Underwater docking enhances the operational capabilities of Autonomous Underwater Vehicles (AUVs) by facilitating energy and data transfer. Optical beacons serve as the primary guidance method for AUVs to localize and track docking stations. This paper presents VIP-Dock, a novel optical beacon tracking algorithm for robust underwater docking of AUVs. VIP-Dock addresses the challenge of maintaining accurate beacon tracking under visual interference by integrating visual, inertial, and pressure perception. Employing an unscented Kalman filter framework, the VIP-Dock algorithm provides continuous optimal estimation of beacon positions. Experimental results demonstrated VIP-Dock's real-time tracking performance in actual docking scenarios and its ability to maintain accuracy during visual input failure. Implementation in a simulation platform for an underwater vertical shuttle showed significant improvement, increasing docking success rates from 62% to 84% across 100 trials under simulated current disturbances.

Autonomous Underwater Vehicles (AUVs) require energy-efficient and responsive attitude control for underwater operations. We present UVS, a novel underwater vehicle that combines Variable Center of Mass System (VCMS) and thrusters for hybrid attitude regulation. Through multi-objective optimization of the VCMS structure, we achieved a 5.19% larger pitch angle range while reducing space occupation by 15.72%. Pool experiments demonstrated near-linear pitch control from 17.5° to 172.5° with stable horizontal-vertical mode transitions. Our proposed collaborative control method integrates VCMS and thruster advantages, enabling rapid convergence to target attitudes with long-term stability. The results show UVS’s potential for energy-efficient, wide-range attitude control in mobile ocean sensing applications.

Optimizing the controls of quantum systems plays a crucial role in advancing quantum technologies. The time-varying noises in quantum systems and the widespread use of inhomogeneous quantum ensembles raise the need for high-quality quantum controls under uncertainties. In this paper, we consider a stochastic discrete optimization formulation of a discretized binary optimal quantum control problem involving Hamiltonians with predictable uncertainties. We propose a sample-based reformulation that optimizes both risk-neutral and risk-averse measurements of control policies, and solve these with two gradient-based algorithms using sum-up-rounding approaches. Furthermore, we discuss the differentiability of the objective function and prove upper bounds of the gaps between the optimal solutions to binary control problems and their continuous relaxations. We conduct numerical simulations on various sized problem instances based on two applications of quantum pulse optimization; we evaluate different strategies to mitigate the impact of uncertainties in quantum systems. We demonstrate that the controls of our stochastic optimization model achieve significantly higher quality and robustness compared with the controls of a deterministic model. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This work was supported by the US Department of Energy, Advanced Scientific Computing Research [Grants DE-AC02-06CH11357, DE-SC0018018]; Defense Sciences Office, DARPA [Grant IAA-8839-annex-130]; the US National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation [Grant 2041745]; and the US National Aeronautics and Space Administration (NASA) Ames Research Center [Grant 80ARC020D0010]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0560 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0560 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Quantum optimal control is a technique for controlling the evolution of a quantum system and has been applied to a wide range of problems in quantum physics. We study a binary quantum control optimization problem, where control decisions are binary-valued and the problem is solved in diverse quantum algorithms. In this paper, we utilize classical optimization and computing techniques to develop an algorithmic framework that sequentially optimizes the number of control switches and the duration of each control interval on a continuous time horizon. Specifically, we first solve the continuous relaxation of the binary control problem based on time discretization and then use a heuristic to obtain a controller sequence with a penalty on the number of switches. Then, we formulate a switching time optimization model and apply sequential least-squares programming with accelerated time-evolution simulation to solve the model. We demonstrate that our computational framework can obtain binary controls with high-quality performance and also reduce computational time via solving a family of quantum control instances in various quantum physics applications.

Marker labeling plays an important role in optical motion capture pipeline especially in real-time applications; however, the accuracy of online marker labeling is still unclear. This paper presents a novel accurate real-time online marker labeling algorithm for simultaneously dealing with missing and ghost markers. We first introduce a soft graph matching model that automatically labels the markers by using Hungarian algorithm for finding the global optimal matching. The key idea is to formulate the problem in a combinatorial optimization framework. The objective function minimizes the matching cost, which simultaneously measures the difference of markers in the model and data graphs as well as their local geometrical structures consisting of edge constraints. To achieve high subsequent marker labeling accuracy, which may be influenced by limb occlusions or self-occlusions, we also propose an online high-quality full-body pose reconstruction process to estimate the positions of missing markers. We demonstrate the power of our approach by capturing a wide range of human movements and achieve the state-of-the-art accuracy by comparing against alternative methods and commercial system like VICON.