Robotic simulators for tissue examination training with multimodal sensory feedback

Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordination, mainly involving haptic, visual, and auditory feedback. The skills clinicians have to learn can be as subtle as regulating finger pressure with breathing, choosing palpation action, monitoring involuntary facial and vocal expressions in response to palpation, and using pain expressions both as a source of information and as a constraint on physical examination. Patient simulators can provide a safe learning platform to novice physicians before trying real patients. This paper reviews state-of-the-art medical simulators for the training for the first time with a consideration of providing multimodal feedback to learn as many manual examination techniques as possible. The study summarizes current advances in tissue examination training devices simulating different medical conditions and providing different types of feedback modalities. Opportunities with the development of pain expression, tissue modeling, actuation, and sensing are also analyzed to support the future design of effective tissue examination simulators

Design and Implementation of an Interactive Surface System with Controllable Shape and Softness

「平面的で硬い」という従来のディスプレイの物理的制約は、ユーザが3次元的な形状を有するデータを扱う場合や触覚的な情報を有するデータと対話する場合に様々な制限を与えている. また, 平面的なディスプレイ上で複雑な立体形状を閲覧・モデリングするためには, 頻繁な視点移動や複雑な頂点操作等を伴うGUI操作が必要である. このような問題を解決するため, 砂, 粘土のような非平面的・柔軟な素材をサーフェスに取り入れて, 従来のディスプレイにできない異なるインタラクションを可能にした研究が行われていたが, 一つのデバイスで異なる物理性質を表現できるディスプレイはあまり研究されていない.本研究は細かなパーティクルと気圧操作による硬さ制御技術に着目し, 硬度可変ディスプレイの実装を行った. 硬さ制御によって, 軟らかいときに形状の変形や, 用途に応じて形状を維持することもできる.このディスプレイの可能性を探るため, 硬さ制御を利用したモデリングアプリケーションを開発した. このアプリケーションでは, モデリング操作に応じて, 適切な硬さを選択する事ができ, モデルが完成した時にディスプレイを硬化し形状を維持させることが可能である.また, 深度カメラを用いることで, タッチ入力による彩色が可能になり, 作成したモデルをスキャンし, CADデータとして保存することもできる. さらに, 3Dプリンターで出力することも可能にした.このシステムは、従来のモデリング操作をより直感的する事ができるが, システム単独で形状を生成することができない. そこで, 本研究では粒子運搬技術を用いて, ディスプレイの形状アクチュエーション手法も提案する. この手法では, モデルの大まかな形状を生成することで, ユーザは形状の細部を自由にカスタマイズすることができる. この手法は, 硬さ制御技術と同じくパーティクルと空気アクチュエーションを用いているため, 低コストかつシンプルなシステムで実現することができる.電気通信大学201

Bio-inspired soft robotic systems: Exploiting environmental interactions using embodied mechanics and sensory coordination

Despite the widespread development of highly intelligent robotic systems exhibiting great precision, reliability, and dexterity, robots remain incapable of performing basic manipulation tasks that humans take for granted. Manipulation in unstructured environments continues to be acknowledged as a significant challenge. Soft robotics, the use of less rigid materials in robots, has been proposed as one means of addressing these limitations. The technique enables more compliant interactions with the environment, allowing for increasingly adaptive behaviours better suited to more human-centric applications. Embodied intelligence is a biologically inspired concept in which intelligence is a function of the entire system, not only the controller or `brain'. This thesis focuses on the use of embodied intelligence for the development of soft robots, with a particular focus on how it can aid both perception and adaptability. Two main hypotheses are raised: first, that the mechanical design and fabrication of soft-rigid hybrid robots can enable increasingly environmentally adaptive behaviours, and second, that sensing materials and morphology can provide intelligence that assists perception through embodiment. A number of approaches and frameworks for the design and development of embodied systems are presented that address these hypotheses. It is shown how embodiment in soft sensor morphology can be used to perform localised processing and thereby distribute the intelligence over the body of a system. Specifically in soft robots, sensor morphology utilises the directional deformations created by interactions with the environment to aid in perception. Building on and formalising these ideas, a number of morphology-based frameworks are proposed for detecting different stimuli. The multifaceted role of materials in soft robots is demonstrated through the development of materials capable of both sensing and changes in material property. Such materials provide additional functionality beyond their integral scaffolding and static mechanical characteristics. In particular, an integrated material has been created exhibiting both sensing capabilities and also variable stiffness and `tack’ force, thereby enabling complex single-point grasping. To maximise the intelligence that can be gained through embodiment, a design approach to soft robots, `soft-rigid hybrid' design is introduced. This approach exploits passive behaviours and body dynamics to provide environmentally adaptive behaviours and sensing. It is leveraged by multi-material 3D printing techniques and novel approaches and frameworks for designing mechanical structures. The findings in this thesis demonstrate that an embodied approach to soft robotics provides capabilities and behaviours that are not currently otherwise achievable. Utilising the concept of `embodiment' results in softer robots with an embodied intelligence that aids perception and adaptive behaviours, and has the potential to bring the physical abilities of robots one step closer to those of animals and humans.EPSR

Robotic Picking of Tangle-prone Materials (with Applications to Agriculture).

The picking of one or more objects from an unsorted pile continues to be non-trivial for robotic systems. This is especially so when the pile consists of individual items that tangle with one another, causing more to be picked out than desired. One of the key features of such tangling-prone materials (e.g., herbs, salads) is the presence of protrusions (e.g., leaves) extending out from the main body of items in the pile.This thesis explores the issue of picking excess mass due to entanglement such as occurs in bins composed of tangling-prone materials (TPs), especially in the context of a one-shot mass-constrained robotic bin-picking task. Specifically, it proposes a human-inspired entanglement reduction method for making the picking of TPs more predictable. The primary approach is to directly counter entanglement through pile interaction with an aim of reducing it to a level where the picked mass is predictable, instead of avoiding entanglement by picking from collision or entanglement-free points or regions. Taking this perspective, several contributions are presented that (i) improve the understanding of the phenomenon of entanglement and (ii) reduce the picking error (PE) by effectively countering entanglement in a TP pile.First, it studies the mechanics of a variety of TPs improving the understanding of the phenomenon of entanglement as observed in TP bins. It reports experiments with a real robot in which picking TPs with different protrusion lengths (PLs) results in up to a 76% increase in picked mass variance, suggesting PL be an informative feature in the design of picking strategies. Moreover, to counter the inherent entanglement in a TP pile, it proposes a new Spread-and-Pick (SnP) approach that significantly reduces entanglement, making picking more consistent. Compared to prior approaches that seek to pick from a tangle-free point in the pile, the proposed method results in a decrease in PE of up to 51% and shows good generalisation to previously unseen TPs