Tim of AFIT

This work focuses on path planning for autonomous agents, leveraging multiple sensing domains to provide navigation solutions in contested environments. The emphasis is on mutli-objective optimization, finding optimal path costs that minimize uncertainty in the goal region.  The algorithm developed is based on the Rapidly-exploring Random Tree (RRT) probabilistic planning algorithm, but extends into the belief space to plan over uncertainty. The Rapidly-exploring Random Belief Alt-Nav Graph (RRBANG) leverages the probabilistic guarantees of the RRT-based algorithms, ensuring the properties for probabilistic completeness and asymptotic optimality. The algorithm is designed to be agent and measurement model agnostic, but specifically how complementary navigation techniques obtain their measurements when developing plans within a complex environment. The algorithm provides an offline, initial plan for an agent given a priori world information. There are several, significant planned avenues for advancement, targeting the algorithm itself, extending to implement real-time dynamic re-planning, as well as benchmarking against other belief space planning (BSP) algorithms.  Captain Machin will also discuss several ANT center research efforts focused on pushing Autonomy.

Biography:

Captain Timothy Machin is currently assigned to the Air Force Institute of Technology (AFIT), where he serves as an Assistant Professor of Electrical Engineering. He is currently the section lead for the General E.E. students, leading 20+ students. He is heavily involved with the Autonomy and Navigation Technology (ANT) Center, wherein he is the Autonomy Lead, pushing research for autonomous agent integration with complementary navigation methodologies. He teaches courses related to guidance, navigation, and control, practical machine learning, and autonomous path planning in complex environments.  Capt Machin was commissioned via the Reserve Officer Training Corps (ROTC) in May of 2014.  A developmental engineer, Capt Machin expanded his academic knowledge by attending the Air Force Institute of Technology (AFIT) through the direct-ascension program. He earned a Master’s of Science in Electrical Engineering focusing on UAS navigation in GPS-denied environments. He then supported intel efforts for communication satellites at NASIC, before moving to the Air Force Research Laboratories, Sensors Directorate, wherein he served as deputy branch chief for the Sensing Management Branch, program manager for several contracts, and test director for the Small Unmanned System EXploitation (SUSEX) program. He then returned to AFIT, where he earned his Doctorate in Philosophy for Electrical Engineering in March 2023, with an emphasis on artificial intelligence, machine learning, and path planning for autonomous agents in contested environments.