Takeshi Hatanaka

Takeshi Hatanaka
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Takeshi Hatanaka

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Mathematics - Optimization and Control (2)
Computer Science - Robotics (1)
Computer Science - Distributed; Parallel; and Cluster Computing (1)
Computer Science - Multiagent Systems (1)

Publications Authored By Takeshi Hatanaka

This paper focuses on a passivity-based distributed reference governor (RG) applied to a pre-stabilized mobile robotic network. The novelty of this paper lies in the method used to solve the RG problem, where a passivity-based distributed optimization scheme is proposed. In particular, the gradient descent method minimizes the global objective function while the dual ascent method maximizes the Hamiltonian. Read More

In this paper, we address energy management for heating, ventilation, and air-conditioning (HVAC) systems in buildings, and present a novel combined optimization and control approach. We first formulate a thermal dynamics and an associated optimization problem. An optimization dynamics is then designed based on a standard primal-dual algorithm, and its strict passivity is proved. Read More

In this paper, we address a class of distributed optimization problems in the presence of inter-agent communication delays based on passivity. We first focus on unconstrained distributed optimization and provide a passivity-based perspective for distributed optimization algorithms. This perspective allows us to handle communication delays while using scattering transformation. Read More

This paper investigates coverage control for visual sensor networks based on gradient descent techniques on matrix manifolds. We consider the scenario that networked vision sensors with controllable orientations are distributed over 3-D space to monitor 2-D environment. Then, the decision variable must be constrained on the Lie group SO(3). Read More

This paper investigates cooperative environmental monitoring for Pan-Tilt-Zoom (PTZ) visual sensor networks. We first present a novel formulation of the optimal environmental monitoring problem, whose objective function is intertwined with the uncertain state of the environment. In addition, due to the large volume of vision data, it is desired for each sensor to execute processing through local computation and communication. Read More

This paper investigates vision-based cooperative estimation of a 3D target object pose for visual sensor networks. In our previous works, we presented an estimation mechanism called networked visual motion observer achieving averaging of local pose estimates in real time. This paper extends the mechanism so that it works even in the presence of cameras not viewing the target due to the limited view angles and obstructions in order to fully take advantage of the networked vision system. Read More

This paper handles a kind of strategic game called potential games and develops a novel learning algorithm Payoff-based Inhomogeneous Partially Irrational Play (PIPIP). The present algorithm is based on Distributed Inhomogeneous Synchronous Learning (DISL) presented in an existing work but, unlike DISL,PIPIP allows agents to make irrational decisions with a specified probability, i.e. Read More

This paper investigates cooperative estimation of 3D target object motion for visual sensor networks. In particular, we consider the situation where multiple smart vision cameras see a group of target objects. The objective here is to meet two requirements simultaneously: averaging for static objects and tracking to moving target objects. Read More