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. Co-sponsored by: Wright-Patt Multi-Intelligence Development Consortium (WPMDC), The DOD & DOE Communities Speaker(s): Tim Agenda: 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. Virtual: https://events.vtools.ieee.org/m/384182