Random Finite Set Theory and Optimal Control of Large Collaborative Swarms

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 L2^2 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.Comment: arXiv admin note: text overlap with arXiv:1801.0731

Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory

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) control of large collaborative swarms for control of individual agents. The RFS control formulation assumes that the topology underlying the swarm control is complete and uses the complete graph in a centralized manner. To generalize the control topology in a localized or decentralized manner, sparse LQR is used to sparsify the RFS 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 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 control for large collaborative swarms.Comment: arXiv admin note: text overlap with arXiv:1810.0069

Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps

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

Astrobiology eXploration at Enceladus (AXE): A New Frontiers Mission Concept Study

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