Robots performing mobile manipulation in unstructured environments must identify grasp affordances quickly and with robustness to perception noise. Yet in domains such as underwater manipulation, where perception noise is severe, computation is constrained, and the environment is dynamic, existing techniques fail. They are too computationally demanding, or too sensitive to noise to allow for closed loop grasping or dynamic replanning, or do not consider 6-DOF grasps. We present a novel grasp synthesis network, TSGrasp, that uses spatio-temporal sparse convolution to process a streaming point cloud in real time. The network generates 6-DOF grasps at greater speed and with less memory than Contact GraspNet, a state-of-the-art algorithm based on Point-Net++. By considering information from multiple successive frames of depth video, TSGrasp boosts robustness to noise or temporary self-occlusion and allows more grasps to be rapidly identified. Our grasp synthesis system was successfully demonstrated in an underwater environment with a Blueprint Labs Bravo robotic arm.

We present a navigation framework to perform autonomous underwater docking to a wave energy converter (WEC) under various ocean conditions by incorporating flow state estimation into the design of model predictive control (MPC). Existing methods lack the ability to perform dynamic rendezvous and autonomously dock in energetic conditions. The use of exteroceptive sensors or high performing acoustic sensors have been previously investigated to obtain or estimate the flow states. However, the use of such sensors increases the overall cost of the system and expects the vehicle to navigate close to the seafloor or other landmarks. To overcome these limitations, our method couples an active perception framework with MPC to estimate the flow states simultaneously while moving towards the dock. Our simulation results demonstrate the robustness and reliability of the proposed framework for autonomous docking under various ocean conditions. Furthermore, we conducted laboratory trials with a BlueROV2 docking with an oscillating dock and achieved a greater than 70% success rate.

Accurate flow predictions are critical for energy-optimal path planning of AUVs with endurance requirements. However, the complex dynamics of ocean currents make it difficult to achieve accurate flow predictions. For an AUV with flow and location sensing capabilities, one can optimize vehicle actions so that the flow information collected along the vehicle path reduces flow prediction uncertainty, referred to as active flow perception. In this paper, we propose an energy-optimal path planning approach that incorporates active flow perception. The proposed approach achieves the objectives of vehicle energy consumption minimization and flow prediction uncertainty reduction. To quantify flow prediction uncertainty, an empirical flow model parameterized using the proper orthogonal decomposition (POD) is constructed based on historical data. Assuming negligible unmodeled dynamics in the POD model, the flow prediction uncertainty is evaluated by the Cramer-Rao (CR) bound of estimated model parameters. To establish active flow perception combined with energy optimal path planning, we formulate the cost to be minimized during path planning in terms of vehicle energy using estimated flow parameters and CR bound. Through simulations, the proposed approach is compared with approaches that plan energy-optimal paths using i) true flow and ii) flow predictions without active flow perception. Simulation results demonstrate the satisfactory energy-saving performance of the proposed approach.

This paper considers a group of mobile sensing agents in a flow field and presents a distributed method for motion tomography (MT) that estimates the underlying flow field. MT formulates an underdetermined nonlinear system of equations as an inverse problem. Inspired by the Kaczmarz method which is an optimization approach for solving a linear system of equations, our previous work developed a nonlinear Kaczmarz method that solves the system of equations associated with MT. Considering distributed multi-agent systems for MT, this paper extends the nonlinear Kaczmarz method into a distributed framework. The distributed nonlinear Kaczmarz method is developed by formulating a constrained consensus problem that belongs to a class of projected consensus algorithms. To study the convergence and consensus for the method, its linear case is analyzed first and then its nonlinear case is discussed. The nonlinear case of the method is further validated through simulations by estimating a gyre flow field using mobile sensor networks with different numbers of neighboring agents. Resulting estimated flow fields are compared with a flow field estimated by its centralized counterpart.

We develop a plume tracking algorithm for a swarm of mobile sensing agents in turbulent flow. Inspired by blue crabs, we propose a stochastic model for plume spikes based on the Poisson counting process, which captures the turbulent characteristic of plumes. We then propose an approach to estimate the parameters of the spike model, and transform the turbulent plume field detected by sensing agents into a smoother scalar field that shares the same source with the plume field. This transformation allows us to design path planning algorithms for mobile sensing agents in the smoother field instead of in the turbulent plume field. Inspired by the source seeking behaviors of fish schools, we design a velocity controller for each mobile agent by decomposing the velocities into two perpendicular parts: the forward velocity incorporates feedback from the estimated spike parameters, and the side velocity keeps the swarm together. The combined velocity is then used to plan the path for each agent in the swarm. Theoretical justifications are provided for convergence of the agent group to the plume source. The algorithms are also demonstrated through simulations.