Visual positioning is very significant for underwater robot autonomous manipulation. However, visual positioning feedbacks are often ineffective with unstable and discontinuous matching information during small target capture in underwater environment. Therefore, the low cost sea organism object positioning process should be applied with self-navigation and matching system in order to guide the underwater vehicle manipulator systems (UVMS) manipulation control. In order to improve the matching robustness and accuracy, this study established the underwater refraction and intrinsic & extrinsic parameter optimization model accurate calibration, designed an improved ORB feature matching method, with adaptive threshold to extract FAST feature points and non-maximum suppress to remove feature point blocks in the feature extraction stage. Moreover, the comparison of three pixel blocks norm values are proposed in the feature point description stage to improve the robustness of the descriptor. This paper has proposed a novel tightly coupled binocular vision-DVL fusion positioning feedback optimization algorithm with multi-state constraint Invariant Extended Kalman Filter to improve accurate real-time measurement for the capture of sea organism with limited features. Systematic tank and oceanic experiments have been performed to prove and analyze the purposed algorithm. Experimental results show that the algorithm can provide reliable positioning information in tank and complex seabed environment, which is effective for the underwater robot to realize accurate capture of marine organisms.

With the rapid advancement of the simultaneous localization and mapping (SLAM) technology, the collaboration of several autonomous underwater vehicles (AUVs) in large-scale seafloor imaging has become a trending topic. Electromagnetic waves are difficult to transfer underwater, the only viable method of communication is acoustic transmission, but its bandwidth is limited. Therefore, how to compress and process multibeam bathymetry maps so that AUVs can acquire maps gathered by other AUVs has become an important topic of study. This study presents a representation approach for multibeam bathymetry maps based on a quadtree structure. In comparison to the girding approach, the sparse pseudo-input Gaussian processes (SPGPs) method, and the octree-based method, the quadtree-based method suggested in this study preserves precision while compressing storage space. Experiments utilizing field data validate the performance of the proposed technique, and the method’s ability to compress storage space towards an AUV cooperative SLAM’s scenario.

This paper presents the design of two predefined-time active fault-tolerant controllers for the trajectory tracking of autonomous underwater vehicles (AUVs) which can address actuator faults without causing actuator saturation. The first controller offers improved steady-state trajectory tracking precision, while the second ensures a nonsingular property. Firstly, a predefined-time sliding mode controller is formulated based on a predefined-time disturbance observer by integrating a novel predefined-time auxiliary system to prevent the control input from exceeding the actuator’s physical limitations. Subsequently, a non-singular backstepping controller is introduced to circumvent potential singularities in the sliding mode controller, guaranteeing that the trajectory tracking error is uniformly ultimately bounded (UUB) within the predefined time. Additionally, theoretical analysis and simulation results are presented to illustrate the advantages of the proposed method.

Micro-small autonomous underwater vehicles (AUVs) typically rely on micro-electromechanical systems (MEMS) inertial measurement units (IMUs) for dead reckoning navigation, yet their navigation accuracy is compromised by cumulative errors over time. Considering the payload space, cost, and environmental compatibility, underwater active electrolocation offers an effective complementary sensing modality to mitigate this issue. This paper proposes a moving target tracking method that integrates active electrolocation-based quantum-behaved particle swarm optimization (QPSO) localization and rapidly-exploring random tree^* (RRT^*) path planning algorithm, enabling accurate underwater tracking and localization of moving targets. Tank experiments were conducted using the proposed method. Results demonstrate that the AECPP system achieves robust tracking under different planned paths and target velocities, and the average locating error was reduced by 27.43% compared with the least square (LS) algorithm.