Author

# Dongbing Gu

Other affiliations: Beijing Institute of Technology, Shanghai Jiao Tong University, University of Oxford

Bio: Dongbing Gu is an academic researcher from University of Essex. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 30, co-authored 223 publications receiving 4375 citations. Previous affiliations of Dongbing Gu include Beijing Institute of Technology & Shanghai Jiao Tong University.

##### Papers published on a yearly basis

##### Papers

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21 May 2018TL;DR: In this article, the authors proposed a novel monocular visual odometry (VO) system called UnDeepVO, which is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks.

Abstract: We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVo:one is the unsupervised deep learning scheme, and the other is the absolute scale recovery. Specifically, we train UnDeepVoby using stereo image pairs to recover the scale but test it by using consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss function defined for training the networks is based on spatial and temporal dense information. A system overview is shown in Fig. 1. The experiments on KITTI dataset show our UnDeepVO achieves good performance in terms of pose accuracy.

399 citations

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TL;DR: A novel monocular visual odometry system called UnDeepVO, able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks, is proposed.

Abstract: We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVO: one is the unsupervised deep learning scheme, and the other is the absolute scale recovery. Specifically, we train UnDeepVO by using stereo image pairs to recover the scale but test it by using consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss function defined for training the networks is based on spatial and temporal dense information. A system overview is shown in Fig. 1. The experiments on KITTI dataset show our UnDeepVO achieves good performance in terms of pose accuracy.

324 citations

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TL;DR: In this paper, a receding horizon (RH) controller is developed for tracking control of a nonholonomic mobile robot and it is shown that the control strategy is feasible.

Abstract: In this paper, a receding horizon (RH) controller is developed for tracking control of a nonholonomic mobile robot. The control stability is guaranteed by adding a terminal-state penalty to the cost function and constraining the terminal state to a terminal-state region. The stability analysis in the terminal-state region is investigated, and a virtual controller is found. The analysis results show that the RH tracking control has simultaneous tracking and regulation capability. Simulation results are provided to verify the proposed control strategy. It is shown that the control strategy is feasible.

252 citations

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TL;DR: A receding horizon approach is adopted to synthesize a state-feedback controller for the formation control of mobile robots as a linear-quadratic Nash differential game through the use of graph theory.

Abstract: This paper presents a differential game approach to formation control of mobile robots. The formation control is formulated as a linear-quadratic Nash differential game through the use of graph theory. Finite horizon cost function is discussed under the open-loop information structure. An open-loop Nash equilibrium solution is investigated by establishing existence and stability conditions of the solutions of coupled (asymmetrical) Riccati differential equations. Based on the finite horizon open-loop Nash equilibrium solution, a receding horizon approach is adopted to synthesize a state-feedback controller for the formation control. Mobile robots with double integrator dynamics are used in the formation control simulation. Simulation results are provided to justify the models and solutions.

173 citations

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TL;DR: It is shown that the distributed EM algorithm is a stochastic approximation to the standard EM algorithm and converges to a local maximum of the log-likelihood.

Abstract: This paper presents a distributed expectation-maximization (EM) algorithm over sensor networks. In the E-step of this algorithm, each sensor node independently calculates local sufficient statistics by using local observations. A consensus filter is used to diffuse local sufficient statistics to neighbors and estimate global sufficient statistics in each node. By using this consensus filter, each node can gradually diffuse its local information over the entire network and asymptotically the estimate of global sufficient statistics is obtained. In the M-step of this algorithm, each sensor node uses the estimated global sufficient statistics to update model parameters of the Gaussian mixtures, which can maximize the log-likelihood in the same way as in the standard EM algorithm. Because the consensus filter only requires that each node communicate with its neighbors, the distributed EM algorithm is scalable and robust. It is also shown that the distributed EM algorithm is a stochastic approximation to the standard EM algorithm. Thus, it converges to a local maximum of the log-likelihood. Several simulations of sensor networks are given to verify the proposed algorithm.

167 citations

##### Cited by

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01 Jan 1990

TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

2,933 citations

30 Apr 1984

TL;DR: A review of the literature on optimal foraging can be found in this article, with a focus on the theoretical developments and the data that permit tests of the predictions, and the authors conclude that the simple models so far formulated are supported by available data and that they are optimistic about the value both now and in the future.

Abstract: Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

2,709 citations

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TL;DR: This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

2,198 citations

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TL;DR: In this article, the authors reviewed some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006 and proposed several promising research directions along with some open problems that are deemed important for further investigations.

Abstract: This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

1,814 citations

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TL;DR: In this paper, the authors reviewed some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006, and proposed several promising research directions along with some open problems that are deemed important for further investigations.

Abstract: This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

1,655 citations