Luigi Manfredi

CoCoRo -- The Self-Aware Underwater Swarm

The EU-funded CoCoRo project studies heterogeneous swarms of AUVs used for the purposes of under water monitoring and search. The CoCoRo underwater swarm system will combine bio-inspired motion principles with biologically-derived collective cognition mechanisms to provide a novel robotic system that is scalable, reliable and flexible with respect its behavioural potential. We will investigate and develop swarm-level emergent self-awareness, taking biological inspiration from fish, honeybees, the immune system and neurons. Low-level, local information processing will give rise to collective-level memory and cognition. CoCoRo will develop a novel bio-inspired operating system whose default behaviour will be to provide AUV shoaling functionality and the maintenance of swarm coherence. Collective discrimination of environmental properties will be processed on an individual-or on a collective-level given the cognitive capabilities of the AUVs. We will investigate collective self-recognition through experiments inspired by ethology and psychology, allowing for the quantification of collective cognition.

A novel autonomous, bioinspired swimming robot developed by neuroscientists and bioengineers

This paper describes the development of a new biorobotic platform inspired by the lamprey. Design, fabrication and implemented control are all based on biomechanical and neuroscientific findings on this eel-like fish. The lamprey model has been extensively studied and characterized in recent years because it possesses all basic functions and control mechanisms of higher vertebrates, while at the same time having fewer neurons and simplified neural structures. The untethered robot has a flexible body driven by compliant actuators with proprioceptive feedback. It also has binocular vision for vision-based navigation. The platform has been successfully and extensively experimentally tested in aquatic environments, has high energy efficiency and is ready to be used as investigation tool for high level motor tasks.

Design of a Robotic Module for Autonomous Exploration and Multimode Locomotion

The mechanical design of a novel robotic module for a self-reconfigurable modular robotic system is presented in this paper. The robotic module, named Scout robot, was designed to serve both as a fully sensorized autonomous miniaturized robot for exploration in unstructured environments and as a module of a larger robotic organism. The Scout robot has a quasi-cubic shape of 105 mm × 105 mm × 123.5 mm, and weighs less than 1 kg. It is provided with tracks for 2-D locomotion and with two rotational DoFs for reconfiguration and macrolocomotion when assembled in a modular structure. A laser sensor was incorporated to measure the distance and relative angle to an object, and image-guided locomotion was successfully demonstrated. In addition, five Scout robot prototypes were fabricated, and multimodal locomotion of assembled robots was demonstrated.

Image-based 3D modeling and validation of radiofrequency interstitial tumor ablation using a tissue-mimicking breast phantom

PurposeMinimally invasive treatment of solid cancers, especially in the breast and liver, remains clinically challenging, despite a variety of treatment modalities, including radiofrequency ablation (RFA), microwave ablation or high-intensity focused ultrasound. Each treatment modality has advantages and disadvantages, but all are limited by placement of a probe or US beam in the target tissue for tumor ablation and monitoring. The placement is difficult when the tumor is surrounded by large blood vessels or organs. Patient-specific image-based 3D modeling for thermal ablation simulation was developed to optimize treatment protocols that improve treatment efficacy.MethodsA tissue-mimicking breast gel phantom was used to develop an image-based 3D computer-aided design (CAD) model for the evaluation of a planned RF ablation. First, the tissue-mimicking gel was cast in a breast mold to create a 3D breast phantom, which contained a simulated solid tumor. Second, the phantom was imaged in a medical MRI scanner using a standard breast imaging MR sequence. Third, the MR images were converted into a 3D CAD model using commercial software (ScanIP, Simpleware), which was input into another commercial package (COMSOL Multiphysics) for RFA simulation and treatment planning using a finite element method (FEM). For validation of the model, the breast phantom was experimentally ablated using a commercial (RITA) RFA electrode and a bipolar needle with an electrosurgical generator (DRE ASG-300). The RFA results obtained by pre-treatment simulation were compared with actual experimental ablation.ResultsA 3D CAD model, created from MR images of the complex breast phantom, was successfully integrated with an RFA electrode to perform FEM ablation simulation. The ablation volumes achieved both in the FEM simulation and the experimental test were equivalent, indicating that patient-specific models can be implemented for pre-treatment planning of solid tumor ablation.ConclusionA tissue-mimicking breast gel phantom and its MR images were used to perform FEM 3D modeling and validation by experimental thermal ablation of the tumor. Similar patient-specific models can be created from preoperative images and used to perform finite element analysis to plan radiofrequency ablation. Clinically, the method can be implemented for pre-treatment planning to predict the effect of an individual’s tissue environment on the ablation process, and this may improve the therapeutic efficacy.

Implementation of a neurophysiological model of saccadic eye movements on an anthropomorphic robotic head

In this paper we investigated the relevance of a robotic implementation in the development and validation of a neurophysiological model of the generation of saccadic eye movements. To this aim, a well-characterized model of the brainstem saccadic circuitry was implemented on a humanoid robot head with 7 degrees of freedom (DOFs), which was designed to mimic the human head in terms of the physical dimensions (i.e. geometry and masses), the kinematics (i.e. number of DOFs and ranges of motion), the dynamics (i.e. velocities and accelerations), and the functionality (i.e. the ocular movements of vergence, smooth pursuit and saccades). Our implementation makes the robot head execute saccadic eye movements upon a visual stimulus appearing in the periphery of the robot visual field, by reproducing the following steps: projection or the camera images onto collicular images, according to the modeled mapping between the retina and the superior colliculus (SC); transformation of the retinotopic coordinates of the stimulus obtained in the camera reference frame into their corresponding projections on the SC; spatio-temporal transformation of these coordinates according to what is known to happen in the brainstem saccade burst generator of primates; and execution of the eye movement by controlling one eye motor of the robot, in velocity. The capabilities of the robot head to execute saccadic movements have been tested with respect to the neurophysiological model implemented, in view of the use of this robotic implementation for validating and tuning the model itself, in further focused experimental trials

An underwater reconfigurable robot with bioinspired electric sense

Morphology, perception and locomotion are three key features highly inter-dependent in robotics. This paper gives an overview of an underwater modular robotic platform equipped with a bio-inspired electric sense. The platform is reconfigurable in the sense that it can split into independent rigid modules and vice-versa. Composed of 9 modules, the longer entity can swim like an eel over long distances, while once detached, each of its modules is efficient for small displacements with a high accuracy. Challenges are to mechanically ensure the morphology changes and to do it automatically. Electric sense is used to guide the modules during docking phases and to navigate in unknown scenes. Several aspects of the design of the robot are described and a particular attention is paid to the inter-module docking system. The feasibility of the design is assessed through experiments.

A bioinspired autonomous swimming robot as a tool for studying goal-directed locomotion

The bioinspired approach has been key in combining the disciplines of robotics with neuroscience in an effective and promising fashion. Indeed, certain aspects in the field of neuroscience, such as goal-directed locomotion and behaviour selection, can be validated through robotic artefacts. In particular, swimming is a functionally important behaviour where neuromuscular structures, neural control architecture and operation can be replicated artificially following models from biology and neuroscience. In this article, we present a biomimetic system inspired by the lamprey, an early vertebrate that locomotes using anguilliform swimming. The artefact possesses extra- and proprioceptive sensory receptors, muscle-like actuation, distributed embedded control and a vision system. Experiments on optimised swimming and on goal-directed locomotion are reported, as well as the assessment of the performance of the system, which shows high energy efficiency and adaptive behaviour. While the focus is on providing a robotic platform for testing biological models, the reported system can also be of major relevance for the development of engineering system applications.

Bioinspired velocity control of fast gaze shifts on a robotic anthropomorphic head

Abstract In this paper we address the problem of executing fast gaze shifts toward a visual target with a robotic platform. The robotic platform is an anthropomorphic head with seven degrees of freedom (DOFs) that was designed to mimic the physical dimensions (i.e. geometry and masses), the performances (i.e. angles and velocities) and the functional abilities (i.e. neck-movements and eyes vergence) of the human head. In our approach the “gold performance” of the robotic head is represented by the accurate eye-head coordination that is observed during head-free gaze saccades in humans. To this aim, we implemented and tested on the robotic head a well-characterized, biologically inspired model of gaze control and we investigate the effectiveness of the bioinspired paradigm to achieve an appropriate control of the multi-DOF robotic head. Moreover, in order to verify if the proposed model can reproduce the typical patterns of actual human movements, we performed a quantitative investigation of the relation between movement amplitude, duration and peak velocity. In the latter case, we compared the actual robot performances with existing data on human main sequence which is known to provide a general method for quantifying the dynamic of oculomotor control. The obtained results confirmed (1) the ability of the proposed bioinspired control to achieve and maintain and stable fixation of the target which was always well-positioned within the fovea and (2) the ability to reproduce the typical human main sequence diagrams which were never been successfully implemented on a fully anthropomorphic head. Even if fundamentally aimed at the experimental investigation of the underlying neurophysiologic models, the present study is also intended to provide some possible relevant solutions to the development of human-like eye movements in humanoid robots.

RobotCub implementation of real-time least-square fitting of ellipses

This paper presents the implementation of a new algorithm for pattern recognition in machine vision developed in our laboratory applied to the RobotCub humanoid robotics platform simulator. The algorithm is a robust and direct method for the least-square fitting of ellipses to scattered data. RobotCub is an open source platform, born to study the development of neuro-scientific and cognitive skills in human beings, especially in children. By the estimation of the surrounding objects properties (such as dimensions, distances, etc...) a subject can create a topographic map of the environment, in order to navigate through it without colliding with obstacles. In this work we implemented the method of the least-square fitting of ellipses of Maini (EDFE), previously developed in our laboratory, in a robotics context. Moreover, we compared its performance with the hough transform, and others least-square ellipse fittings techniques. We used our system to detect spherical objects, and we applied it to the simulated RobotCub platform. We performed several tests to prove the robustness of the algorithm within the overall system, and finally we present our results.

A model of the smooth pursuit eye movement with prediction and learning

Smooth pursuit is one of the five main eye movements in humans, consisting of tracking a steadily moving visual target. Smooth pursuit is a good example of a sensory-motor task that is deeply based on prediction: tracking a visual target is not possible by correcting the error between the eye and the target position or velocity with a feedback loop, but it is only possible by predicting the trajectory of the target. This paper presents a model of smooth pursuit based on prediction and learning. It starts from amodel of the neuro-physiological system proposed by Shibata and Schaal Shibata et al., Neural Networks, vol. 18, pp. 213-224, 2005. The learning component added here decreases the prediction time in the case of target dynamics already experienced by the system. In the implementation described here, the convergence time is, after the learning phase, 0.8 s.