We describe the design of a modular system for untethered real-time kinematic motion capture using sensors with inertial measuring units (IMU). Our system is comprised of a set of small and lightweight sensors. Each sensor provides its own global orientation (3 degrees of freedom) and is physically and computationally independent, requiring only external communication. Orientation information from sensors is communicated via wireless to host computer for processing. We present results of the real-time usage of our untethered motion capture system for teleoperating the NASA Robonaut. We also discuss potential applications for untethered motion capture with respect to humanoid robotics.

Motion Capture from Inertial Sensing for UntetheredHumanoid Teleoperation

In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper introduces a novel framework that unites a Hierarchical Quadratic Programming controller with an advanced interactive perception module. This integration addresses the need for adaptive visual field control and robust tool tracking in the operating scene, ensuring that surgeons and assistants have optimal viewpoint throughout the surgical task. The proposed framework handles multiple objectives within predefined thresholds, ensuring efficient tracking even amidst changes in operating backgrounds, varying lighting conditions, and partial occlusions. Empirical validations in scenarios involving single, double, and quadruple tool tracking during tissue resection tasks have underscored the system’s robustness and adaptability. The positive feedback from user studies, coupled with the low cognitive and physical strain reported by surgeons and assistants, highlight the system’s potential for real-world application.

Endoscope Automation Framework with Hierarchical Control and Interactive Perception for Multi-Tool Tracking in Minimally Invasive Surgery

Nowadays, the use of robotic systems for inspection and maintenance is gaining importance due to the number of scenarios in which robots can operate. Indeed, robotic systems provide many advantages in harsh and hostile environments, improving workers' safety and overall efficiency. Given their ability to perform different tasks, robotic manipulators constitute a significant proportion of the possible robotic systems employed in these environments. The category of manipulators is a heterogeneous group that comprises many different types of robots: non-redundant, redundant, and hyper-redundant manipulators, the latter being subdivided into discrete-joint manipulators and continuum manipulators. Among these types of robots, hyper-redundant manipulators play a crucial role in operating in challenging environments due to their ability to perform auxiliary tasks, such as obstacle avoidance and joint limits satisfaction. Furthermore, manipulators can be made of rigid or soft mechanisms and can be mobile, operating in aerial, ground, and underwater environments. The objective of this review article is to provide a reference point for researchers interested in modelling and controlling manipulators for inspection and maintenance in challenging environments. 

Modelling and control of manipulators for inspection and maintenance in challenging environments: A literature review

Modeling, Cooperative Planning and Compliant Control of Multi-arm Space Continuous Robot for Target Manipulation

ABSTRACT Robots are universal mechanical systems that are now ubiquitous in manufacturing. One of the most important properties of industrial robots is their kinematic accuracy. Robot’s accuracy is influenced by many factors including manufacture accuracy of mechanical parts and other aspects. Calibration is a technique that allows to identify design and other parameters of the robot to achieve its highest accuracy. There are widely used traditional kinematic calibration methods based on kinematic models of the robot. Simulation is used to compare results of traditional calibration method and a newly developed method based on multi-objective deep learning evolutionary algorithm. EvoDN2 was used together with a reference vector-based evolutionary algorithm, cRVEA, used for optimization, in order to find optimal estimates of the robot parameters. It is well known that the evalutionary algorithms are capable of dealing with noisy data from measurement. Results and comparison of both techniques are discussed and evaluated.

Evolutionary algorithms in robot calibration

Visual Servo Control of Endoscope-Holding Robot Based on Multi-Objective Optimization: System Modeling and Instrument Tracking

Two trajectory tracking control methods for space hyper-redundant cable-driven robots considering model uncertainty

The attitude estimation method of attitude heading reference system (AHRS) using an Extended Kalman Filter (EKF) with a filter tuning algorithm based on fuzzy controller is introduced.The AHRS uses inertial sensors and magnetometers to calculate its attitude. It is known that the attitude update using gyros are prone to diverge and hence the attitude error needs to compensate using accelerometers and magnetometers. In this paper, a Kalman filter model with a state variables represented by Modified Rodrigues Parameters (MRP) is presented to improve the computational efficiency and a model changing algorithm is used to make the filter more robust to acceleration and magnetic disturbances.If the AHRS measures any disturbances which are caused by movement of the vehicle, using fuzzy controller changes the filter gain .Simulation results show, EKF tuned by fuzzy controller is correct method that makes robust to disturbances more properly ,Rodrigues parameters can improve the computational efficiency..

Improvement of Attitude Estimation Using Modified Rodrigues Parameters and Intelligent Controller

Kinematics, statics modeling and workspace analysis of a cable-driven hybrid robot

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Visual Servo Control of Endoscope-Holding Robot Based on Multi-Objective Optimization: System Modeling and Instrument Tracking

Two trajectory tracking control methods for space hyper-redundant cable-driven robots considering model uncertainty

Improvement of Attitude Estimation Using Modified Rodrigues Parameters and Intelligent Controller

Kinematics, statics modeling and workspace analysis of a cable-driven hybrid robot