also takes the orientation of the sensor into account, utilizing a model of the field of view frustum. The planner applies a penalty to paths that cause the sensor to lose target visibility by exiting the visible region or rotating so the target is not in the field of view. The planner applies these penalties to the edges in a probabilistic roadmap, providing weights in the roadmap graph for graph-search based planning algorithms. This thesis presents two variants on the planner. The static multi-query planner precomputes penalties for all roadmap edges and performs a best-path search using Dijkstra's algorithm. The dynamic single-query planner uses an iterative test-and-reject search to find paths of acceptable penalty without the benefit of precomputation. Four experiments are presented which validate the planners and present examples of the path planning for visibility on 6-DOF robot manipulators. The algorithms are statistically tested with multiple queries. Results show that the planner finds paths with significantly lower losses of target visibility than existing shortest-path planners. / Science, Faculty of / Computer Science, Department of / Graduate

The objective of this thesis was to select a star for observation and determine the error in the retrieval technique for a rocket experiment to measure lower thermospheric Nitric Oxide in the polar night using stellar occultation technique. These objectives are accomplished by planning the geometry, determining the requirements for observations, window for launch and discussing the retrieval technique. The planning is carried out using an approximated (no drag) and simulated rocket trajectory (provided by NSROC: NASA Rocket Operations Contract). The simulation for the retrievals is done using data from Student Nitric Oxide Explorer. Stars were taken from a catalogue called TD1. Launch times were obtained from the geometry planned resulting from selecting a zenith angle after choosing a maximum occultation height and determining rocket apogee. Window for observing Spica was found to be 20 minutes. The retrieval technique and simulations showed that column densities and volume densities should be retrievable to less than 5% and 20% respectively observing occultation heights 90-120km. The study suggests that choosing a star positioned north w.r.t the observation location gives us more poleward latitudes and larger launch window. Future research can be carried out applying the stellar occultation and retrieval technique to a satellite. / Master of Science