Some Discussions about the Error Functions on SO(3) and SE(3) for the Guidance of a UAV Using the Screw Algebra Theory

In this paper a new error function designed on 3-dimensional special Euclidean group SE(3) is proposed for the guidance of a UAV (Unmanned Aerial Vehicle). In the beginning, a detailed 6-DOF (Degree of Freedom) aircraft model is formulated including 12 nonlinear differential equations. Secondly the definitions of the adjoint representations are presented to establish the relationships of the Lie groups SO(3) and SE(3) and their Lie algebras so(3) and se(3). After that the general situation of the differential equations with matrices belonging to SO(3) and SE(3) is presented. According to these equations the features of the error function on SO(3) are discussed. Then an error function on SE(3) is devised which creates a new way of error functions constructing. In the simulation a trajectory tracking example is given with a target trajectory being a curve of elliptic cylinder helix. The result shows that a better tracking performance is obtained with the new devised error function

Cognitive UAS Path-Planning for Large Spatial Search

Search and Rescue/Destroy missions are some of the most high-risk situations in modern engineering. Every mission nearly always presents a life or death scenario for one or more individuals, with the penalty for failure often being human lives. Modern Search and Rescue/Destroy missions implement the use of autonomous systems in the form of giving an unmanned autonomous aerial system(s) the task of searching a given area in the attempt of discovering one or more objects of interest. Though this ingenuity has already benefited the line of work, these unmanned systems are still not being used to their full potential. Some means of planning how to search the area must be developed, with the most basic means of accomplishing this task being creating a predefined path which is guaranteed to cover all known areas. To increase the rate of success and decrease necessary search time, a pseudo-random search method, known as meta-heuristics, is used to develop a new path planning algorithm to search the field in an intelligent manner. This work develops a means of turning meta-heuristic optimization into a cognitive navigation with autonomous path-planning algorithm that is decoupled from apriori information, with minimal requirements for initiation. To account for the higher performance requirements of such a method, novel guidance methods were developed to meet said demands. Simulations suggest that the proposed search method performs better on average than the current accepted basis

Mission-Oriented Autonomy for Intelligent, Adaptive, and Multi-Agent Remote Sensing of Ice Sheets using Unmanned Aerial Systems

Throughout our history, humanity has been developing and progressing technology in order to help us better understand the world in which we live. As climate change becomes an increasingly urgent global crisis, scientists have been tasked with developing models for better understanding the complex dynamics involved, as well as to more accurately forecast the long term effects on our environment. With respect to sea level rise, both our knowledge of these dynamics and the accuracy of these models can be improved through the routine collection of crucial data concerning glacier ice thickness and bedrock topology. To accomplish this, innovative solutions are being developed by groups of inter-disciplinary research teams, combining fields such as earth-science, radar systems, data science, and aerospace engineering. Through this collaboration, we have the potential to leverage breakthroughs in unmanned systems technology and miniaturized, specialized sensors for comprehensive, precise, and routine data collection of key polar research objectives. As Unmanned Aerial Systems (UASs) have become more reliable research platforms in recent years, they now have the capability to perform these remote sensing operations at a reduced cost compared to manned operations, while also providing repeatable, precision tracking capabilities along flight lines, enabling the surveying of tightly-spaced grids, and removing human flight crews from hazardous polar environments. However, the payload, range, and wind constraints for these platforms severely restrict their operational sensing footprint. Additionally, UASs generally have a much smaller wingspan compared to manned aircraft typically used in Earth Science missions, which becomes a challenging factor for incorporating efficient directive antennas at the low operating frequencies required for glacial sounding. The aim of this work is to address these issues and to enhance mission efficiency and the overall quality of data collection for these operations through the implementation of onboard mission-oriented autonomy that includes cognitive decision-making for intelligent survey operations, adaptive functionalities, and a scalable, robust framework for multi-agent operations. As opposed to conventional methods for polar research operations which generally involve single-agent missions, using standard waypoint guidance and fixed-routes planned by human operators, the unique contributions of the developed mission-oriented autonomy in this work include: 1) Automated flight line generation for rapid and reliable mission planning of tightly-spaced flight lines required for cross-track synthetic aperture radar processes and surface clutter suppression, with required spacing based on the operating frequency of the onboard radar system. 2) Implementation of Dubins Path guidance methods into polar research operations for precision end-to-end survey of mission flight lines while taking into account the kinematic constraints of the fixed wing aircraft, as well as for efficiently traversing to and from a home loiter location during mission operations. 3) Cognitive, real-time optimal path planning through mission flight lines utilizing both deterministic and stochastic Traveling Salesman Problem heuristics. 4) Modifications to these Traveling Salesman Problem heuristics for ensuring safe, feasible, and reliable operations in real-time by taking into account aircraft range constraints. 5) Collaborative Multi-Agent survey operations utilizing space partitioning and Hungarian Assignment for distributed task allocation, as well as morphing potential fields for collision avoidance. 6) Modifications for Multi-Agent deployment scheduling to reduce inter-agent interference for sensitive radar systems to improve coherency of the collected data, and to rapidly and efficiently deploy agents into and out of survey areas. 7) Modifications for Heterogeneous flight operations for increasing operational capabilities through cross-platform collaboration. 8) Failsafe features to instill robustness in Multi-Agent operations with respect towards accommodating and adapting to single-agent system failures, by automatically re-planning collaborative survey operations. In this work, the motivation for the creation of this mission-oriented autonomy is discussed, along with the methodology of each of the autonomy features, and the framework for implementation onto UAS platforms. Case studies are conducted for past and future polar research deployments using unmanned systems to assess the potential improvements in operational capabilities and data collection for the developed autonomy compared to conventional methods. Finally, the developed autonomy is implemented onto an embedded system for preliminary flight testing and validation, as well as used for intelligent mission planning for a manned operation