Weiyan Ren

When a lunar assisted robot helps an astronaut turn over or transports the astronaut from the ground, the trajectory of the robot’s dual arms should be automatically planned according to the unstructured environment on the lunar surface. In this paper, a dual-arm control strategy model of a lunar assisted robot based on hierarchical reinforcement learning is proposed, and the trajectory planning problem is modeled as a two-layer Markov decision process. In the training process, a reward function design method based on the idea of the artificial potential field method is proposed, and the reward information is fed back in a dense reward method, which significantly reduces the invalid exploration space and improves the learning efficiency. Large-scale tests are carried out in both simulated and physical environments, and the results demonstrate the effectiveness of the method proposed in this paper. This research is of great significance in respect of human–robot interaction, environmental interaction, and intelligent control of robots.

An autonomous assistive robot needs to recognize the body-limb posture of the person being assisted while he/she is lying in a bed to provide care services such as helping change the posture of the person or carrying him/her from the bed to a wheelchair. This paper presents a data-efficient classification of human postures when lying in a bed using a hybrid fuzzy logic and machine learning approach. The classifier was trained using a relatively small dataset containing 19,800 annotated depth images collected using Kinect from 32 test subjects lying in bed. An overall accuracy of 97.1% was achieved on the dataset. Furthermore, the image dataset including depth and red-green-blue (RGB) images, is available to the research community with the publication of this paper, with the hope that it can benefit other researchers.