Low Profile Stretch Sensor for Soft Wearable Robotics

This paper presents a low profile stretch sensor for integration into soft structures, robots and wearables. The sensor mechanism uses a single piece of highly flexible and light weight optical fibre and is based on the notion that bending an optical fibre modulates the intensity of the light transmitted through the fibre, a technique often referred as macrobending light loss. In this arrangement, the optical fibre originates from sensor’s electronic unit, passes through a stretchable encasing structure in a macrobend pattern, and then loop back to the same unit resulting in a simplified electrical and optical design; the closed optical loop allows for no electronics at one end of the sensor making it safe for human robotics applications, and no optical interference with the external environment eliminating the need for complex conditioning circuitries. Of particular interest of the soft robotics community, the ability of this custom macrobend stretch sensor to flexibly adapt its configuration allows preserving the inherent softness and compliance of the robot which it is installed on. Our experimental results indicate that the optical fibre’s bending radius is the dominant design parameter for sufficiently complex patterns, a finding that can facilitate generalisation of the sensing methods across different scales. The measurement performance of the mechanism and its impact on the stiffness of the encasing structure is benchmarked against a custom calibration and testing system

Kirigami artificial muscles with complex biologically inspired morphologies

In this paper we present bio-inspired smart structures which exploit the actuation of flexible ionic polymer composites and the kirigami design principle. Kirigami design is used to convert planar actuators into active 3D structures capable of large out-of-plane displacement and that replicate biological mechanisms. Here we present the burstbot, a fluid control and propulsion mechanism based on the atrioventricular cuspid valve, and the vortibot, a spiral actuator based on Vorticella campanula, a ciliate protozoa. Models derived from biological counterparts are used as a platform for design optimisation and actuator performance measurement. The symmetric and asymmetric fluid interactions of the burstbot are investigated and the effectiveness in fluid transport applications is demonstrated. The vortibot actuator is geometrically optimised as a camera positioner capable of 360 degree scanning. Experimental results for a one-turn spiral actuator show complex actuation derived from a single degree of freedom control signal

Multifunctional arm for telerobotic wind turbine blade repair

Within the Multi-Platform Inspection, Maintenance and Repair in Extreme Environments (MIMRee) project, a lightweight and multifunctional robotic repair arm is created for wind turbine blades. The design features a toolbox at the base of the arm housing multiple end-effector tools and an autonomous end-effector tool-changer. The arm communicates commands and data via internet with a bespoke user interface enabling human-in-the-loop operation and overriding of autonomous repair actions. This paper outlines our approach in design, development, testing and control of the robotic repair system. The functionalities of the arm include cleaning, sanding, and filler material deposition and forming, each using a bespoke end-effector tool closely replicating the relevant manual repair process. The experimental results confirm the effectiveness of our approach indicating a maximum end-effector position error of 3 mm, a maximum tool switching time of 8 seconds, and a maximum arm’s weight of 1.8 kg. This presents around 84% weight reduction compared with existing technologies used for the same purpose. Our standalone design enables modular integration into a wide range of mobile platform types used in industrial operations

Autonomous decision making in a bioinspired adaptive robotic anchoring module

This paper proposes a bioinspired adaptive anchoring module that can be integrated into robots to enhance their mobility and manipulation abilities. The design of the module is inspired by the structure of the mouth in Chilean lamprey (Mordacia lapicida) where a combination of suction and several arrays of teeth with different sizes around the mouth opening is used for catching preys and anchoring onto them. The module can deploy a suitable mode of attachment, via teeth or vacuum suction, to different contact surfaces in response to the textural properties of those surfaces. In order to make a decision on the suitable mode of attachment, an original dataset of 500 images of outdoor and indoor surfaces was used to train a visual surface examination model using YOLOv3; a virtually real-time object detection algorithm. The mean average precision of the trained model was calculated to be 91%. We have conducted a series of pull-out tests to characterize the module’s strength of attachments. The results of the experiments indicate that the anchoring module can withstand an applied detachment force of up to 70N and 30N when attached using teeth and vacuum suction, respectively

A Non-linear Model for Predicting Tip Position of a Pliable Robot Arm Segment Using Bending Sensor Data

Using pliable materials for the construction of robot bodies presents new and interesting challenges for the robotics community. Within the EU project entitled STIFFness controllable Flexible & Learnable manipulator for surgical Operations (STIFF-FLOP), a bendable, segmented robot arm has been developed. The exterior of the arm is composed of a soft material (silicone), encasing an internal structure that contains air-chamber actuators and a variety of sensors for monitoring applied force, position and shape of the arm as it bends. Due to the physical characteristics of the arm, a proper model of robot kinematics and dynamics is difficult to infer from the sensor data. Here we propose a non-linear approach to predicting the robot arm posture, by training a feed-forward neural network with a structured series of pressures values applied to the arm's actuators. The model is developed across a set of seven different experiments. Because the STIFF-FLOP arm is intended for use in surgical procedures, traditional methods for position estimation (based on visual information or electromagnetic tracking) will not be possible to implement. Thus the ability to estimate pose based on data from a custom fiber-optic bending sensor and accompanying model is a valuable contribution. Results are presented which demonstrate the utility of our non-linear modelling approach across a range of data collection procedures

Additive manufacture of polymeric organometallic ferroelectric diodes (POMFeDs) for structural neuromorphic hardware

Hardware design and implementation for online machine learning applications is complicated by a number of facets of conventional artificial neural networks (ANN), e.g. deep neural networks (DNNs), such as reliance on atemporal locality, offline learning using large datasets, potential difficulties in transfer from model to substrates, and issues with processing of noisy sensory data using energy-efficient and asynchronous information processing modalities. Analog or mixed-signal spiking neural networks (SNNs) have promise for lower power, temporally localised, and stimuli selective sensing and inference but are difficult fabricate at low cost. Investigation of beyond-CMOS alternative organic substrates may be worthwhile for development of unconventional neuromorphic hardware with pseudo-spiking dynamics for structural electronics integration in bio-signal processing and robotics. Here, polymeric organometallic ferroelectric diodes (POMFeDs) are introduced for development of printable ferroelectric in-sensor SNNs

Faster R-CNN-based Decision Making in a Novel Adaptive Dual-Mode Robotic Anchoring System

This paper proposes a novel adaptive anchoring module that can be integrated into robots to enhance their mobility and manipulation abilities. The module can deploy a suitable mode of attachment, via spines or vacuum suction, to different contact surfaces in response to the textural properties of the surfaces. In order to make a decision on the suitable mode of attachment, an original dataset of 100 images of outdoor and indoor surfaces was enhanced using a WGAN-GP generating an additional 200 synthetic images. The enhanced dataset was then used to train a visual surface examination model using Faster R-CNN. The addition of synthetic images increased the mean average precision of the Faster R-CNN model from 81.6% to 93.9%. We have also conducted a series of load tests to characterize the module’s strength of attachments. The results of the experiments indicate that the anchoring module can withstand an applied detachment force of around 22N and 20N when attached using spines and vacuum suction on the ideal surfaces, respectively

ROSIC: Enhancing secure and accessible robot control through open-source instant messaging platforms

Ensuring secure communication and seamless accessibility remains a primary challenge in controlling robots remotely. The authors propose a novel approach that leverages open-source instant messaging platforms to overcome the complexities and reduce costs associated with implementing a secure and user-centred communication system for remote robot control named Robot Control System using Instant Communication (ROSIC). By leveraging features, such as real-time messaging, group chats, end-to-end encryption and cross-platform support inherent in the majority of instant messenger platforms, we have developed middleware that establishes a secure and efficient communication system over the Internet. By using instant messaging as the communication interface between users and robots, ROSIC caters to non-technical users, making it easier for them to control robots. The architecture of ROSIC enables various scenarios for robot control, including one user controlling multiple robots, multiple users controlling one robot, multiple robots controlled by multiple users, and one user controlling one robot. Furthermore, ROSIC facilitates the interaction of multiple robots, enabling them to interoperate and function collaboratively as a swarm system by providing a unified communication platform that allows for seamless exchange of data and commands. Telegram was specifically chosen as the instant messaging platform by the authors due to its open-source nature, robust encryption, compatibility across multiple platforms and interactive communication capabilities through channels and groups. Notably, the ROSIC is designed to communicate effectively with robot operating system (ROS)-based robots to enhance our ability to control them remotely

Treatment of petroleum refinery effluents by a hybrid system of activated sludge and rotation biological reactor, followed by the sand filter

In this study, a novel hybrid treatment system was designed to increase the removal efficiency of petroleum refinery wastewater. The hybrid system is a pilot scale including an activated sludge combined with a rotating biological contractor (RBC) and sand filter. Four vertical rotating polyurethane disks in the aeration tank combined activated sludge-rotating biological contactor pilot. The influent wastewater for this system was the effluent from the DAF unit in the Shahid Tondgooyan Oil Refining Co's wastewater treatment plant. The rotation rate of disks and retention time has been evaluated for their impact on the removal efficiency of total dissolved solids (TDS), dissolved oxygen (DO), pH, total suspended solids (TSS), turbidity (TU), chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia (NH3). According to the results, enhancing the rotational speed of disks (from 4 rpm to 8 rpm) and increasing the retention time (from 6 hours to 10 hours) can improve COD, NH3, TSS, BOD5, TU, TDS, and oil removal efficiency of this system to 100, 98.52, 84.21, 100, 99.25, 13.32 and 100% respectively. Escalating the rotational speed beyond 8 rpm had reverse effects on the performance of this hybrid system. The rotational speed of 8 rpm and a retention time of 10 hours were the optimum conditions for removing the abovementioned parameters. It is worth mentioning that the high TU removal efficiency of the system was due to the presence of a sand filter. This system performed well in removing pollutants compared to other biological wastewater treatment systems

Aerial additive building manufacturing: Three-dimensional printing of polymer structures using drones

This paper describes the first aerial additive building manufacturing system developed to create and repair civil engineering structures remotely using polymers extruded from unmanned aerial robots (drones). The structural potential of three commercially available expanding polyurethane foams of varying density (LD40, Reprocell 300 and Reprocell 500), and their feasibility for deposition using an autonomous flying dual-syringe device is described. Test specimens consisting of one and two layers, with horizontal and vertical interfaces, were mechanically tested both parallel and perpendicular to the direction of expansion. LD40 specimens exhibited ductile failure in flexural tests and provided evidence that the interfaces between layers were not necessarily regions of weaknesses. Hand-mixed specimens of Reprocell 500 possessed compressive strengths comparable to those of concrete and flexural strengths similar to those of the lower range of timber, though they exhibited brittle failure. There are challenges to be faced with matching the performance of hand-mixed specimens using an autonomous dual-syringe deposition device, primarily concerning the rheological properties of the material following extrusion. However, the device successfully imported and deposited two liquid components, of varying viscosity, and maintained correct mixing ratios. This work has demonstrated the structural and operational feasibility of polyurethane foam as a viable structural material for remote three-dimensional printing using drones