Dam construction is an important means to improve water use efficiency and the aquatic environment. However, the flow regulation of the Three Gorges Reservoir (TGR) in the middle Yangtze River has attracted much attention because the severe drought occurred in the river-lake system downstream of the TGR. In this paper, the Dongting Lake was selected as a case study in order to detect the possible relationship between the flow regulation of the TGR and the extreme drought in the river-lake system based on a coupled hydrodynamic model. The results not only confirmed the significant role of the TGR to relieve drought in the river-lake system, but also indicated that the outflow of the TGR and the hydraulic gradient between the Zhicheng to Chenglingji stations were the crucial factors to affect the water exchange between the rivers and the Dongting Lake. The adjustment of hydraulic gradient within a proper range during the water compensation of the TGR will be an effective measure to improve the water exchange and water environment in the river-lake system. These findings present the quantitative influence of these important factors on the water exchange between rivers and lakes and provide a scientific reference for environmental and ecological management of other river-lake systems.

In the past decades, considerable attention has been paid to bio-inspired intelligence and its applications to robotics. This paper provides a comprehensive survey of bio-inspired intelligence, with a focus on neurodynamics approaches, to various robotic applications, particularly to path planning and control of autonomous robotic systems. Firstly, the bio-inspired shunting model and its variants (additive model and gated dipole model) are introduced, and their main characteristics are given in detail. Then, two main neurodynamics applications to real-time path planning and control of various robotic systems are reviewed. A bio-inspired neural network framework, in which neurons are characterized by the neurodynamics models, is discussed for mobile robots, cleaning robots, and underwater robots. The bio-inspired neural network has been widely used in real-time collision-free navigation and cooperation without any learning procedures, global cost functions, and prior knowledge of the dynamic environment. In addition, bio-inspired backstepping controllers for various robotic systems, which are able to eliminate the speed jump when a large initial tracking error occurs, are further discussed. Finally, the current challenges and future research directions are discussed in this paper.

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