Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed. It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. Extensive simulation and experiment results show that the proposed mobile manipulation system is able to grasp different types of objects autonomously in various simulation and real-world scenarios, verifying the effectiveness of the proposed mobile manipulation system.

The dataset contains 1225 data samples for 5 fault types (labels). We divided the dataset into the training set and the test set through random stratified sampling. The test set accounted for $20\%$ of the total dataset. Our experimental subject is ‘Haizhe’, which is a small quadrotor AUV developed in the laboratory. For each fault type, ‘Haizhe’ was tested several times. For each time, ‘Haizhe’ ran the same program and sailed underwater for 10–20 s to ensure that state data was long enough. The state data recorded in each test were then used as a data sample, and the corresponding fault type was the true label of the data sample. The dataset was used to validate a model-free fault diagnosis method proposed in our paper [1] and the complete dynamic model of ‘Haizhe’ AUV was reported in [2].

The 2022 Winter Olympics has shown that robots can serve, protect, and help people excel in many aspects of their lives.

An ultrasonic method was proposed to solve the problem of accurately measuring the thickness of oil slick using a Remotely Operated Vehicle (ROV) as a platform. The laboratory experiments were performed to collect ultrasonic reflections from interfaces underneath oil slick during ROV’s resting and running. A new signal processing method was proposed to improve the accuracy of the thickness measurement by precisely calculating the transmit time of flight (TOF) of ultrasound within oil slick. The feasibility of this method was verified by performing eight measurements of oil slick thicknesses during ROV’s resting and running. The results showed that the average values of absolute differences between ROV’s resting and running were both less than 4.0% for two transducers, a result which indicated the high measurement accuracy, repeatability and stability of this method. In addition, the minimum measurable thickness for each transducer was initially investigated, and the suitable transducer frequency was also determined. The ultrasonic method proposed in this paper demonstrates a strong capability of measuring the thickness of thin oil slick with the high accuracy. The studies in this paper would promote the development and application of this method in the oil spill response. It could possibly be developed into an applicable tool for solving the problem of measuring the thickness of oil in the field in the future.

This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of targets and trajectories from measurements affected by noise, missed detections, false alarms and port–starboard ambiguity. However, its complexity increases as the number of sensors increases. In addition, due to the time-varying underwater environment, AUV detection probabilities are time-varying, and their mismatches often lead to poor MTT performance. Consequently, we detail a robust multi-sensor LMB filter that estimates detection probabilities and multi-target states simultaneously in real time. Moreover, we derive an effective approximate form of the multi-sensor LMB filter using Kullback–Leibler divergence and develop an efficient belief propagation (BP) implementation of the multi-sensor LMB filter. Our method scales linearly with the number of AUVs, providing good scalability and low computational complexity. The proposed method demonstrates superior performance in underwater multi-AUV network MTT simulations.