Can the Abdominal Aortic Atherosclerotic Plaque Index Predict Functional Outcomes after Robot-Assisted Partial Nephrectomy?

This study aims to evaluate the abdominal aortic atherosclerotic plaque index (API)'s predictive role in patients with pre-operatively or post-operatively developed chronic kidney disease (CKD) treated with robot-assisted partial nephrectomy (RAPN) for renal cell carcinoma (RCC). One hundred and eighty-three patients (134 with no pre- and post-operative CKD (no CKD) and 49 with persistent or post-operative CKD development (post-op CKD)) who underwent RAPN between January 2019 and January 2022 were deemed eligible for the analysis. The API was calculated using dedicated software by assessing the ratio between the CT scan atherosclerotic plaque volume and the abdominal aortic volume. The ROC regression model demonstrated the influence of API on CKD development, with an increasing effect according to its value (coefficient 0.13; 95% CI 0.04-0.23; p = 0.006). The Model 1 multivariable analysis of the predictors of post-op CKD found that the following are independently associated with post-op CKD: Charlson Comorbidity Index (OR 1.31; p = 0.01), last follow-up (FU) Δ%eGFR (OR 0.95; p < 0.01), and API ≥ 10 (OR 25.4; p = 0.01). Model 2 showed API ≥ 10 as the only factor associated with CKD development (OR 25.2; p = 0.04). The median follow-up was 22 months. Our results demonstrate API to be a strong predictor of post-operative CKD, allowing the surgeon to tailor the best treatment for each patient, especially in those who might be at higher risk of CKD.

Weakly Supervised Temporal Convolutional Networks for Fine-Grained Surgical Activity Recognition

Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of current vision-based activity recognition methods relies heavily on a high volume of manually annotated data. This data is difficult and time-consuming to generate and requires domain-specific knowledge. In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos. We introduce a step-phase dependency loss to exploit the weak supervision signal. We then employ a Single-Stage Temporal Convolutional Network (SS-TCN) with a ResNet-50 backbone, trained in an end-to-end fashion from weakly annotated videos, for temporal activity segmentation and recognition. We extensively evaluate and show the effectiveness of the proposed method on a large video dataset consisting of 40 laparoscopic gastric bypass procedures and the public benchmark CATARACTS containing 50 cataract surgeries.

TRandAugment: temporal random augmentation strategy for surgical activity recognition from videos

PURPOSE

Automatic recognition of surgical activities from intraoperative surgical videos is crucial for developing intelligent support systems for computer-assisted interventions. Current state-of-the-art recognition methods are based on deep learning where data augmentation has shown the potential to improve the generalization of these methods. This has spurred work on automated and simplified augmentation strategies for image classification and object detection on datasets of still images. Extending such augmentation methods to videos is not straightforward, as the temporal dimension needs to be considered. Furthermore, surgical videos pose additional challenges as they are composed of multiple, interconnected, and long-duration activities.

METHODS

This work proposes a new simplified augmentation method, called TRandAugment, specifically designed for long surgical videos, that treats each video as an assemble of temporal segments and applies consistent but random transformations to each segment. The proposed augmentation method is used to train an end-to-end spatiotemporal model consisting of a CNN (ResNet50) followed by a TCN.

RESULTS

The effectiveness of the proposed method is demonstrated on two surgical video datasets, namely Bypass40 and CATARACTS, and two tasks, surgical phase and step recognition. TRandAugment adds a performance boost of 1-6% over previous state-of-the-art methods, that uses manually designed augmentations.

CONCLUSION

This work presents a simplified and automated augmentation method for long surgical videos. The proposed method has been validated on different datasets and tasks indicating the importance of devising temporal augmentation methods for long surgical videos.

Measuring thermal spread during bipolar cauterizing using an experimental pneumoperitoneum and thermal sensors

Objective

During nerve-sparing robot-assisted radical prostatectomy (RARP) bipolar electrocoagulation is often used but its use is controversial for the possible thermal damage of neurovascular bundles. Aim of the study was to evaluate the spatial-temporal thermal distribution in the tissue and the correlation with the electrosurgery-induced tissue damage in a controlled, CO2-rich environment modelling the laparoscopy conditions..

Methods

We manufactured a sealed plexiglass chamber (SPC) equipped with sensors to reproduce experimentally the environmental conditions of pneumoperitoneum during RARP. We evaluated in 64 pig musculofascial tissues (PMTs) of approximately 3 cm³ × 3 cm³ × 2 cm³ the spatial-temporal thermal distribution in the tissue and the correlation with the electrosurgery-induced tissue damage in a controlled CO2-rich environment modeling the laparoscopy conditions. Critical heat spread of bipolar cauterizing during surgical procedure was assessed by the employment of a compact thermal camera (C2) with a small core sensor (60 × 80 microbolometer array in the range 7-14 μm).

Results

Bipolar instruments used at 30 W showed a thermal spread area of 18 mm² when applied for 2 s and 28 mm² when applied for 4 s. At 60 W, bipolar instruments showed a mean thermal spread and 19 mm² when applied for 2 s; and 21 mm² when applied for 4 s. Finally, histopathological analysis showed that thermal damage is distributed predominantly on the surface rather than in depth.

Conclusions

The application of these results is very interesting for the definition of an accurate use of bipolar cautery during nerve-sparing RARP. It demonstrates the feasibility of using miniaturized thermal sensors, thus addressing the potential for next developments regarding the design of thermal endoscopic devices for robotic use.

Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application.

Machine understanding surgical actions from intervention procedure textbooks

The automatic extraction of procedural surgical knowledge from surgery manuals, academic papers or other high-quality textual resources, is of the utmost importance to develop knowledge-based clinical decision support systems, to automatically execute some procedure's step or to summarize the procedural information, spread throughout the texts, in a structured form usable as a study resource by medical students. In this work, we propose a first benchmark on extracting detailed surgical actions from available intervention procedure textbooks and papers. We frame the problem as a Semantic Role Labeling task. Exploiting a manually annotated dataset, we apply different Transformer-based information extraction methods. Starting from RoBERTa and BioMedRoBERTa pre-trained language models, we first investigate a zero-shot scenario and compare the obtained results with a full fine-tuning setting. We then introduce a new ad-hoc surgical language model, named SurgicBERTa, pre-trained on a large collection of surgical materials, and we compare it with the previous ones. In the assessment, we explore different dataset splits (one in-domain and two out-of-domain) and we investigate also the effectiveness of the approach in a few-shot learning scenario. Performance is evaluated on three correlated sub-tasks: predicate disambiguation, semantic argument disambiguation and predicate-argument disambiguation. Results show that the fine-tuning of a pre-trained domain-specific language model achieves the highest performance on all splits and on all sub-tasks. All models are publicly released.

Concepts and Trends n Autonomy for Robot-Assisted Surgery

Surgical robots have been widely adopted with over 4000 robots being used in practice daily. However, these are telerobots that are fully controlled by skilled human surgeons. Introducing "surgeon-assist"-some forms of autonomy-has the potential to reduce tedium and increase consistency, analogous to driver-assist functions for lanekeeping, cruise control, and parking. This article examines the scientific and technical backgrounds of robotic autonomy in surgery and some ethical, social, and legal implications. We describe several autonomous surgical tasks that have been automated in laboratory settings, and research concepts and trends.

Thermal endoscope based on cost-effective LWIR camera cores

The implementation of a thermal endoscope based on the LWIR camera cores Lepton and a custom miniaturized electronics is reported. The sensor and the PCB can be inserted into a cylindrical protective case of diameter down to 15mm, inox tube or plastic, 3D printable envelope, with an optical window in Germanium. Two PCBs were developed for assembling the endoscope in two different schemes, to enable frontal or lateral thermal vision setup. The thermal endoscope unit is controlled by a Raspberry external unit. The Infrared Vision Software is provided for controlling the acquisition of thermal frames, and for the thermographic calculation of the object temperature from the input parameters on object surface emissivity and environment. In general, the device enables to perform thermography in applications in which traditional larger equipment cannot be employed, as nondestructive diagnostics in confined space in the engineering field. The thermal endoscope was designed with dimensions also compatible for robotic-assisted/traditional minimally-invasive surgery.

Robot-Assisted Electrical Impedance Scanning system for 2D Electrical Impedance Tomography tissue inspection

The electrical impedance tomography (EIT) technology is an important medical imaging approach to show the electrical characteristics and the homogeneity of a tissue region noninvasively. Recently, this technology has been introduced to the Robot Assisted Minimally Invasive Surgery (RAMIS) for assisting the detection of surgical margin with relevant clinical benefits. Nevertheless, most EIT technologies are based on a fixed multiple-electrodes probe which limits the sensing flexibility and capability significantly. In this study, we present a method for acquiring the EIT measurements during a RAMIS procedure using two already existing robotic forceps as electrodes. The robot controls the forceps tips to a series of predefined positions for injecting excitation current and measuring electric potentials. Given the relative positions of electrodes and the measured electric potentials, the spatial distribution of electrical conductivity in a section view can be reconstructed. Realistic experiments are designed and conducted to simulate two tasks: subsurface abnormal tissue detection and surgical margin localization. According to the reconstructed images, the system is demonstrated to display the location of the abnormal tissue and the contrast of the tissues' conductivity with an accuracy suitable for clinical applications.

Toward autonomous robotic prostate biopsy: a pilot study

Purpose

We present the validation of PROST, a robotic device for prostate biopsy. PROST is designed to minimize human error by introducing some autonomy in the execution of the key steps of the procedure, i.e., target selection, image fusion and needle positioning. The robot allows executing a targeted biopsy through ultrasound (US) guidance and fusion with magnetic resonance (MR) images, where the target was defined.

Methods

PROST is a parallel robot with 4 degrees of freedom (DOF) to orient the needle and 1 DOF to rotate the US probe. We reached a calibration error of less than 2 mm, computed as the difference between the needle positioning in robot coordinates and in the US image. The autonomy of the robot is given by the image analysis software, which employs deep learning techniques, the integrated image fusion algorithms and automatic computation of the needle trajectory. For safety reasons, the insertion of the needle is assigned to the doctor.

Results

System performance was evaluated in terms of positioning accuracy. Tests were performed on a 3D printed object with nine 2-mm spherical targets and on an anatomical commercial phantom that simulates human prostate with three lesions and the surrounding structures. The average accuracy reached in the laboratory experiments was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ 1.30 \pm 0.44\, \text {mm}$$\end{document}1.30±0.44mm in the first test and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ 1.54 \pm 0.34\, \text {mm}$$\end{document}1.54±0.34mm in the second test.

Conclusions

We introduced a first prototype of a prostate biopsy robot that has the potential to increase the detection of clinically significant prostate cancer and, by including some level of autonomy, to simplify the procedure, to reduce human errors and shorten training time. The use of a robot for the biopsy of the prostate will create the possibility to include also a treatment, such as focal ablation, to be delivered through the same system.

An electrical bioimpedance scanning system for subsurface tissue detection in Robot Assisted Minimally Invasive Surgery

In Robot Assisted Minimally Invasive Surgery, discriminating critical subsurface structures is essential to make the surgical procedure safer and more efficient. In this paper, a novel robot assisted electrical bio-impedance scanning (RAEIS) system is developed and validated using a series of experiments. The proposed system constructs a tri-polar sensing configuration for tissue homogeneity inspection. Specifically, two robotic forceps are used as electrodes for applying electric current and measuring reciprocal voltages relative to a ground electrode which is placed distal from the measuring site. Compared to the other existing electrical bioimpedance sensing technology, the proposed system is able to use miniaturized electrodes to measure a site flexibly with enhanced subsurfacial detection capability. In this paper, we present the concept, the modeling of the sensing method, the hardware design, and the system calibration. Subsequently, a series of experiments are conducted for system evaluation including finite element simulation, saline solution bath experiments and experiments based on ex vivo animal tissues. The experimental results demonstrate that the proposed system can measure the resistivity of the material with high accuracy, and detect a subsurface non-homogeneous object with 100% success rate. The proposed parameters estimation algorithm is able to approximate the resistivity and the depth of the subsurface object effectively with one fast scanning.

Inductive learning of answer set programs for autonomous surgical task planning

The quality of robot-assisted surgery can be improved and the use of hospital resources can be optimized by enhancing autonomy and reliability in the robot’s operation. Logic programming is a good choice for task planning in robot-assisted surgery because it supports reliable reasoning with domain knowledge and increases transparency in the decision making. However, prior knowledge of the task and the domain is typically incomplete, and it often needs to be refined from executions of the surgical task(s) under consideration to avoid sub-optimal performance. In this paper, we investigate the applicability of inductive logic programming for learning previously unknown axioms governing domain dynamics. We do so under answer set semantics for a benchmark surgical training task, the ring transfer. We extend our previous work on learning the immediate preconditions of actions and constraints, to also learn axioms encoding arbitrary temporal delays between atoms that are effects of actions under the event calculus formalism. We propose a systematic approach for learning the specifications of a generic robotic task under the answer set semantics, allowing easy knowledge refinement with iterative learning. In the context of 1000 simulated scenarios, we demonstrate the significant improvement in performance obtained with the learned axioms compared with the hand-written ones; specifically, the learned axioms address some critical issues related to the plan computation time, which is promising for reliable real-time performance during surgery.

Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures

PURPOSE

Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities in robot-assisted surgeries. Prior works have focused on recognizing either coarse activities, such as phases, or fine-grained activities, such as gestures. This work aims at jointly recognizing two complementary levels of granularity directly from videos, namely phases and steps.

METHODS

We introduce two correlated surgical activities, phases and steps, for the laparoscopic gastric bypass procedure. We propose a multi-task multi-stage temporal convolutional network (MTMS-TCN) along with a multi-task convolutional neural network (CNN) training setup to jointly predict the phases and steps and benefit from their complementarity to better evaluate the execution of the procedure. We evaluate the proposed method on a large video dataset consisting of 40 surgical procedures (Bypass40).

RESULTS

We present experimental results from several baseline models for both phase and step recognition on the Bypass40. The proposed MTMS-TCN method outperforms single-task methods in both phase and step recognition by 1-2% in accuracy, precision and recall. Furthermore, for step recognition, MTMS-TCN achieves a superior performance of 3-6% compared to LSTM-based models on all metrics.

CONCLUSION

In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on a gastric bypass dataset with multi-level annotations. The proposed method shows that the joint modeling of phases and steps is beneficial to improve the overall recognition of each type of activity.

Automatic detection of procedural knowledge in robotic-assisted surgical texts

Purpose

The automatic extraction of knowledge about intervention execution from surgical manuals would be of the utmost importance to develop expert surgical systems and assistants. In this work we assess the feasibility of automatically identifying the sentences of a surgical intervention text containing procedural information, a subtask of the broader goal of extracting intervention workflows from surgical manuals.

Methods

We frame the problem as a binary classification task. We first introduce a new public dataset of 1958 sentences from robotic surgery texts, manually annotated as procedural or non-procedural. We then apply different classification methods, from classical machine learning algorithms, to more recent neural-network approaches and classification methods exploiting transformers (e.g., BERT, ClinicalBERT). We also analyze the benefits of applying balancing techniques to the dataset.

Results

The architectures based on neural-networks fed with FastText’s embeddings and the one based on ClinicalBERT outperform all the tested methods, empirically confirming the feasibility of the task. Adopting balancing techniques does not lead to substantial improvements in classification.

Conclusion

This is the first work experimenting with machine / deep learning algorithms for automatically identifying procedural sentences in surgical texts. It also introduces the first public dataset that can be used for benchmarking different classification methods for the task.

Actuation Selection for Assistive Exoskeletons: Matching Capabilities to Task Requirements

Selecting actuators for assistive exoskeletons involves decisions in which designers usually face contrasting requirements. While certain choices may depend on the application context or design philosophy, it is generally desirable to avoid oversizing actuators in order to obtain more lightweight and transparent systems, ultimately promoting the adoption of a given device. In many cases, the torque and power requirements can be relaxed by exploiting the contribution of an elastic element acting in mechanical parallel. This contribution considers one such case and introduces a methodology for the evaluation of different actuator choices resulting from the combination of different motors, reduction gears, and parallel stiffness profiles, helping to match actuator capabilities to the task requirements. Such methodology is based on a graphical tool showing how different design choices affect the actuator as a whole. To illustrate the approach, a back-support exoskeleton for lifting tasks is considered as a case study.

Design and Integration of Electrical Bio-impedance Sensing in Surgical Robotic Tools for Tissue Identification and Display

The integration of intra-operative sensors into surgical robots is a hot research topic since this can significantly facilitate complex surgical procedures by enhancing surgical awareness with real-time tissue information. However, currently available intra-operative sensing technologies are mainly based on image processing and force feedback, which normally require heavy computation or complicated hardware modifications of existing surgical tools. This paper presents the design and integration of electrical bio-impedance sensing into a commercial surgical robot tool, leading to the creation of a novel smart instrument that allows the identification of tissues by simply touching them. In addition, an advanced user interface is designed to provide guidance during the use of the system and to allow augmented-reality visualization of the tissue identification results. The proposed system imposes minor hardware modifications to an existing surgical tool, but adds the capability to provide a wealth of data about the tissue being manipulated. This has great potential to allow the surgeon (or an autonomous robotic system) to better understand the surgical environment. To evaluate the system, a series of ex-vivo experiments were conducted. The experimental results demonstrate that the proposed sensing system can successfully identify different tissue types with 100% classification accuracy. In addition, the user interface was shown to effectively and intuitively guide the user to measure the electrical impedance of the target tissue, presenting the identification results as augmented-reality markers for simple and immediate recognition.

Correction to: Position-based modeling of lesion displacement in ultrasound-guided breast biopsy

The original version of this article unfortunately contained a mistake.

Position-based modeling of lesion displacement in ultrasound-guided breast biopsy

PURPOSE

Although ultrasound (US) images represent the most popular modality for guiding breast biopsy, malignant regions are often missed by sonography, thus preventing accurate lesion localization which is essential for a successful procedure. Biomechanical models can support the localization of suspicious areas identified on a preoperative image during US scanning since they are able to account for anatomical deformations resulting from US probe pressure. We propose a deformation model which relies on position-based dynamics (PBD) approach to predict the displacement of internal targets induced by probe interaction during US acquisition.

METHODS

The PBD implementation available in NVIDIA FleX is exploited to create an anatomical model capable of deforming online. Simulation parameters are initialized on a calibration phantom under different levels of probe-induced deformations; then, they are fine-tuned by minimizing the localization error of a US-visible landmark of a realistic breast phantom. The updated model is used to estimate the displacement of other internal lesions due to probe-tissue interaction.

RESULTS

The localization error obtained when applying the PBD model remains below 11 mm for all the tumors even for input displacements in the order of 30 mm. This proposed method obtains results aligned with FE models with faster computational performance, suitable for real-time applications. In addition, it outperforms rigid model used to track lesion position in US-guided breast biopsies, at least halving the localization error for all the displacement ranges considered.

CONCLUSION

Position-based dynamics approach has proved to be successful in modeling breast tissue deformations during US acquisition. Its stability, accuracy and real-time performance make such model suitable for tracking lesions displacement during US-guided breast biopsy.

Formal Verification of Medical CPS

The use of robots in operating rooms improves safety and decreases patient recovery time and surgeon fatigue, but it introduces new potential hazards that can lead to severe injury or even the loss of human life. Thus, safety has been perceived as a crucial system property since the early days by the industry, the medical community, and the regulatory agents. In this article, we discuss the application of the mathematically rigorous technique known as Formal Verification to analyze the safety properties of a laser incision case study, and we assess its safe and predictable operation. Like all formal methods approaches, our analysis has three distinct components: a method to create a model of the system, a language to specify the properties, and a strategy to prove rigorously that the behavior of the model fulfills the desired properties. The model of the system takes the form of a hybrid automaton consisting of a discrete control part that operates in a continuous environment. The safety constraints are formalized as reachability properties of the hybrid automaton model, while the verification strategy exploits the capabilities of the tool A riadne to address the verification problem and answer the related questions ranging from safety to efficiency and effectiveness.

Rapid and sensitive detection of viral nucleic acids using silicon microchips

Rapid and sensitive amplification of viral nucleic acids is feasible on a flexible silicon microchip technology platform.

Neurophysiological measures for users' training objective assessment during simulated robot-assisted laparoscopic surgery

Minimally invasive surgery can be performed with robotic assistance, as evolution of laparoscopic surgery. Robots for assisted surgery are far from being user friendly and require extensive training. To this end, ad-hoc devices and experimental set-ups are needed. The da Vinci system is one of the most diffused surgical robotics technology. The aim of the study was two-fold: i) to propose a neurophysiological measure by which objectively assess the learning progress of the users by means of a simulator of the da Vinci system, and ii) to demonstrate the advantages of cognitive assessment with respect to the standard methodologies for the evaluation of training efficiency.

A Deformable Smart Skin for Continuous Sensing Based on Electrical Impedance Tomography

In this paper, we present a low-cost, adaptable, and flexible pressure sensor that can be applied as a smart skin over both stiff and deformable media. The sensor can be easily adapted for use in applications related to the fields of robotics, rehabilitation, or costumer electronic devices. In order to remove most of the stiff components that block the flexibility of the sensor, we based the sensing capability on the use of a tomographic technique known as Electrical Impedance Tomography. The technique allows the internal structure of the domain under study to be inferred by reconstructing its conductivity map. By applying the technique to a material that changes its resistivity according to applied forces, it is possible to identify these changes and then localise the area where the force was applied. We tested the system when applied to flat and curved surfaces. For all configurations, we evaluate the artificial skin capabilities to detect forces applied over a single point, over multiple points, and changes in the underlying geometry. The results are all promising, and open the way for the application of such sensors in different robotic contexts where deformability is the key point.

Multiplex SNP genotyping in whole blood using an integrated microfluidic lab-on-a-chip

Pharmacogenetics has often been touted as a cornerstone for precision medicine as detailed knowledge of a specific genetic makeup may allow for accurate predictions of a patient's individual drug response. Still, the widespread use of genetic tests is limited as they remain expensive and cumbersome, requiring sophisticated tools and highly trained personnel. In order for pharmacogenetics to reach its full potential, more cost-effective and easily accessible genotyping methods are desired. To meet these challenges, we present a silicon-based integrated microsystem for the detection of multiple single nucleotide polymorphisms (SNPs) directly from human blood. The device combines a blood lysis chamber, a cross-flow filter, a T-junction mixer, and a microreactor for quantitative polymerase chain reaction (qPCR). Using this device, successful on-chip genotyping of two clinically relevant SNPs in human CYP2C9 gene was demonstrated with TaqMan assays, starting from blood. The two SNPs were detected simultaneously by introducing a sequence of plugs, each containing a different set of primers and probes. The method can be easily extended to detect several SNPs. The microsystem described here offers a rapid, reproducible, and accurate sample-to-answer technology enabling multiplex SNP profiling in point-of-care settings, bringing pharmacogenetics-based precision medicine a step closer to reality.

Motion Planning in Dynamic Environments Using Velocity Obstacles

This paper presents a method for robot motion planning in dynamic environments. It consists of selecting avoidance maneuvers to avoid static and moving obstacles in the velocity space, based on the cur rent positions and velocities of the robot and obstacles. It is a first- order method, since it does not integrate velocities to yield positions as functions of time.

The avoidance maneuvers are generated by selecting robot ve locities outside of the velocity obstacles, which represent the set of robot velocities that would result in a collision with a given obstacle that moves at a given velocity, at some future time. To ensure that the avoidance maneuver is dynamically feasible, the set of avoidance velocities is intersected with the set of admissible velocities, defined by the robot's acceleration constraints. Computing new avoidance maneuvers at regular time intervals accounts for general obstacle trajectories.

The trajectory from start to goal is computed by searching a tree of feasible avoidance maneuvers, computed at discrete time intervals. An exhaustive search of the tree yields near-optimal trajectories that either minimize distance or motion time. A heuristic search of the tree is applicable to on-line planning. The method is demonstrated for point and disk robots among static and moving obstacles, and for an automated vehicle in an intelligent vehicle highway system scenario.

Minimalist Jumping Robots for Celestial Exploration

In this paper we describe a novel approach to the design and deployment of small and minimally actuated jumping or hopping robots that are suitable for exploring the unstructured terrains of celestial bodies. We introduce the basic jumping mobility paradigm, as well as the evolution of our hopping robot concept by way of the main prototypes that we have developed. These prototypes show that a small number of actuators can control the vehicle's steering, hopping, and self-righting motions. The last prototype is equipped with wheels so that precision motion can be combined with gross hopping motion. Lessons learned during the development of these prototypes have general applicability to the design of jumping robots. In addition to reviewing the issues relevant to the design of jumping systems, in this paper we describe some of the key mechanisms that enable our approach, we summarize tests obtained with these systems, and we present our future plans of localization and sensing for hopping mobility.

Navigating a Robotic Wheelchair in a Railway Station during Rush Hour

This paper describes the hardware design, control, and navigation system of and some preliminary experiments with the robotic wheelchair Mobility Aid for elderly and disabled people (MAid). MAid’s general task is to transport people with severely impaired motion skills. The authors did not set out to reinvent and redevelop the set of standard skills of so-called intelligent wheelchairs, such as Follow Wall, FollowCorridor, PassDoorway, which are commonly described in the literature. These maneuvers require motion control skills that disabled people, in spite of their disabilities, are eager to learn and quite good at using. Instead, this work focused on generalizing the approach to fine motion control by considering those maneuvers identified as very burdensome due to their duration and required concentration. One of these functions is deliberative locomotion in rapidly changing, large-scale environments, such as shopping malls, entry halls of theaters, and concourses of airports or railway stations, where tens or hundreds of people and objects move around. MAid’s performance was tested in the central station of Ulm during rush hour and in the exhibition halls of the Hannover Messe ’98, the largest industrial fair in the world. Altogether, MAid has survived more than 36 h of testing in public, crowded environments with heavy passenger traffic.

A Design and Control Environment for Internet-Based Telerobotics

This paper describes an environment for the design, simulation, and control of Internet-based force-reflecting telerobotic systems. We define these systems as using a segment of the computer network to connect the master to the slave. Computer networks introduce a time delay that is best described by a time-varying random process. Thus, known techniques for controlling time-delay telerobots are not directly applicable, and an environment for iterative designing and testing is necessary. The underlying software architecture sup ports tools for modeling the delay of the computer network, design ing a stable controller, simulating the performance of a telerobotic system, and testing the control algorithms using a force-reflecting input device. Furthermore, this setup provides data about including the Internet into more general telerobotic control architectures. To demonstrate the features of this environment, the complete proce dure for the design of a telerobotic controller is discussed. First, the delay parameters of an Internet segment are identified by prob ing the network. Then, these parameters are used in the design of a controller that includes a quasi-optimal estimator to compensate small data losses. Finally, simulations of the complete telerobotic system and emulations using a planar force-reflecting master and a virtual slave exemplify a typical design-and-test sequence.

A fish perspective: detecting flow features while moving using an artificial lateral line in steady and unsteady flow

For underwater vehicles to successfully detect and navigate turbulent flows, sensing the fluid interactions that occur is required. Fish possess a unique sensory organ called the lateral line. Sensory units called neuromasts are distributed over their body, and provide fish with flow-related information. In this study, a three-dimensional fish-shaped head, instrumented with pressure sensors, was used to investigate the pressure signals for relevant hydrodynamic stimuli to an artificial lateral line system. Unsteady wakes were sensed with the objective to detect the edges of the hydrodynamic trail and then explore and characterize the periodicity of the vorticity. The investigated wakes (Kármán vortex streets) were formed behind a range of cylinder diameter sizes (2.5, 4.5 and 10 cm) and flow velocities (9.9, 19.6 and 26.1 cm s(-1)). Results highlight that moving in the flow is advantageous to characterize the flow environment when compared with static analysis. The pressure difference from foremost to side sensors in the frontal plane provides us a useful measure of transition from steady to unsteady flow. The vortex shedding frequency (VSF) and its magnitude can be used to differentiate the source size and flow speed. Moreover, the distribution of the sensing array vertically as well as the laterally allows the Kármán vortex paired vortices to be detected in the pressure signal as twice the VSF.