Motion Compensation for Real Time Ultrasound Scanning in Robotically Assisted Prostate Biopsy Procedures

Prostate cancer is one of the most common types of cancer in men. Its diagnosis by biopsy requires a high level of expertise and precision from the surgeon, so the results are highly operator-dependent. The aim of this work is to develop a robotic system for assisted ultrasound (US) examination of the prostate, a prebiopsy step that could reduce the dexterity requirements and enable faster, more accurate and more available prostate biopsy. We developed and validated a laboratory setup with a collaborative robotic arm that can autonomously scan a prostate phantom and attached the phantom to a medical robotic arm that mimics the patient's movements. The scanning robot keeps the relative position of the US probe and the prostate constant, ensuring a consistent and robust approach to reconstructing the prostate. To reconstruct the prostate, each slice is segmented to generate a series of prostate contours converted into a 3D point cloud used for biopsy planning. The average scan time of the prostate was 30 s, and the average 3D reconstruction of the prostate took 3 s. We performed four motion scenarios: the phantom was scanned in a stationary state (S), with horizontal motion (H), with vertical motion (V), and with a combination of the two (C). System validation is performed by registering the prostate point cloud reconstructions acquired during different motions (H, V, C) with those obtained in the stationary state. ICP registration with a threshold of 0.8 mm yields mean 83.2\% fitness and 0.35 mm RMSE for S-H registration, 84.1\% fitness and 0.37 mm RMSE for S-V registration and 79.4\% fitness and 0.37 mm RMSE for S-C registration. Due to the elastic and soft material properties of the prostate phantom, the maximum robot tracking error was 3 mm, which can be sufficient for prostate biopsy according to medical literature. The maximum delay in motion compensation was 0.5 s.

Odometry Calibration and Pose Estimation of a 4WIS4WID Mobile Wall Climbing Robot

This paper presents the design of a pose estimator for a four wheel independent steer four wheel independent drive (4WIS4WID) wall climbing mobile robot, based on the fusion of multimodal measurements, including wheel odometry, visual odometry, and an inertial measurement unit (IMU) data using Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The pose estimator is a critical component of wall climbing mobile robots, as their operational environment involves carrying precise measurement equipment and maintenance tools in construction, requiring information about pose on the building at the time of measurement. Due to the complex geometry and material properties of building facades, the use of traditional localization sensors such as laser, ultrasonic, or radar is often infeasible for wall-climbing robots. Moreover, GPS-based localization is generally unreliable in these environments because of signal degradation caused by reinforced concrete and electromagnetic interference. Consequently, robot odometry remains the primary source of velocity and position information, despite being susceptible to drift caused by both systematic and non-systematic errors. The calibrations of the robot's systematic parameters were conducted using nonlinear optimization and Levenberg-Marquardt methods as Newton-Gauss and gradient-based model fitting methods, while Genetic algorithm and Particle swarm were used as stochastic-based methods for kinematic parameter calibration. Performance and results of the calibration methods and pose estimators were validated in detail with experiments on the experimental mobile wall climbing robot.

Solving Pallet loading Problem with Real-World Constraints

Efficient cargo packing and transport unit stacking play a vital role in enhancing logistics efficiency and reducing costs in the field of logistics. This article focuses on the challenging problem of loading transport units onto pallets, which belongs to the class of NP-hard problems. We propose a novel method for solving the pallet loading problem using a branch and bound algorithm, where there is a loading order of transport units. The derived algorithm considers only a heuristically favourable subset of possible positions of the transport units, which has a positive effect on computability. Furthermore, it is ensured that the pallet configuration meets real-world constraints, such as the stability of the position of transport units under the influence of transport inertial forces and gravity.

Interspecies Collaboration in the Design of Visual Identity: A Case Study

Design usually relies on human ingenuity, but the past decade has seen the field's toolbox expanding to Artificial Intelligence (AI) and its adjacent methods, making room for hybrid, algorithmic creations. This article aims to substantiate the concept of interspecies collaboration - that of natural and artificial intelligence - in the active co-creation of a visual identity, describing a case study of the Regional Center of Excellence for Robotic Technology (CRTA) which opened on 750 m2 in June 2021 within the University of Zagreb. The visual identity of the Center comprises three separately devised elements, each representative of the human-AI relationship and embedded in the institution's logo. Firstly, the letter "C" (from the CRTA acronym) was created using a Gaussian Mixture Model (GMM) applied to (x, y) coordinates that the neurosurgical robot RONNA, CRTA's flagship innovation, generated when hand-guided by a human operator. The second shape of the letter "C" was created by using the same (x, y) coordinates as inputs fed to a neural network whose goal was to output letters in a novel, AI-generated typography. A basic feedforward back-propagating neural network with two hidden layers was chosen for the task. The final and third design element was a trajectory the robot RONNA makes when performing a brain biopsy. As CRTA embodies a state-of-the-art venue for robotics research, the 'interspecies' approach was used to accentuate the importance of human-robot collaboration which is at the core of the newly opened Center, illustrating the potential of reciprocal and amicable relationship that humans could have with technology.