A brief review of recent progress on deep sea mining vehicle

The article discusses the main methodologies and key technologies behind the ROBUST project, funded by the European Union within the Horizon 2020 framework, whose goal is the development of an autonomous underwater vehicle manipulator system for deep sea mining exploration. This article focuses on the developed control and perception framework and related mechatronics, and presents the experimental results from the sea trials in Sardinia, Italy, supporting the proposed methodologies.

Sea Mining Exploration With an UVMS: Experimental Validation of the Control and Perception Framework

Experimental validation of the modeling and control of a multibody underwater vehicle manipulator system for sea mining exploration

There is a growing interest and literature on autonomous underwater intervention systems The purpose of this paper is to provide a review of the recent literature on control systems for autonomous or semi-autonomous underwater manipulation activities, classifying the results based on the type of task executed and the testing environment (simulation, pool, or sea) Amongst underwater intervention tasks, the grasping of objects lying on the seafloor is one of the most studied topics Several results are given both in pool and sea environments The perception of such objects might still need further improvements before the system can be considered robust enough Manipulation of valves while docked has been demonstrated in sea environments Results on floating valve manipulation or floating inspection through force regulation are still limited to pool environments Finally, cooperative transportation by multiple agents is still limited to numerical simulation results only A review of the state of the art of underwater manipulation is presented First an introduction is given, recalling the fundamental milestones reached in the past on this topic Then, recent findings on control systems for (semi-)autonomous intervention are presented, subdivided in grasping, valve manipulation and force regulation tasks and cooperative manipulation Some unconventional systems are also presented

https://link.springer.com/content/pdf/10.1007/s43154-020-00012-7.pdf

Autonomous Underwater Intervention

Ocean exploration has become one of the most important strategies for a sustainable development for our world. To better understand the ocean and make an efficient use of its resources, autonomous marine vehicles (AMVs) including both surface and underwater vehicles play an essential role to extend and accelerate the exploration capabilities. This chapter provides an in-depth review of the key technologies in the development of autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs), which are two main types of AMVs. With the illustration of some typical vehicle prototypes, the control methods and deployment strategies of ASVs and AUVs, especially the collaborative operation of these two types of vehicles, have been discussed to inspire a wide application of marine autonomy in future ocean explorations.

Ocean Explorations Using Autonomy: Technologies, Strategies and Applications

This paper presents the control and perception framework used in the ROBUST EU Horizon 2020 project, aimed at integrate different technologies for developing an underwater autonomous robotic system for exploring deep-sea mining sites. The vehicle firstly collects data of the initial zone of interest and it selects a sub area which is the most probable to contain a manganese nodule field; then it carries out a low altitude survey. When a possible nodule is detected by the cameras, the vehicle lands on the seafloor, allowing the following fixed based manipulation which is designed to perform the nodule analysis. The work reports the implemented control and perception architecture and the preliminary pool experiments results.

Control and Perception Framework for Deep Sea Mining Exploration

This paper presents the control framework developed within the Horizon 2020 ROBUST project aimed at building an autonomous system for exploring deep sea mining sites. First, the Autonomous Underwater Vehicle analyzes the initial zone of interest with the aim of finding a sub area with the highest probability to contain a manganese nodule field. When such an area is found, a low altitude survey is performed. When a possible nodule is detected, the vehicle lands on the seafloor, allowing a dedicated sensor mounted on the manipulator’s end-effector to perform the nodule analysis.This work presents the ROBUST control framework and the task priority based kinematic used for its implementation. In addition, software in the loop simulation, dry and pool test results are shown to validate the control framework addressed.

Autonomous Deep Sea Mining Exploration: The EU ROBUST Project Control Framework

Collaborative robots can relief human operators from excessive efforts during payload lifting activities. Modelling the human partner allows the design of safe and efficient collaborative strategies. In this paper, we present a control approach for human-robot collaboration based on human monitoring through whole-body wearable sensors, and interaction modelling through coupled rigid-body dynamics. Moreover, a trajectory advancement strategy is proposed, allowing for online adaptation of the robot trajectory depending on the human motion. The resulting framework allows us to perform payload lifting tasks, taking into account the ergonomic requirements of the agents. Validation has been performed in an experimental scenario using the iCub3 humanoid robot and a human subject sensorized with the iFeel wearable system.

A Control Approach for Human-Robot Ergonomic Payload Lifting

In recent years, there has been a drastic improvement in the robotic field concerning the development and control of humanoid robots to endow them with the ability to interact and collaborate with human beings for performing a large variety of tasks [1], [2], [3] and particular interest has been paid to the introduction of such platforms into the industry 4.0 [4]. In the aforementioned interaction, agents’ safety and system efficiency are two of the main aspects to be ensured [5] leading to the ergonomy principle [6]. While several works have addressed the design of control architectures to achieve an ergonomic physical interaction [7], [8], the humanoid robot hardware design has been rarely considered as an element to be optimized for the specific collaborative action, and it is rather assumed as given. However, several works have proposed the usage of different algorithms to jointly consider hardware design and control requirements [9], [10]. The proposed co-optimization is achieved via classical optimization techniques such as block coordinate descendent methods [11]. Other works have exploited reinforcement learning techniques [12], [13], and evolutionary algorithm [14], [15], [16] to port the biological principle of embodied cognition [17] in the robotic field, showing evidence on how the environment and related tasks directly affect the complexity of the agent, thus underlining the importance of considering the environment in the design process of both the system morphology and the control strategy. The aforementioned papers, despite the interesting work presented, apply the principles of embodied cognition to simple robotics platforms which are composed of a limited number of links and do not consider a possible interaction with a human being. In this paper a novel strategy to design humanoid robots, to achieve ergonomic human-robot interaction is presented, extending the work done in [18], adding the robot hardware parameters in the loop to achieve an ergonomic interaction.

Robot Design Optimization for Human-Robot Collaborative Lifting Tasks

When a human and a humanoid robot collaborate physically, ergonomics is a key factor to consider. Assuming a given humanoid robot, several control architectures exist nowadays to address ergonomic physical human-robot collaboration. This paper takes one step further by considering robot hardware parameters as optimization variables in the problem of collaborative payload lifting. The variables that parametrize robot's kinematics and dynamics ensure their physical consistency, and the human model is considered in the optimization problem. By leveraging the proposed modelling framework, the ergonomy of the interaction is maximized, here given by the agents' energy expenditure. Robot kinematic, dynamics, hardware constraints and human geometries are considered when solving the associated optimization problem. The proposed methodology is used to identify optimum hardware parameters for the design of the ergoCub robot, a humanoid possessing a degree of embodied intelligence for ergonomic interaction with humans. For the optimization problem, the starting point is the iCub humanoid robot. The obtained robot design reaches loads at heights in the range of 0.8 - 1.5 m with respect to the iCub robot whose range is limited to 0.8-1.2 m. The robot energy expenditure is decreased by about 33%, meanwhile, the human ergonomy is preserved, leading overall to an improved interaction.

Carlotta Sartore

Papers

Control and Perception Framework for Deep Sea Mining Exploration

Autonomous Deep Sea Mining Exploration: The EU ROBUST Project Control Framework

A Control Approach for Human-Robot Ergonomic Payload Lifting

Robot Design Optimization for Human-Robot Collaborative Lifting Tasks

Optimization of Humanoid Robot Designs for Human-Robot Ergonomic Payload Lifting