This paper proposes a novel trajectory planning algorithm to design an end-effector motion profile along a specified path. An optimization model based on the whale optimization algorithm (WOA) is established for time-optimal asymmetrical S-curve velocity scheduling. Trajectories designed by end-effector limits may violate kinematic constraints due to the non-linear relationship between the operation and joint space of redundant manipulators. A constraints conversion approach is proposed to update end-effector limits. The path can be divided into segments at the minimum of the updated limitations. On each path segment, the jerk-limited S-shaped velocity profile is generated within the updated limitations. The proposed method aims to generate end-effector trajectory by kinematic constraints which are imposed on joints, resulting in efficient robot motion performance. The WOA-based asymmetrical S-curve velocity scheduling algorithm can be automatically adjusted for different path lengths and start/end velocities, allowing flexibility in finding the time-optimal solution under complex constraints. Simulations and experiments on a redundant manipulator prove the effect and superiority of the proposed method.

Runoff is an important component of water resources and also the basis for regional water resources development and utilization. In order to explore the new characteristics of the spatiotemporal variation of runoff in the whole Yellow River Basin, the spatiotemporal variation of runoff in the Yellow River Basin from 1982 to 2012 was studied based on the measured runoff data of 14 representative basins in the upper, middle, and lower reaches of the Yellow River Basin. The results showed that the runoff depth of the Yellow River Basin from 1982 to 2012 showed a decreasing trend, with a decrease rate of 0.3 mm/a. Among them, the discharge depth decreased significantly (p < 0.01) from 1982 to 1999, with a rate of 1.55 mm/a. Most of the area of the basin has a discharge depth of 0–10 mm, which is relatively dry. The area of higher runoff depth (40–100 mm) is decreasing and gradually concentrating in high-altitude steep-slope areas, while the area of lower runoff depth (0–10 mm) is increasing and spreading to low-altitude gentle-slope areas. After 1999, the discharge in the four sub-basins in the upper reaches decreased, and most of the sub-basins in the middle reaches also showed a decreasing trend, while the discharge in a few sub-basins, such as Qinhe River and Yiluo River, increased. The discharge depth of the sub-basins in the lower reaches increased, but the magnitude and rate of change of most of the sub-basins were consistent with the overall trend of the Yellow River Basin, which showed a decreasing trend.

Increasing climate change makes vegetation dynamic. At the same time, dynamic changes in vegetation not only have a feedback effect on climate change, but also affect the hydrological cycle process. Therefore, understanding the vegetation change and its response to climate change is a priority for predicting future climate change and studying the impact of vegetation change on the hydrological cycle. In this study, the Yellow River Basin in China is the study area. Based on the analysis of the evolution characteristics of meteorological elements and fractional vegetation cover (FVC), the delta downscaling Coupled Model Intercomparison Project Phase 6 (CMIP6) models are optimized. The empirical orthogonal function (EOF) and singular value decomposition (SVD) methods are used to investigate the impact of climate change on vegetation in the Yellow River Basin. The results show that: (1) in the four scenarios (SSP126, SSP245, SSP370, and SSP585), FVC in the Yellow River Basin from 2022 to 2100 shows an increasing trend, SSP370 (0.017 10a–1) &gt; SSP126 (0.014 10a–1) &gt; SSP245 (0.0087 10a–1) &gt; SSP585 (0.0086 10a–1). Spatially, FVC in most regions of the Yellow River Basin show an increasing trend under the four scenarios, and the degraded areas are concentrated in a small part of the Yellow River headwaters. (2) There is a significant positive correlation between FVC and precipitation (Pre) and temperature (Tem) under four scenarios in the Yellow River Basin from 2022 to 2100. Under the same scenario, the annual average temperature can be considered as the dominant factor of FVC change in the Yellow River Basin. Under different scenarios, the impact of climate change on FVC under the high emission scenarios is greater than that under the low emission scenarios. This study will help to better understand the response of vegetation to climate change and provide a scientific basis for formulating ecological protection measures to cope with future climate change in the Yellow River Basin.