Water body segmentation utilizing Synthetic Aperture Radar (SAR) images plays a crucial role in water resource and flood monitoring. However, existing SAR water segmentation methods often underutilize the information present in the multiple bands of SAR images. Moreover, U-shaped semantic segmentation algorithms based on convolutional neural networks (CNN) often suffer from the loss of valuable feature information and the neglect of global and local correlations caused by the inclusion of pooling layers. To address these challenges, this letter first generates two novel bands from the VV and VH bands of the SAR images and combines them with the VV band to create a new SAR image as the input for the model. Then, to enhance the model’s capacity for feature learning and minimize the risk of overlooking small target water bodies, the letter proposed a water body segmentation model for SAR images that improves upon the UNet model by integrating coordinate attention mechanisms, CBAM and PVTv2. Finally, to tackle the issue of high computational complexity associated with the model, traditional convolutions are substituted with depthwise separable convolutions. The experimental results demonstrate that the proposed method achieves a 1.68% increase in accuracy, a 4.21% increase in MIOU, and a 3.51% increase in F-score when compared to the widely adopted UNet algorithm. Consequently, the proposed model surpasses other algorithms in the domain of water segmentation.

With new, safer manipulator robots, the probability of serious injury due to collisions with humans remains low (5%), even at speeds as high as 2 m.s-1. Collisions would better be avoided nevertheless, because they disrupt the tasks of both the robot and the human. We propose in this paper to equip robots with exteroceptive sensors and online motion generation so that the robot is able to perceive and react to the motion of the human in order to reduce the occurrence of collisions. It's impossible to guarantee that no collision will ever take place in a partially unknown dynamic environment such as a shared workspace, but we can guarantee instead that, if a collision takes place, the robot is at rest at the time of collision, so that it doesn't inject its own kinetic energy in the collision. To do so, we adapt a Model Predictive Control scheme which has been demonstrated previously with two industrial manipulator robots avoiding collisions while sharing their workspace. The proposed control scheme is validated in simulation.