Whole body control of a dual arm underwater vehicle manipulator system

This paper presents a whole body control framework for the control of a dual arm underwater vehicle manipulator system developed in the context of the MARIS Italian research project, which deals with the control and coordination of underwater vehicles for manipulation and transportation problems. The proposed framework is the extension of the one used in the successful TRIDENT FP7 project that has been improved to be able to deal with multidimensional inequality control objectives. After the presentation of the mathematical background, the paper presents some simulation results showing the good performances of the proposed algorithm.     

基于对象Petri网的AUV水面监控系统建模

针对通常的模型不能同时描述自主式水下航行器(AUV)水面监控系统的静态结构和动态过程,而且在层次化、可扩充性等方面也不能满足建模需求的问题,采用对象Petri网建立了AUV水面监控系统模型.首先对AUV水面监控系统进行了分析,划分了系统对象;然后建立了适用于各个对象的不同工作模式的Petri网模型;最后给出了AUV水面监控系统的对象Petri网模型,并进行了仿真分析,仿真结果与实际过程相符合.仿真结果表明,基于Petri网建立的AUV水面监控系统具有实时性和可靠性,提高了AUV系统的自主能力和可视化能力,以及航行的可靠性和安全性.     

Trajectory Tracking Control of an Underactuated Underwater Vehicle Redundant Manipulator System

The purpose of this study is to control the position of an underactuated underwater vehicle manipulator system (U‐UVMS). It is possible to control the end‐effector using a regular 6‐DOF manipulator despite the undesired displacements of the underactuated vehicle within a certain range. However, in this study an 8‐DOF redundant manipulator is used in order to increase the positioning accuracy of the end‐effector. The redundancy is resolved according to the criterion of minimal vehicle and joint motions. The underactuated underwater vehicle redundant manipulator system is modeled including the hydrodynamic forces for the manipulator in addition to those for the autonomous underwater vehicle (AUV). The shadowing effects of the bodies on each other are also taken into account when computing the hydrodynamic forces. The Newton‐Euler formulation is used to derive the system equations of motion including the thruster dynamics. In order to establish the end‐effector trajectory tracking control of the system, an inverse dynamics control law is formulated. The effectiveness of the control law even in the presence of parameter uncertainties and disturbing ocean currents is illustrated by simulations.     

Output Regulation and Active Disturbance Rejection Control: Unified Formulation and Comparison

Output regulation theory aims to design a controller for achieving reference tracking and disturbance rejection while maintaining system stability. Different from the stabilization problem about an equilibrium point, the output regulation problem is capable of characterizing more complicated steady‐state trajectories induced by reference and/or disturbance. Many efforts have been made to reveal how a steady‐state trajectory can be characterized, estimated, and hence compensated by a controller such that output regulation can be asymptotically achieved. When the steady‐state trajectory is approximately treated as a constant “quantity”, the standard output regulation implies an approximate version within which output regulation is practically achieved. It is revealed in this paper that such an approximate version of output regulation includes the later developed active disturbance rejection control (ADRC) method as a special case.     

基于网络控制系统的水下航行器制导系统结构

研究基于网络控制原理的水下航行器制导系统结构,详细分析了基于时间触发的TFCAN高层协议在水下航行器网络控制系统中的应用;针对目前水下航行器制导系统的控制现状,提出基于NCS的水下航行器制导系统一般性结构和设计方法,有效地解决了水下航行器制导系统中信息传送的实时性和信息共享性问题,同时论证了水下航行器网络控制系统中影响其系统稳定性的时延问题：论文旨在通过对水下航行器网络控制系统结构的研究,指出其结构的意义和进一步的研究领域。     

Optimal Guidance for Autonomous Underwater Vehicle Navigation within Undersea Areas of Current Disturbances

Simulated navigations of an autonomous underwater vehicle (AUV) achieved by the minimum time guidance within undersea areas of current disturbances are presented. When an AUV has to transit to a given destination within an area of current disturbance, ingenious guidance enables the minimum time navigation. In this study, a numerical solution procedure for an optimal heading guidance law is developed. As the optimal heading reference, the solution of the optimal guidance law is fed to the heading controller. Simulated optimal navigations are realized on the basis of the dynamics of an AUV 'r2D4'. The r2D4 is a deep-ocean-exploring AUV, developed by the Institute of Industrial Science, University of Tokyo. The developed procedure never fails to derive the optimal heading sequence within a finite computational time, provided that the current velocity in the navigation region is known a priori. As a fail-safe strategy in achieving the optimal navigation, the concept of quasi-optimal navigation is presented. The quasi-optimal navigation is implemented by on-site updates of optimal guidances followed by the heading tracking control.     

Minimum Speed Seeking Control for Nonhovering Autonomous Underwater Vehicles

Most autonomous underwater vehicles (AUVs) are propelled by a single thruster, use elevators and rudders as control surfaces, and are torpedo‐shaped. Furthermore, they are positively buoyant to facilitate recovery during an emergency. For this class of nonhovering AUVs, there is a minimum speed at which the AUV must travel for stable depth control. Otherwise, the extra buoyancy will bring the AUV up to the surface when the fin loses its effectiveness at low speeds. Hence, we develop a novel algorithm such that the AUV is automatically controlled to travel at its minimum speed while maintaining a constant depth. This capability is important in a number of practical scenarios, including underwater loitering with minimum energy consumption, underwater docking with minimum impact, and high‐resolution sensing at minimum speed. First, we construct a depth dynamic model to explain the mechanism of the minimum speed, and we show its relationship with the buoyancy, the righting moment, and the fin's effectiveness of the AUV. Next, we discuss the minimum speed seeking problem under the framework of extremum seeking. We extend the framework by introducing a new definition of steady‐state mapping that imposes new structure on the seeking algorithm. The proposed algorithm employs a fuzzy inference system, which is driven by the real‐time measurements of pitch error and elevator deflection. The effectiveness of the algorithm in seeking the minimum speed is validated in both simulations and field experiments.     

Deformation Grammars: Hierarchical Constraint Preservation Under Deformation

Deformation grammars are a novel procedural framework enabling to sculpt hierarchical 3D models in an object‐dependent manner. They process object deformations as symbols thanks to user‐defined interpretation rules. We use them to define hierarchical deformation behaviours tailored for each model, and enabling any sculpting gesture to be interpreted as some adapted constraint‐preserving deformation. A variety of object‐specific constraints can be enforced using this framework, such as maintaining distributions of subparts, avoiding self‐penetrations or meeting semantic‐based user‐defined rules. The operations used to maintain constraints are kept transparent to the user, enabling them to focus on their design. We demonstrate the feasibility and the versatility of this approach on a variety of examples, implemented within an interactive sculpting system.     

Magnetic survey and autonomous target reacquisition with a scalar magnetometer on a small AUV

A scalar magnetometer payload has been developed and integrated into a two‐man portable autonomous underwater vehicle (AUV) for geophysical and archeological surveys. The compact system collects data from a Geometrics microfabricated atomic magnetometer, a total‐field atomic magnetometer. Data from the sensor is both stored for post‐processing and made available to an onboard autonomy engine for real‐time sense and react behaviors. This system has been characterized both in controlled laboratory conditions and at sea to determine its performance limits. Methodologies for processing the magnetometer data to correct for interference and error introduced by the AUV platform were developed to improve sensing performance. When conducting seabed surveys, detection and characterization of targets of interest are performed in real‐time aboard the AUV. This system is used to drive both single‐ and multiple‐vehicle autonomous target reacquisition behaviors. The combination of on‐board target detection and autonomous reacquire capability is found to increase the effective survey coverage rate of the AUV‐based magnetic sensing system.     

基于PeliCAN协议的AUV总线系统设计

设计了基于PeliCAN协议的AUV总线系统;文中介绍了系统总体设计,系统工作流程和基于报文滤波的数据通讯过程;重点介绍了在报文标志符中加入子设备地址的设计方法,在CAN总线原有的开放性基础上,使AUV系统的设备构成更加灵活自由,系统采用双冗余方法,备用总线使系统具有传输容错能力,监测记录系统作为主控计算机的备份,可代替发生故障的主机维持系统正常运转,提高了AUV的可靠性。     

Path‐space Motion Estimation and Decomposition for Robust Animation Filtering

Renderings of animation sequences with physics‐based Monte Carlo light transport simulations are exceedingly costly to generate frame‐by‐frame, yet much of this computation is highly redundant due to the strong coherence in space, time and among samples. A promising approach pursued in prior work entails subsampling the sequence in space, time, and number of samples, followed by image‐based spatio‐temporal upsampling and denoising. These methods can provide significant performance gains, though major issues remain: firstly, in a multiple scattering simulation, the final pixel color is the composite of many different light transport phenomena, and this conflicting information causes artifacts in image‐based methods. Secondly, motion vectors are needed to establish correspondence between the pixels in different frames, but it is unclear how to obtain them for most kinds of light paths (e.g. an object seen through a curved glass panel). To reduce these ambiguities, we propose a general decomposition framework, where the final pixel color is separated into components corresponding to disjoint subsets of the space of light paths. Each component is accompanied by motion vectors and other auxiliary features such as reflectance and surface normals. The motion vectors of specular paths are computed using a temporal extension of manifold exploration and the remaining components use a specialized variant of optical flow. Our experiments show that this decomposition leads to significant improvements in three image‐based applications: denoising, spatial upsampling, and temporal interpolation.     

Inspection of an underwater structure using point‐cloud SLAM with an AUV and a laser scanner

This paper presents experimental results using a newly developed 3D underwater laser scanner mounted on an autonomous underwater vehicle (AUV) for real‐time simultaneous localization and mapping (SLAM). The algorithm consists of registering point clouds using a dual step procedure. First, a feature‐based coarse alignment is performed, which is then refined using iterative closest point. The robot position is estimated using an extended Kalman filter (EKF) that fuses the data coming from navigation sensors of the AUV. Moreover, the pose from where each point cloud was collected is also stored in the pose‐based EKF‐SLAM state vector. The results of the registration algorithm are used as constraint observations among the different poses within the state vector, solving the full‐SLAM problem. The method is demonstrated using the Girona 500 AUV, equipped with a laser scanner and inspecting a 3D sub‐sea infrastructure inside a water tank. Our results prove that it is possible to limit the navigation drift and deliver a consistent high‐accuracy 3D map of the inspected object.     

Multirepresentation,Multiheuristic A* search‐based motion planning for a free‐floating underwater vehicle‐manipulator system in unknown environment

A key challenge in autonomous mobile manipulation is the ability to determine, in real time, how to safely execute complex tasks when placed in unknown or changing world. Addressing this issue for Intervention Autonomous Underwater Vehicles (I‐AUVs), operating in potentially unstructured environment is becoming essential. Our research focuses on using motion planning to increase the I‐AUVs autonomy, and on addressing three major challenges: (a) producing consistent deterministic trajectories, (b) addressing the high dimensionality of the system and its impact on the real‐time response, and (c) coordinating the motion between the floating vehicle and the arm. The latter challenge is of high importance to achieve the accuracy required for manipulation, especially considering the floating nature of the AUV and the control challenges that come with it. In this study, for the first time, we demonstrate experimental results performing manipulation in unknown environment. The Multirepresentation, Multiheuristic A* (MR‐MHA*) search‐based planner, previously tested only in simulation and in a known a priori environment, is now extended to control Girona500 I‐AUV performing a Valve‐Turning intervention in a water tank. To this aim, the AUV was upgraded with an in‐house‐developed laser scanner to gather three‐dimensional (3D) point clouds for building, in real time, an occupancy grid map (octomap) of the environment. The MR‐MHA* motion planner used this octomap to plan, in real time, collision‐free trajectories. To achieve the accuracy required to complete the task, a vision‐based navigation method was employed. In addition, to reinforce the safety, accounting for the localization uncertainty, a cost function was introduced to keep minimum clearance in the planning. Moreover a visual‐servoing method had to be implemented to complete the last step of the manipulation with the desired accuracy. Lastly, we further analyzed the approach performance from both loose‐coupling and clearance perspectives. Our results show the success and efficiency of the approach to meet the desired behavior, as well as the ability to adapt to unknown environments.     

Experimental analysis of low‐altitude terrain following for hover‐capable flight‐style autonomous underwater vehicles

Operating an autonomous underwater vehicle (AUV) in close proximity to terrain typically relies solely on the vehicle sensors for terrain detection, and challenges the manoeuvrability of energy efficient flight‐style AUVs. This paper gives new results on altitude tracking limits of such vehicles by using the fully understood environment of a lake to perform repeated experiments while varying the altitude demand, obstacle detection and actuator use of a hover‐capable flight‐style AUV. The results are analysed for mission success, vehicle risk and repeatability, demonstrating the terrain following capabilities of the overactuated AUV over a range of altitude tracking strategies and how these measures better inform vehicle operators. A major conclusion is that the effects of range limits, bias and false detections of the sensors used for altitude tracking must be fully accounted for to enable mission success. Furthermore it was found that switching between hover‐ and flight‐style actuations based on speed, whilst varying the operation speed, has advantages for performance improvement over combining hover‐ and flight‐style actuators at high speeds.     

Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise-time, overshoot, steady-state error, and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.     

基于前视声纳信息的AUV避碰规划方法

提出了基于实时前视声纳信息实现AUV避碰规划的框架.整个避碰规划系统包括声纳探测目标、目标跟踪、特征信息提取、目标运动估计、避碰算法等部分.在小型局域网仿真系统上仿真表明,该控制方法使得AUV能够较好的实现对静止目标和运动目标的避碰.     

Dynamic motion planning of overarm throw for a biped human multibody system

We propose a motion planning formulation of overarm throw for a 55-degree-of-freedom biped human multibody system. The unique characteristics of the throwing task—highly redundant, highly nonlinear, and highly dynamic—make the throwing motion simulation challenging in the literature and are addressed within the framework of multibody dynamics and optimization. To generate physically feasible throwing motions in a fully predictive method without input reference from motion capture or animation, rigorous dynamics modeling, such as dynamic balance based on Zero-Moment Point (ZMP) and ground reaction loads, is associated with the constraints. Given the target location and the object mass, the algorithm outputs the motion, required actuator torques, release conditions, and projectile and flight time of the object. Realistic human-like motions of throwing are generated for different input parameters, which demonstrate valid cause–effect relations in terms of both kinematic and kinetic outputs.     

A Multi‐Autonomous Underwater Vehicle System for Autonomous Tracking of Marine Life

This paper presents a multi‐autonomous underwater vehicle system capable of cooperatively and autonomously tracking and following marine targets (i.e., fish) tagged with an acoustic transmitter. The AUVs have been equipped with stereo‐hydrophones that receive signals broadcasted by the acoustic transmitter tags to enable real‐time calculation of bearing‐to‐tag and distance‐to‐tag measurements. These measurements are shared between AUVs via acoustic modem and fused within each AUV's particle filter for estimating the target's position. The AUVs use a leader/follower multi‐AUV control system to enable the AUVs to drive toward the estimated target state by following collision‐free paths. Once within the local area of the target, the AUVs circumnavigate the target state until it moves to another area. The system builds on previous work by incorporating a new SmartTag package that can be attached to an individual's dorsal fin. The SmartTag houses a full inertial measurement unit (INU), video logger, acoustic transmitter, and timed release mechanism. After real‐time AUV tracking experiments, the SmartTag is recovered. Logged IMU data are fused with logged AUV‐obtained acoustic tag measurements within a particle filter to improve state estimation accuracy. This improvement is validated through a series of multi‐AUV shark and boat tracking experiments conducted at Santa Catalina Island, California. When compared with previous work that did not use the SmartTag package, results demonstrated a decrease in mean position estimation error of 25–75%, tag orientation estimation errors dropped from 80° to 30° , the sensitivity of mean position error with respect to distance to the tag was less by a factor of 50, and the sensitivity of mean position error with respect to acoustic signal reception frequency to the tag was 25 times less. These statistics demonstrate a large improvement in the system's robustness when the SmartTag package is used.     

A learning from demonstration framework for adaptive task and motion planning in varying package-to-order scenarios

Current advances in Task and Motion Planning (TAMP) framework often rely on a specific and static task structure. A task structure is a sequence of how work pieces should be manipulated towards achieving a goal. Such systems can be problematic when task structures change as a result of human performance during human-robot collaboration scenarios in manufacturing or when redundant objects are present in the workspace, for example, during a Package-To- Order scenario with the same object type fulfilling different package configurations. In this paper, we propose a novel integrated TAMP framework that supports learning from human demonstrations while tackling variations in object positions and product configurations during massive-Package-To-Order (mPTO) scenarios in manufacturing as well as during human-robot collaboration scenarios. We design and apply a Graph Neural Network(GNN) based high-level reasoning module that is capable of handling variant goal configurations and can generalize to different task structures. Moreover, we also built a two-level motion module which can produce flexible and collision-free trajectories based on important features and task labels produced by the reasoning module. Through simulations and physical experiments, we show that our framework holds several advantages when compared with state-of-the-art previous work. The advantages include sample-efficiency and generalizability to unseen goal configurations as well as task structures.