Coordinated Formation Sensing (Collaboration with NASA JPL)
CADRE (Cooperative Autonomous Distributed Robotic Exploration) is a lunar technology demonstration mission of multi-agent autonomy on a team of three rovers and a base station. The goal of CADRE is to demonstrate how a team of autonomous rovers, receiving only high-level tasks from Earth, can autonomously explore a region of the Lunar surface, as well as perform a distributed measurement in coordination with a multi-static ground-penetrating radar.
Multi-Modal Mobility Platform (NASA JPL EELS)
Ice worlds are at the forefront of astrobiological interest because of the evidence of subsurface oceans. Enceladus in particular is unique among the icy moons because there are known vent systems that are likely connected to a subsurface ocean, through which the ocean water is ejected to space. An existing study has shown that sending small robots into the vents and directly sampling the ocean water is likely possible. To enable such a mission, NASA’s Jet Propulsion Laboratory is developing a snake-like robot called Exobiology Extant Life Surveyor (EELS) that can navigate Enceladus’ extreme surface and descend an erupting vent to capture unaltered liquid samples and potentially reach the ocean.
DARPA Learning Enabled Introspective Neural Control (ARL/JPL/Caltech)
The Learning Introspective Control (LINC) program aims to develop machine learning-based introspection technologies that enable physical systems, with specific interest in ground vehicles, ships, drone swarms, and robotic systems, to respond to events not predicted at design time. LINC technologies endeavors to update control laws as required in real time while providing guidance and situational awareness to the operator, whether that operator is human or an autonomous controller.
Collaborative On-Orbit Inspection
Inspection or mapping of a target spacecraft in a low Earth orbit using multiple observer spacecraft in stable passive relative orbits (PROs) is a key enabling technology for future space missions. Our guidance and control architecture uses an information gain approach to directly consider the tradeoff between gathered data and fuel/energy cost.
Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems
We present generalized polynomial chaos-based sequential convex programming (gPC-SCP) to compute a suboptimal solution for a continuous-time chance-constrained stochastic nonlinear optimal control (SNOC) problem. The approach enables motion planning for robotic systems under uncertainty. The gPC-SCP method involves two steps. The first step is to derive a surrogate problem of deterministic nonlinear optimal control (DNOC) with convex constraints by using gPC expansion and the distributionally robust convex subset of the chance constraints. The second step is to solve the DNOC problem using sequential convex programming for trajectory generation and control.