Control Strategy of an Underactuated Underwater Drone-Shape Robot for Grasping Tasks.

In underwater environments, ensuring people's safety is complicated, with potentially life-threatening outcomes, especially when divers have to work in deeper conditions. To improve the available solutions for working with robots in this kind of environment, we propose the validation of a control strategy for robots when taking objects from the seabed. The control strategy proposed is based on acceleration feedback in the model of the system. Using this model, the reference values for position, velocity and acceleration are estimated, and then the position error signal can be computed. When the desired position is obtained, it is possible to then obtain the position error. The validation was carried out using three different objects: a ball, a bottle, and a plant. The experiment consisted of using this control strategy to take those objects, which the robot carried for a moment to validate the stabilisation control and reference following the control in terms of angle and depth. The robot was operated by a pilot from outside of the pool and was guided using a camera and sonar in a teleoperated way. As an advantage of this control strategy, the model upon which the robot is based is decoupled, allowing control of the robot for each uncoupled plane, this being the main finding of these tests. This demonstrates that the robot can be controlled by a control strategy based on a decoupled model, taking into account the hydrodynamic parameters of the robot.

Method to Develop Legs for Underwater Robots: From Multibody Dynamics with Experimental Data to Mechatronic Implementation.

Exploration of the seabed may be complex, and different parameters must be considered for a robotic system to achieve tasks in this environment, such as soil characteristics, seabed gait, and hydrodynamic force in this extreme environment. This paper presents a gait simulation of a quadrupedal robot used on a typical terrigenous sediment seabed, considering the mechanical properties of the type of soil, stiffness, and damping and friction coefficients, referenced with the specialized literature and applied in a computational multibody model with many experimental data in a specific underwater environment to avoi hydrodynamic effects. The requirements of the positions and torque in the robot's active joints are presented in accordance with a 5R mechanism for the leg and the natural pattern shown in the gait of a dog on the ground. These simulation results are helpful for the design of a testbed, with a leg prototype and its respective hardware and software architecture and a subsequent comparison with the real results.

Learning Haptic-Based Object Pose Estimation for In-Hand Manipulation Control With Underactuated Robotic Hands.

Unlike traditional robotic hands, underactuated compliant hands are challenging to model due to inherent uncertainties. Consequently, pose estimation of a grasped object is usually performed based on visual perception. However, visual perception of the hand and object can be limited in occluded or partly-occluded environments. In this paper, we aim to explore the use of haptics, i.e., kinesthetic and tactile sensing, for pose estimation and in-hand manipulation with underactuated hands. Such haptic approach would mitigate occluded environments where line-of-sight is not always available. We put an emphasis on identifying the feature state representation of the system that does not include vision and can be obtained with simple and low-cost hardware. For tactile sensing, therefore, we propose a low-cost and flexible sensor that is mostly 3D printed along with the finger-tip and can provide implicit contact information. Taking a two-finger underactuated hand as a test-case, we analyze the contribution of kinesthetic and tactile features along with various regression models to the accuracy of the predictions. Furthermore, we propose a Model Predictive Control (MPC) approach which utilizes the pose estimation to manipulate objects to desired positions solely based on haptics. We have conducted a series of experiments that validate the ability to estimate poses of various objects with different geometry, stiffness and texture, and show manipulation to goals in the workspace with relatively high accuracy.