Towards an anthropomorphic design of minimally invasive instrumentation for soft tissue robotic surgery

Minimally invasive procedures, such as laparoscopy, have significantly decreased blood loss, postoperative morbidity and length of hospital stay. Robot-assisted Minimally Invasive Surgery (MIS) has offered refined accuracy and more ergonomic instruments for surgeons, further minimizing trauma to the patient [1]. On the other hand, training surgeons in minimally invasive surgical procedures is becoming increasingly long and arduous [2]. In this paper, we outline the rationale of a novel design of instruments for robotic surgery with increased dexterity that will provide more natural manipulation of soft tissues. The proposed system will not only reduce the training time for surgeons but also improve the ergonomics of the procedure. © 2012 Springer-Verlag

Toward Bio-Inspired Tactile Sensing Capsule Endoscopy for Detection of Submucosal Tumors

© 2016 IEEE. Here, we present a method for lump characterization using a bio-inspired remote tactile sensing capsule endoscopy system. While current capsule endoscopy utilizes cameras to diagnose lesions on the surface of the gastrointestinal tract lumen, this proposal uses remote palpation to stimulate a bio-inspired tactile sensing surface that deforms under the impression of both hard and soft raised objects. Current capsule endoscopy utilizes cameras to visually diagnose lesions on the surface of the gastrointestinal tract. Our approach introduces remote palpation by deploying a bio-inspired tactile sensor that deforms when pressed against soft or hard lumps. This can enhance visual inspection of lesions and provide more information about the structure of the lesions. Using classifier systems, we have shown that lumps of different sizes, shapes, and hardnesses can be distinguished in a synthetic test environment. This is a promising early start toward achieving a remote palpation system used inside the GI tract that will utilize the clinician's sense of touch

A novel bio-inspired tactile tumour detection concept for capsule endoscopy

Examination of the gastrointestinal(GI) tract has traditionally been performed using endoscopy tools that allow a surgeon to see the inside of the lining of the digestive tract. Endoscopes are rigid or flexible tubes that use fibre-optics or cameras to visualise tissues in natural orifices. This can be an uncomfortable and very invasive procedure for the patient. © 2014 Springer International Publishing

A Novel Design for a Robot Grappling Hook for use in a Nuclear Cave Environment

© 2016 Within the field of robotics there exist few designs for detachable grappling hooks. This paper focusses on the novel design of a detachable grappling hook for use within a nuclear cave environment. The design seeks to exploit the complex network of pipes that is present within a nuclear cave. It is hoped that the grapple may be used to aid with mapping and characterisation of the nuclear cave, as well as increasing the movement capabilities of robots within the cave. It is shown that our prototype grapple is able to support on average 2.4kg of mass, or thirty times its own weight. In addition when dropped from a height of 7.5cm, which removes ballistic instability, the grapple is able to engage itself 87% of the time. Finally the minimum speed that the grapple must be travelling, in order to secure itself to its target, is found to be 1.08m/s

Detrive: Imitation Learning with Transformer Detection for End-to-End Autonomous Driving

This Paper proposes a novel Transformer-based end-to-end autonomous driving model named Detrive. This model solves the problem that the past end-to-end models cannot detect the position and size of traffic participants. Detrive uses an end-to-end transformer based detection model as its perception module; a multi-layer perceptron as its feature fusion network; a recurrent neural network with gate recurrent unit for path planning; and two controllers for the vehicle's forward speed and turning angle. The model is trained with an on-line imitation learning method. In order to obtain a better training set, a reinforcement learning agent that can directly obtain a ground truth bird's-eye view map from the Carla simulator as a perceptual output, is used as teacher for the imitation learning. The trained model is tested on the Carla's autonomous driving benchmark. The results show that the Transformer detector based end-to-end model has obvious advantages in dynamic obstacle avoidance compared with the traditional classifier based end-to-end model.Comment: 7 pages, 5 figures, DISA 202

Unicellular self-healing electronic array

This paper presents on-line fault detection and fault repair capability of our Unitronics architecture, based on a bio-inspired prokaryotic bacterial colony model. At the device programming level, it appears as a cellular FPGA-like system; however, underlying structures transpose it into an inherently self-healing and fault tolerant electronics system. An e-puck object avoidance robot controller was built to demonstrate all the underlying theories of our research. The robot successfully demonstrated that it was able to cope with multiple, simultaneously occurring faults on-line whilst the robot was being controlled to move in a „figure 8‟-like manner. Integrity of the system is continuously monitored on-line, and if a fault is detected its location is automatically identified. Detection will trigger an on-line self-repair process. The amount of repair only depends on the number of spare cells the system is equipped with. The embedded fault repair mechanism uses significantly less memory for gene storage and considerably less hardware overall for target system implementation than any previously proposed bio-inspired architecture

Audio Localization for Robots Using Parallel Cerebellar Models

© 2016 IEEE. A robot audio localization system is presented that combines the outputs of multiple adaptive filter models of the Cerebellum to calibrate a robot's audio map for various acoustic environments. The system is inspired by the MOdular Selection for Identification and Control (MOSAIC) framework. This study extends our previous work that used multiple cerebellar models to determine the acoustic environment in which a robot is operating. Here, the system selects a set of models and combines their outputs in proportion to the likelihood that each is responsible for calibrating the audio map as a robot moves between different acoustic environments or contexts. The system was able to select an appropriate set of models, achieving a performance better than that of a single model trained in all contexts, including novel contexts, as well as a baseline generalized cross correlation with phase transform sound source localization algorithm. The main contribution of this letter is the combination of multiple calibrators to allow a robot operating in the field to adapt to a range of different acoustic environments. The best performances were observed where the presence of a Responsibility Predictor was simulated

An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification

Simulation-based verification is beneficial for assessing otherwise dangerous or costly on-road testing of autonomous vehicles (AV). This paper addresses the challenge of efficiently generating effective tests for simulation-based AV verification using software testing agents. The multi-agent system (MAS) programming paradigm offers rational agency, causality and strategic planning between multiple agents. We exploit these aspects for test generation, focusing in particular on the generation of tests that trigger the precondition of an assertion. On the example of a key assertion we show that, by encoding a variety of different behaviours respondent to the agent's perceptions of the test environment, the agency-directed approach generates twice as many effective tests than pseudo-random test generation, while being both efficient and robust. Moreover, agents can be encoded to behave naturally without compromising the effectiveness of test generation. Our results suggest that generating tests using agency-directed testing significantly improves upon random and simultaneously provides more realistic driving scenarios.Comment: 18 pages, 8 figure

An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification

Simulation-based verification is beneficial for assessing otherwise dangerous or costly on-road testing of autonomous vehicles (AV). This paper addresses the challenge of efficiently generating effective tests for simulation-based AV verification using software testing agents. The multi-agent system (MAS) programming paradigm offers rational agency, causality and strategic planning between multiple agents. We exploit these aspects for test generation, focusing in particular on the generation of tests that trigger the precondition of an assertion. On the example of a key assertion we show that, by encoding a variety of different behaviours respondent to the agent's perceptions of the test environment, the agency-directed approach generates twice as many effective tests than pseudo-random test generation, while being both efficient and robust. Moreover, agents can be encoded to behave naturally without compromising the effectiveness of test generation. Our results suggest that generating tests using agency-directed testing significantly improves upon random and simultaneously provides more realistic driving scenarios.Comment: 18 pages, 8 figure