Browsing by Subject "Random Finite Sets"
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Item Multiple Target Tracking Using Random Finite Sets(2021-01) Siew, Peng MunMultiple target tracking (MTT) plays a crucial role in guidance, navigation, and control of autonomous systems. However, it presents challenges in terms of computational complexity, measurement-to-track association ambiguity, clutter, and miss detection. The first half of the dissertation looks into multiple extended target tracking on a moving platform using cameras and a Light Detection and Ranging (LiDAR) scanner. A Bayesian framework is first designed for simultaneous localization and mapping and detection of dynamic objects. Two random finite sets filters are developed to track the extracted dynamic objects. First, the Occupancy Grid (OG) Gaussian Mixture (GM) Probability Hypothesis Density (PHD) filter jointly tracks the target kinematic states and a modified occupancy grid map representation of the target shape. The OG-GM-PHD filter successfully reconstructed the shape of the targets and resulted in a lower Optimal Sub-Pattern Assignment (OSPA) error metric than the traditional GM-PHD filter. The second MTT filter (Classifying Multiple Model (CMM) Labeled Multi Bernoulli (LMB)) is developed to leverage class-dependent motion characteristics. It fuses classification data from images to point cloud and incorporates object class probabilities into the tracked target states. This allows for better measurement-to-track associations and usage of class-dependent motion and birth models. The CMM-LMB filter is evaluated on KITTI dataset and simulated data from CARLA simulator. The CMM-LMB filter leads to a lower OSPA error metric than the Multiple Model LMB and LMB filters in both cases. The second half looks into sensor management for MTT using a sensor with a narrow field of view and a finite action slew rate. The sensor management for space situational awareness (SSA) is chosen as an application scenario. Classical sensor management algorithm for SSAtends to only consider the immediate reward. In this dissertation, deep reinforcement learning (DRL) agents are developed to overcome the combinatorial increase in problem size for long-term sensor tasking problems. A custom environment for SSA sensor tasking was developed in order to train and evaluate the DRL agents. The DRL agents are trained using Proximal Policy Optimization with Population Based Training and are able to outperform traditional myopic policies.Item Optimal Estimation and Control of Large Collaborative Swarms using Random Finite Set Theory(2019-09) Doerr, BryceControlling 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 contributions of this work is to form the Random Finite Set (RFS) control for large collaborative swarms, decentralize RFS control for individual agents, and form RFS control using other multi-agent RFS filters. The state representation of the large swarms with an unknown number of agents is generalized as an RFS where an RFS is a collection of agent states with no ordering between individual agents that can randomly change through time. The novelty of this idea is to generalize the notion of distance using RFS-based distance measures and "close-the-loop" between an estimating and controlling a swarm RFS. Specifically, multi-target estimation is determined using the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter which processes measurements from an unknown number of agents with defined spawn, birth, and death rates. RFS control is then compared for each distributional distance-based cost studied including the Cauchy-Schwarz, L2^2, and a modified L2^2 divergence using a model predictive control (MPC) based Quasi-Newton optimization. Next, RFS control and estimation is extended to MPC via iterative linear quadratic regulator (a variant of differential dynamic programming) for spacecraft swarms. The swarm is estimated in both cardinality (number of agents) and state using the GM-PHD filter which provides the estimates for RFS control. RFS control through ILQR approximates a quadratic value function from the distributional distance-based cost (i.e. the modified L2^2 divergence) to find an optimal control solution. This results in an implicit proof for RFS control of large collaborative swarms. 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 ILQR. 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. The GM-PHD filter is the most basic RFS-based filters used for estimation. Other RFS-based filters can improve the estimate or provide additional tracking information for RFS control by using either the Cardinalized Probability Hypothesis Density (CPHD) filter or the Generalized labeled Multi-Bernoulli (GLMB) filter, respectively. The CPHD filter generalizes the GM-PHD filter by jointly propagating a generalized cardinality distribution as well as the RFS to produce better estimates at high cardinality. The GLMB filter incorporates labels into the RFS, thus the GLMB filter is able to track individual trajectories of agents through time. Both these filters are propagated in feedback with RFS control for the spacecraft relative motion problem. Specifically, the MPC-based ILQR is implemented to provide swarm control in a centralized manner. By using the CPHD and GLMB filters, the cardinality and state estimates become more accurate for RFS control for large collaborative swarms.