On-orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on-orbit assembly, repair, and astronaut assistance. Of these scenarios, on-orbit assembly is an enabling technology that will allow large space structures to be built in-situ, using smaller building block modules. However, robotic on-orbit assembly involves a number of technical hurdles such as changing system models. For instance, grappled modules moved by a free-flying "assembler" robot can cause significant shifts in system inertial properties, which has cascading impacts on motion planning and control portions of the autonomy stack. Further, on-orbit assembly and other scenarios require collision-avoiding motion planning, particularly when operating in a "construction site" scenario of multiple assembler robots and structures. These complicating factors, relevant to many autonomous microgravity robotics use cases, are tackled in the ReSWARM flight experiments as a set of tests on the International Space Station using NASA's Astrobee robots. RElative Satellite sWarming and Robotic Maneuvering, or ReSWARM, demonstrates multiple key technologies for close proximity operations and on-orbit assembly: (1) global long-horizon planning, accomplished using offline and online sampling-based planner options that consider the system dynamics; (2) on-orbit reconfiguration model learning, using the recently-proposed RATTLE information-aware planning framework; and (3) robust control tools to provide low-level control robustness using current system knowledge. These approaches are detailed individually and in an "on-orbit assembly scenario" of multi-waypoint tracking on-orbit. Additionally, detail is provided discussing the practicalities of hardware implementation and unique aspects of working with Astrobee in microgravity.

The Saturnian moon Enceladus presents a unique opportunity to sample the contents of a subsurface liquid water ocean in situ via the continuous plume formed over its south polar terrain using a multi-flyby mission architecture. Previous analyses of the plume's composition by Cassini revealed an energy-rich system laden with salts and organic compounds, representing an environment containing most of the ingredients for life as we know it. Following in the footsteps of the Cassini-Huygens mission, we present Astrobiology eXploration at Enceladus (AXE), a New Frontiers class Enceladus mission concept study carried out during the 2021 NASA Planetary Science Summer School program at the Jet Propulsion Laboratory, California Institute of Technology. We demonstrate that a scientifically compelling geophysical and life-detection mission to Enceladus can be carried out within the constraints of a New Frontiers-5 cost cap using a modest instrument suite, requiring only a narrow angle, high-resolution telescopic imager, a mass spectrometer, and a high-gain antenna for radio communications and gravity science measurements. Using a multi-flyby mission architecture, AXE would evaluate the habitability and potential for life at Enceladus through a synergistic combination of in situ chemical analysis measurements aimed at directly detecting the presence of molecular biosignatures, along with geophysical and geomorphological investigations to contextualize chemical biosignatures and further evaluate the habitability of Enceladus over geologic time.

Controlling large swarms of robotic agents presents many challenges including but not limited to computational complexity due to a large number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. The contribution of this work is to decentralize random finite set (RFS)-based control of large collaborative swarms for controlling individual agents. The RFS-based control formulation assumes a Gaussian mixture probability hypothesis density approximation and a complete topology for centralized swarm control. To generalize the control topology in a localized or decentralized manner, a sparse linear quadratic regulator (LQR) is used to sparsify the RFS-based control gain matrix obtained using iterative LQR. This allows agents to use information of agents near each other (localized topology) or only the agent's own information (decentralized topology) to make a control decision. Sparsity and performance for decentralized RFS-based control are compared for different degrees of localization in feedback control gains which show that the stability and performance compared to centralized control do not degrade significantly in providing RFS-based control for large collaborative swarms.

As access to space and robotic autonomy capabilities move forward, there is simultaneously a growing interest in deploying large, complex space structures to provide new on-orbit capabilities. New space-borne observatories, large orbital outposts, and even futuristic on-orbit manufacturing will be enabled by robotic assembly of space structures using techniques like on-orbit additive manufacturing which can provide flexibility in constructing and even repairing complex hardware. However, the dynamics underlying the robotic assembler during manipulation may operate under inertial uncertainties. Thus, inertial estimation of the robot and the manipulated component system must be considered during structural assembly. The contribution of this work is to address both the motion planning and control for robotic assembly with consideration of the inertial estimation of the combined free-flying robotic assembler and additively manufactured component system. Specifically, the Linear Quadratic Regulator Rapidly-Exploring Randomized Trees (LQR-RRT*) and dynamically feasible path smoothing are used to obtain obstacle-free trajectories for the system. Further, model learning is incorporated explicitly into the planning stages via approximation of the continuous system and accompanying reward of performing safe, objective-oriented motion. Remaining uncertainty can then be dealt with using robust tube model predictive control. By obtaining controlled trajectories that consider both obstacle avoidance and learning of the inertial properties of the free-flyer and manipulated component system, the free-flyer rapidly considers and plans the construction of space structures with enhanced system knowledge. The approach naturally generalizes to repairing, refueling, and re-provisioning space structure components while providing optimal collision-free trajectories under e.g., inertial uncertainty.

Deploying large, complex space structures is of great interest to the modern scientific world as it can provide new capabilities in obtaining scientific, communicative, and observational information. However, many theoretical mission designs contain complexities that must be constrained by the requirements of the launch vehicle, such as volume and mass. To mitigate such constraints, the use of on-orbit additive manufacturing and robotic assembly allows for the flexibility of building large complex structures including telescopes, space stations, and communication satellites. The contribution of this work is to develop motion planning and control algorithms using the linear quadratic regulator and rapidly-exploring randomized trees (LQR-RRT*), path smoothing, and tracking the trajectory using a closed-loop nonlinear receding horizon control optimizer for a robotic Astrobee free-flyer. By obtaining controlled trajectories that consider obstacle avoidance and dynamics of the vehicle and manipulator, the free-flyer rapidly considers and plans the construction of space structures. The approach is a natural generalization to repairing, refueling, and re-provisioning space structure components while providing optimal collision-free trajectories during operation.

University of Minnesota Ph.D. dissertation. September 2019. Major: Aerospace Engineering and Mechanics. Advisor: Richard Linares. 1 computer file (PDF); xii, 165 pages.

Inverse Reinforcement Learning (RL) can be used to determine the behavior of Space Objects (SOs) by estimating the reward function that an SO is using for control. The approach discussed in this work can be used to analyze maneuvering of SOs from observational data. The inverse RL problem is solved using maximum causal entropy. This approach determines the optimal reward function that a SO is using while maneuvering with random disturbances by assuming that the observed trajectories are optimal with respect to the SO's own reward function. Lastly, this paper develops results for scenarios involving Low Earth Orbit (LEO) station-keeping and Geostationary Orbit (GEO) station-keeping.

Using random finite sets (RFS) for the guidance, navigation, and control (GNC) of autonomous swarms is a recent proposal in literature. However, the overall mission planning has yet to be fully considered, in particular, the translation of natural language mission objectives to suitable RFS objectives and how this could be done autonomously by a mission planner. This paper develops an autonomous mission planner which manages the key mission phases of deployment, execution, return, and recovery for a swarm modeled as a Gaussian Mixture-Probability Hypothesis Density (GM-PHD) in a GM-PHD navigation filter and the use of a Model Predictive Control (MPC) strategy for guidance and control. In particular, supplemental algorithms for using these GNC subroutines in a scalable and systematic way are presented. Lastly, a specific mission planner is developed and simulated for different scenarios for a multi-agent swarm performing a surveillance mission in a hostile environment with centralized guidance, navigation, and planning equipped with a radar-based observer for the swarm.

This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map information fusion method is presented for rapidly merging the information from individual robot maps using limited data communication. A communication strategy that implements data fusion among many robots is also presented for the decentralized computation of GDMs. New entropy-based information-driven path-planning methods are developed and compared to existing approaches, such as particle swarm optimization (PSO) and random walks (RW). Numerical experiments conducted in simulated indoor and outdoor environments show that the information-driven approaches proposed in this paper far outperform other approaches, and avoid mutual collisions in real time.

This paper develops a decentralized approach for mapping and information-driven path planning for Very Large Scale Robotic (VLSR) systems. In this approach, obstacle mapping is performed using a continuous probabilistic representation known as a Hilbert map, which formulates the mapping problem as a binary classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map fusion method is presented that quickly and efficiently combines the information from individual robot maps. An integrated mapping and path planning algorithm is presented to determine paths of maximum information value, while simultaneously performing obstacle avoidance. Furthermore, the effect of how percentage communication failure effects the overall performance of the system is investigated. The approach is demonstrated on a VLSR system with hundreds of robots that must map obstacles collaboratively over a large region of interest using onboard range sensors and no prior obstacle information. The results show that, through fusion and decentralized processing, the entropy of the map decreases over time and robot paths remain collision-free.

Controlling large swarms of robotic agents has many challenges including, but not limited to, computational complexity due to the number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. This work generalizes the swarm state using Random Finite Set (RFS) theory and solves the control problem using model predictive control which naturally handles the challenges. This work uses information divergence to define the distance between swarm RFS and a desired distribution. A stochastic optimal control problem is formulated using a modified L2 2 distance. Simulation results show that swarm densities converge to a target destination, and the RFS control formulation can vary in the number of target destinations.

Controlling large swarms of robotic agents has many challenges including, but not limited to, computational complexity due to the number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. This work generalizes the swarm state using Random Finite Set (RFS) theory and solves the control problem using Model Predictive Control (MPC) to overcome the aforementioned challenges. Computationally efficient solutions are obtained via the Iterative Linear Quadratic Regulator (ILQR). Information divergence is used to define the distance between the swarm RFS and the desired swarm configuration. Then, a stochastic optimal control problem is formulated using a modified L-2(2) distance. Simulation results using MPC and ILQR show that swarm intensities converge to a target destination, and the RFS control formulation can vary in the number of target destinations. ILQR also provides a more computationally efficient solution to the RFS swarm problem when compared to the MPC solution. Lastly, the RFS control solution is applied to a spacecraft relative motion problem showing the viability for this real-world scenario.

The separation principle for control and estimation is the traditional method for stochastic optimal control of linear systems. However, such an approach does not hold true as systems become increasingly nonlinear. This paper presents a new method for control through estimation based on the Path Integral (PI) formulation and the Riemann Manifold Hamiltonian Monte Carlo (RMHMC) method. The approach uses the path integral method to formulate the control problem as an estimation problem and adds the estimation of the model parameters to the problem. Then, a solution is found by solving the estimation problem by Riemann Manifold Hamiltonian Monte Carlo (RMHMC) sampling which includes a solution for the control parameters. By solving the nonlinear control and estimation problem using the path integral method via RMHMC, no open algorithmic tuning parameters other than exploration noise are required and the control solution has numerically robust performance to high dimensionality in the system control input. The methodology is specifically applied to the spacecraft attitude control problem, though it should be noted that such an approach is generalizable. Simulation results are presented which demonstrate good performance when utilizing the PI via RMHMC method.