This article proposes a predictive end-effector control method for manipulators operating on mobile platforms subjected to unwanted base motion. Time series is used to forecast the base motion using historical state information. Then, a trajectory specified in the inertial frame is transformed to a predicted trajectory with respect to the manipulator. By tracking this transformed trajectory, the manipulator negates the base motion. A model-predictive control problem is formulated via quadratic programming (QP) to track said trajectory over the prediction horizon. Only the first control action in the control sequence is constrained by kinematic feasibility. In this manner, QP can be swiftly solved with linear inequality constraints. It is shown that the actual joint trajectory executed by the manipulator is always kinematically feasible. Moreover, tracking error can still be reduced despite future predicted control actions being infeasible. The method is validated through both simulation and experiment. The proposed method can reduce pose error by over 60% compared to a proportional–integral feedback controller.

An increasing number of Autonomous Mobile Robots (AMRs) are used in warehouses and factories in recent years. The risk of some of the AMRs being out of control is surging. Although Reinforcement Learning (RL)-based approaches have achieved dramatic success in the motion planning of a large number of AMRs, the available RL-based motion planning approaches cannot provide a safety guarantee for the remaining functional AMRs if some of the AMRs are out of control. To this end, this paper develops a scalable Multi-agent RL (MARL) with Control Barrier Function (CBF)-based shields algorithm. The MARL with CBF-based shields algorithm can address complex high-level tasks by MARL and deal with the safety issue of every single functional AMR by a low-level CBF-based shield. A CBF-based shield is designed for every single functional AMR to ensure that the action of the functional AMR is safe, even if an uncontrollable AMR is pursuing the functional AMR. Experiments are conducted based on simulated warehouse environments to evaluate the effectiveness and scalability of a safe RL-based motion planning approach (The safe RL-based motion planning approach developed in this study is demonstrated in a video: https://youtu.be/I7ja5nFVpY4). developed according to the MARL with CBF-based shields algorithm.

Accurately localization between multi-robots is crucial for many underwater applications, such as tracking, convoying and subsea intervention tasks. 6D pose estimation is a fundamental task that enables precise object localization in 3D space with full six degrees of freedom. However, one critical challenge is the lack of available large-scale datasets due to the unbearable cost of labelled data collection. To overcome this difficulty, we propose a benchmark dataset, ROV6D, for 6D pose estimation of remotely operated vehicles (ROVs). The training subset consists of a large number of synthetic images with 6D pose ground truth for ROVs. These synthetic images are generated using BlenderProc and further rendered with the underwater neural rendering (UWNR) strategy to enhance their realism. The testing subsets cover different real-world scenarios, including the Pool subset and Maoming subset, focusing on challenging cases that involve partial occlusion and low visibility. Diverse recent methods are evaluated on the constructed dataset. The results show that methods based on dense coordinates currently perform best, outperforming both the keypoint-based method and the refinement-based method. Our dataset will be made publicly available soon.

Effective 3D reconstruction utilizing imaging sonars is vital for underwater robots, particularly in turbid water conditions. The absence of elevation angles in acoustic echo measurements significantly slows down the Neural Radiance Field (NeRF) method. This is attributed to the differentiable rendering model of sonar images, which, unlike visual imagery, needs to generate samples covering a spatial sector rather than a ray for each pixel. To address this, we present a fast 3D reconstruction method using sonar images, termed Differentiable Space Carving (DSC). DSC carves the space iteratively by rendering echo probabilities instead of echo intensities, eliminating the need for the intensity network commonly found in NeRF models. The absence of occupancy-echo correspondences is effectively tackled through backpropagation guided by rendering losses. Additionally, we leverage occupancy probability grids and multiresolution hash encoding to construct differentiable occupancy models, ensuring faster convergence compared to multilayer perceptrons. The experiments have been conducted in numerically simulated environments and with datasets from a laboratory tank. Compared to existing NeRF methods, DSC reconstructs objects about ten times faster and provides more details.

In this study, we use analytical algorithms, specifically the auxiliary equation (AE) approach, the improved F-expansion method, and the modified Sardar sub-equation (MSSE) method to investigate complex wave structures for plentiful solutions associated with the fractional perturbed Gerdjikov-Ivanov (PGI) model with the M-fractional operator. The investigated model is a well-established mathematical model used to represent a variety of physical events in nonlinear dynamics and mathematical physics. By using the aforementioned techniques, we scrutinize some new optical wave solutions in the framework of dark, bright, periodic, combo, W-shaped, M-shape, V-shape, kink type, singular rational, exponential, trigonometric, and hyperbolic solutions. The acquired solutions address a wide range of optical solutions in the form of 3D plots, contour plots, and 2D plots, declaring the free parameters of such optical soliton solutions and comprehending their dynamic behavior. Also, the sensitive analysis of the selected model is analyzed. The main contribution of this study is to extract diverse solitary wave solutions of the adopted model. Some of the solutions are similar and some diverge from the previous solutions which justifies the novelty of the study. Finally, we discovered that the current technique provides a reliable instrument for investigating the analytic solutions of fractional differential equations. The proposed PGI model can be used to transmit ultra-fast pulses across optical fibers. This research goes beyond to the advancement of mathematical techniques for solving fractional differential equations and broadens their application to a wide range of real-world scientific and engineering problems.

Visual degradation in underwater environments poses unique and significant challenges, which distinguishes underwater simultaneous localization and mapping (SLAM) from popular vision-based SLAM on the ground. In this article, we propose robust underwater SLAM with sonar optimization (RUSSO), a robust underwater SLAM system, which fuses stereo camera, inertial measurement unit (IMU), and imaging sonar to achieve robust and accurate localization in challenging underwater environments for 6 degrees of freedom (DoF) estimation. During visual degradation, the system is reduced to a sonar-inertial system estimating 3-DoF poses. The sonar pose estimation serves as a strong prior for IMU propagation, thereby enhancing the reliability of pose estimation with IMU propagation. In addition, we propose a SLAM initialization method that leverages the imaging sonar to counteract the lack of visual features during the initialization stage of SLAM. We extensively validate RUSSO through experiments in simulator, pool, and sea scenarios. The results demonstrate that RUSSO achieves better robustness and localization accuracy compared to the state-of-the-art visual-inertial SLAM systems, especially in visually challenging scenarios. To the best of authors' knowledge, this is the first time fusing stereo camera, IMU, and imaging sonar to realize robust underwater SLAM against visual degradation.