Riku Funada

Objectives: Managing acute heart failure (AHF) involves the dual challenges of stabilizing hemodynamics while minimizing myocardial oxygen consumption. This is a clinically significant and technically challenging issue due to complex cardiovascular interactions and trade-offs between therapeutic goals. We aim to develop a discrete hemodynamic control framework for autonomous drug therapy that enhances both control versatility and clinical potential. Methods: A comprehensive multi-input, multi-output model was derived to capture causal relationships linking drug infusion, cardiovascular parameters, and hemodynamic responses. Based on this model, we designed an optimal control framework capable of regulating arbitrary hemodynamic targets (e.g., arterial pressure, cardiac output, left atrial pressure, right atrial pressure), minimizing myocardial oxygen consumption, accommodating multiple guideline-concordant drug classes, and enforcing clinical constraints such as dosage limits and contraindications. The discrete design enables compatibility with standard hemodynamic measurements in ICU/CCU settings. Control performance was validated using a cardiovascular simulator. Results: The system achieved multi-dimensional regulation of hemodynamics and reduced myocardial oxygen consumption across a range of AHF scenarios. It also identified pharmacologically untreatable cases by evaluating the reachable hemodynamic range. Performance remained stable despite inter-patient variability in drug sensitivities, circulatory properties, and baroreflex activity. Conclusions: The proposed system may function as a physician-assistive tool and a foundation for autonomous drug therapy in AHF. Significances: By integrating physiologically grounded modeling with practical clinical constraints, the system offers a novel and scalable approach to optimize drug therapy in AHF. It has the potential to improve treatment precision, reduce clinician burden, and advance next-generation critical care.

This article proposes a collision avoidance method for ellipsoidal rigid bodies that utilizes a control barrier function (CBF) designed from a supporting hyperplane. We formulate the problem in the special Euclidean groups ${SE}(2)$ and ${SE}(3)$ , where the kinematics are described as rigid body motion (RBM). We consider the condition for separating two ellipsoidal rigid bodies by employing a signed distance from a supporting hyperplane of a rigid body to the other rigid body. Although a positive value of this signed distance implies that the two rigid bodies are collision-free, a naively designed supporting hyperplane yields a smaller value than the actual distance. To avoid such a conservative evaluation, the supporting hyperplane is rotated so that the signed distance from the supporting hyperplane to the other rigid body is maximized. We prove that the maximum value of this optimization problem is equal to the actual distance between two ellipsoidal rigid bodies, hence eliminating excessive conservativeness. We leverage this signed distance as a CBF to prevent collision while the supporting hyperplane is rotated via a gradient-based input. The designed CBF is integrated into a quadratic programming (QP) problem, where each rigid body calculates its collision-free input in a distributed manner, given communication among rigid bodies. We exemplify that our method can be extended to other kinematic models. The proposed method is demonstrated through simulation results.

This paper proposes a distributed coverage control strategy for quadcopters equipped with downward-facing cameras that prevents the appearance of unmonitored areas in between the quadcopters' fields of view (FOVs). We derive a necessary and sufficient condition for eliminating any unsurveilled area that may arise in between the FOVs among a trio of quadcopters by utilizing a power diagram, i.e. a weighted Voronoi diagram defined by radii of FOVs. Because this condition can be described as logically combined constraints, we leverage nonsmooth control barrier functions (NCBFs) to prevent the appearance of unmonitored areas among a team's FOV. We then investigate the symmetric properties of the proposed NCBFs to develop a distributed algorithm. The proposed algorithm can support the switching of the NCBFs caused by changes of the quadcopters composing trios. The existence of the control input satisfying NCBF conditions is analyzed by employing the characteristics of the power diagram. The proposed framework is synthesized with a coverage control law that maximizes the monitoring quality while reducing overlaps of FOVs. The proposed method is demonstrated in simulation and experiment.

This paper presents a novel structure for a quadrotor, making it hover only with clockwise rotors, where we denote it as a 4P (Positive) rotor. We first investigate the condition for a 4P rotor to hover. More specifically, we conduct the hoverability analysis, which is closely related to the controllability analysis, and show that the tilting angles of each rotor are crucial conditions for the hoverability of the 4P rotor. The derived condition for the tilting angles is then utilized to present two prototypes. Each prototype is developed with the following design purposes: One is to reduce the horizontal force generated by the tilted rotors, and the other is to make its torque generation the same as that of a standard quadrotor. The hoverability and dynamical property of both prototype is demonstrated in simulations and experiments. GRAPHICAL ABSTRACT

In this article, we propose a path planning methodology for a mobile robot navigating through an obstacle-filled environment to generate a reference path that is traceable with moderate sensing efforts. The desired reference path is characterized as the shortest path in an obstacle-filled Gaussian belief manifold equipped with a novel information-geometric distance function. The distance function we introduce is shown to be an asymmetric quasi-pseudometric and can be interpreted as the minimum information gain required to steer the Gaussian belief. An RRT*-based numerical solution algorithm is presented to solve the formulated shortest-path problem. To gain insight into the asymptotic optimality of the proposed algorithm, we show that the considered path length function is continuous with respect to the topology of total variation. Simulation results demonstrate that the proposed method is effective in various robot navigation scenarios to reduce sensing costs, such as the required frequency of sensor measurements and the number of sensors that must be operated simultaneously.

This paper presents a distributed input rectification methodology to guarantee the safety of a multi-agent system over its pre-given cooperative control task. We start with defining a multi-agent system as a control target, which consists of a group of agents with general control affine nonlinear dynamics and an interconnection topology between agents. The research objective, safety, is next defined via a set invariance property, where a safe set is formed by control barrier functions. Then, an optimization-based distributed safety control method is presented. In the proposed approach, each agent rectifies its control input by solving an individual optimization problem to achieve a pre-given cooperative control strategy as much as possible while satisfying constraints for the safety derived by investigating the control barrier functions. Here, the optimization problem is formulated in a distributed manner by dividing the safety constraint between agents. To show the utility of the present approach, collision avoidance problems of a mobile robotic network are investigated as case studies. GRAPHICAL ABSTRACT

This paper considers the problem of task-dependent (top-down) attention allocation for vision-based autonomous navigation using known landmarks. Unlike the existing paradigm in which landmark selection is formulated as a combinatorial optimization problem, we model it as a resource allocation problem where the decision-maker (DM) is granted extra freedom to control the degree of attention to each landmark. The total resource available to DM is expressed in terms of the capacity limit of the in-take information flow, which is quantified by the directed information from the state of the environment to the DM’s observation. We consider a receding horizon implementation of such a controlled sensing scheme in the Linear-Quadratic-Gaussian (LQG) regime. The convex-concave procedure is applied in each time step, whose time complexity is shown to be linear in the horizon length if the alternating direction method of multipliers (ADMM) is used. Numerical studies show that the proposed formulation is sparsity-promoting in the sense that it tends to allocate zero data rate to uninformative landmarks.