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Research studies on quadrotors have recently drawn significant interest from academia and industry. Faults and failures handling are the major weaknesses of conventional quadrotor platforms; therefore, an innovative actuation mechanism was introduced to allow tilting the rotors. Tilting rotors of multirotor platforms provide high dexterity for flying between adjacent obstacles and assist the platforms in dealing with various failure scenarios. This paper reviews the state of the research on tilt-quadrotor platforms. Several platforms, software and hardware architectures, were discussed in the literature. Most of the latest developments were focused on conventional quadrotor modelling, combined with rotor tilting dynamics. On the other hand, controlling such platform was mainly studied using two types of controllers: Feedback Linearisation technique and Control Allocator. Recovery strategy in case of fault or failure has been covered extensively for conventional quadrotors, but very limited known work for tilt-quadrotor. This review concludes that the system dynamic modelling is relatively well covered compared to exploring new control techniques for more stringent requirements. However, recovery strategies as the main advantage of tilt-quadrotor platforms are not explored extensively and require more research attention. [ABSTRACT FROM AUTHOR]

Byline: Talal M. Al-ja'afreh, Yahya H. Zweiri, Lakmal D. Seneviratne, Kaspar Althoefer Models that predict soft tissue indentation forces have many important applications including estimation of interaction forces, palpation simulation, disease diagnosis and robotic assistance. These models play an essential role in developing mechatronic systems required in many medical applications, such as rehabilitation, clinical palpation and manipulation of organs, since the proper characterising of soft tissue properties mainly depends on the accurate estimation of indentation forces. A new indentation model for estimating circular indenter 'force-displacement' characteristics is presented in this paper. The proposed model is motivated by a 'force-displacement' soil-tool model and is computationally efficient. The main feature of the proposed model is that it can be used to predict the force variations for a variety of tools without the need for retuning the model parameters for each tool. A six-degree of freedom robot manipulator with force and position sensors is used to validate the indentation model. Measured force versus tool displacement data for lamb liver and kidney, for a variety of tool diameters, are presented and compared with the forces predicted by the model, showing very good agreement (root mean square error, RMSE < 8%).

Additive Manufacturing (AM) is a game changing production technology for aerospace applications. Fused deposition modeling is one of the most widely used AM technologies and recently has gained much attention in the advancement of many products. This paper introduces an extensive review of fused deposition modeling and its application in the development of high performance unmanned aerial vehicles. The process methodology, materials, post processing, and properties of its products are discussed in details. Successful examples of using this technology for making functional, lightweight, and high endurance unmanned aerial vehicles are also highlighted. In addition, major opportunities, limitations, and outlook of fused deposition modeling are also explored. The paper shows that the emerge of fused deposition modeling as a robust technique for unmanned aerial vehicles represents a good opportunity to produce compact, strong, lightweight structures, and functional parts with embedded electronic. [ABSTRACT FROM AUTHOR]

Energy use in internal combustion engine passenger vehicles contributes directly to CO2 emissions and fuel consumption, as well as producing a number of air pollutants. Optimizing the vehicle velocity by utilising upcoming road information is an opportunity to minimize vehicle energy use without requiring mechanical design changes. Dynamic programming is capable of such an optimization task and is shown in simulation to produce fuel savings, on average 12%, compared to real driving data; however, in this paper it is also applied in real time on a Raspberry Pi, a low cost miniature computer, in situ in a vehicle. A test drive was undertaken with driver feedback being provided by a dynamic programming algorithm, and the results are compared to a simulated intelligent cruise control system that can follow the algorithm results precisely. An 8% reduction in fuel with no loss in time is reported compared to the test driver. [ABSTRACT FROM AUTHOR]

The main focus of this paper is to develop a physics-based model for a closed-chain manipulator in an excavator vehicle. The derivation of closed-chain manipulator dynamic equations with a structure similar to open-chain manipulator equations is an important research problem, particularly with reference to controller design. In this paper, an approach for deriving closed-chain manipulator equations with an open-chain structure, based on trigonometric t- formulae, is presented. Holonomic loop closure constraints are employed in order to derive the closed-chain mechanism dynamics from the reduced system dynamics. The closed-chain equations, with a structure similar to serial link equations, are presented. The model incorporates the dynamic properties of the manipulator and bucket. The dynamic model for the excavation system is validated against measured data obtained from a full-scale closed-chain excavator vehicle. A dynamic model is important for the design of control strategies for trajectory tracking, a key requirement for automating the excavation task. It is noted that even though the results presented in this paper are focused on a particular excavator vehicle, the research is generic and can be adapted to any closed-chain manipulator. [ABSTRACT FROM AUTHOR]

Hydrogen plays a pivotal role as a sustainable energy carrier, but the cost-effective and efficient production of gas barrier materials for hydrogen storage still poses a considerable challenge. In this research, we investigate the hydrogen barrier properties of nanocomposites based on low-density polyethylene (LDPE) filled with varying concentrations (0–5 wt%) of rippled graphene. The low-density polyethylene rippled graphene (LPRG) nanocomposites were prepared by drop-casting a suspension of graphene with LDPE dissolved in Toluene, which resulted in a uniform dispersion of rippled graphene within the LDPE matrix. Mechanical testing revealed notable improvements in tensile strength (+40%) and Young's modulus (+120%), confirming the reinforcing effect of rippled graphene in the polymer matrix. Results from the thermogravimetric analysis showed that the addition of rippled graphene significantly increased the thermal stability of the material, reporting an increase of 77 °C in the thermal degradation temperature at a nanofiller loading of 1 wt%. Additionally, the incorporation of rippled graphene in the composite improved the thermal conductivity by 161 %. Moreover, it resulted in an electrically percolated network at a critical nanofiller loading of 1 wt%, contributing to enhanced electrostatic discharge safety. Finally, gas permeability tests unveiled a 50% reduction in hydrogen permeability of LPRG with graphene content of 5 wt% compared to pristine LDPE, making graphene/LDPE composites a superior choice for sustainable and efficient hydrogen storage applications. • H 2 barrier properties of LDPE composites filled with (0–5 wt%) rippled graphene were investigated. • Mechanical testing revealed notable improvements in tensile strength (+40%) and Young's modulus (+120%). • Adding rippled graphene boosted material's thermal stability, raising degradation temp by 77 °C at 1% nanofiller loading. • Rippled graphene enhanced thermal conductivity by 161% and formed an electrically percolated network at 1 wt%. • The 5 wt% concentration of rippled graphene reduced H 2 permeability by 50% compared to pristine LDPE. [ABSTRACT FROM AUTHOR]

Trained deep reinforcement learning (DRL) based controllers can effectively control dynamic systems where classical controllers can be ineffective and difficult to tune. However, the lack of closed‐loop stability guarantees of systems controlled by trained DRL agents hinders their adoption in practical applications. This research study investigates the closed‐loop stability of dynamic systems controlled by trained DRL agents using Lyapunov analysis based on a linear‐quadratic polynomial approximation of the trained agent. In addition, this work develops an understanding of the system's stability margin to determine operational boundaries and critical thresholds of the system's physical parameters for effective operation. The proposed analysis is verified on a DRL‐controlled system for several simulated and experimental scenarios. The DRL agent is trained using a detailed dynamic model of a non‐linear system and then tested on the corresponding real‐world hardware platform without any fine‐tuning. Experiments are conducted on a wide range of system states and physical parameters and the results have confirmed the validity of the proposed stability analysis (https://youtu.be/QlpeD5sTlPU). [ABSTRACT FROM AUTHOR]

This work describes the development of a vision-based tactile sensor system that utilizes the image-based information of the tactile sensor in conjunction with input loads at various motions to train the neural network for the estimation of tactile contact position, area, and force distribution. The current study also addresses pragmatic aspects, such as choice of the thickness and materials for the tactile fingertips and surface tendency, etc. The overall vision-based tactile sensor equipment interacts with an actuating motion controller, force gauge, and control PC (personal computer) with a LabVIEW software on it. The image acquisition was carried out using a compact stereo camera setup mounted inside the elastic body to observe and measure the amount of deformation by the motion and input load. The vision-based tactile sensor test bench was employed to collect the output contact position, angle, and force distribution caused by various randomly considered input loads for motion in X, Y, Z directions and R x R y rotational motion. The retrieved image information, contact position, area, and force distribution from different input loads with specified 3D position and angle are utilized for deep learning. A convolutional neural network VGG-16 classification modelhas been modified to a regression network model and transfer learning was applied to suit the regression task of estimating contact position and force distribution. Several experiments were carried out using thick and thin sized tactile sensors with various shapes, such as circle, square, hexagon, for better validation of the predicted contact position, contact area, and force distribution. [ABSTRACT FROM AUTHOR]

Cobots play an essential role in the fourth industrial revolution and the automation of complex manufacturing processes. However, cobots still face challenges in achieving high precision, which obstructs their usage in precise applications such as the aerospace industry. Nonetheless, advances in perception systems unlock new cobot manufacturing capabilities. This paper presents a novel multi-functional sensor that combines visual and tactile feedback using a single optical sensor, featuring a moving gate mechanism. This work also marks the first integration of Vision-Based Tactile Sensing (VBTS) into a robotic machining end-effector. The sensor provides vision-based tactile perception capabilities for precise normality control and exteroceptive perception for robot localization and positioning. Its performance is experimentally demonstrated in a precise robotic deburring application, where the sensor achieves the high-precision requirements of the aerospace industry with a mean normality error of 0.13° and a mean positioning error of 0.2 mm. These results open a new paradigm for using vision-based sensing for precise robotic manufacturing, which surpasses conventional approaches in terms of precision, weight, size, and cost-effectiveness. • We develop a novel multi-functional sensor for robotic manufacturing that combines proprioceptive and exteroceptive sensing using only a single camera. This proposed design offers improvements in cost, weight, and design simplicity over prior work in the literature, pioneering the application of tactile sensing in robotic manufacturing tasks. • For the first time, we propose vision-based tactile sensing for normality adjustment in precise robotic machining. We develop the corresponding perception and control algorithms by training a CNN architecture. This approach outperforms State-of-The-Art, achieving a mean absolute error of 0.13° and significantly enhancing robotic machining precision. • We formulate and validate the visual guidance and control laws that utilize the camera's exteroceptive feedback to precisely position the robotic tool relative to a target workpiece with 0.2 mm accuracy. • We perform experimental validation and testing of the proposed sensor in a robotic deburring setup and present a detailed qualitative and quantitative analysis of the obtained results. Results show that the proposed sensor and perception algorithms provide the positioning and normality requirements of automated manufacturing in the aerospace industry. [ABSTRACT FROM AUTHOR]

Standard quadcopters are popular largely because of their mechanical simplicity relative to other hovering aircraft, low cost and minimum operator involvement. However, this simplicity imposes fundamental limits on the types of maneuvers possible due to its under-actuation. The dexterity and fault tolerance required for flying in limited spaces like forests and industrial infrastructures dictate the use of a bespoke dual-tilting quadcopter that can launch vertically, performs autonomous flight between adjacent obstacles and is even capable of flying in the event of the failure of one or two motors. This paper proposes an actuation concept to enhance the performance characteristics of the conventional under-actuated quadcopter. The practical formation of this concept is followed by the design, modeling, simulation and prototyping of a dual-axis tilting quadcopter. Outdoor flight tests using tilting rotors, to follow a trajectory containing adjacent obstacles, were conducted in order to compare the flight of conventional quadcopter with the proposed over-actuated vehicle. The results show that the quadcopter with tilting rotors provides more agility and mobility to the vehicle especially in narrow indoor and outdoor infrastructures. [ABSTRACT FROM AUTHOR]

Vision-based tactile sensors (VBTSs) have become the de facto method for giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTSs offer high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead.In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. In particular, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTSs use an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios (with and without an internal illumination source) thus further reducing instrumentation costs. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0. 62 ∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the computing time needed by VBTS when both are tested on the same scenario. [ABSTRACT FROM AUTHOR]

Vision-Based Tactile Sensors (VBTS) play a key role in enhancing the accuracy and efficiency of machining operations in robotic-assisted precision machining systems. Equipped with VBTS, these systems offer contact-based measurements, which are essential in machining accurate components for industries such as aerospace, automotive, medical devices, and electronics. This paper presents a novel approach to virtual prototyping of VBTS, specifically in perpendicularity measurements using Computer-Aided Design (CAD) generation of VBTS designs, Finite Element Analysis (FEA) simulations, and Sim2Real deep learning to achieve VBTS with high precision measurements. The virtual prototyping approach enables an understanding of the contact between VBTS with different designs and machined surfaces in terms of contact module shape, thickness, markers' density. Additive manufacturing was employed to fabricate the molds of VBTS contact module, followed by experimental validation of the robotic arm to confirm the effectiveness of the optimized VBTS design. The results show that deviation from the hemispherical shape reduces the data quality captured by the camera, hence increasing the prediction errors. Additionally, reducing the thickness of the contact module enhances the precision of perpendicularity measurements. Importantly increasing markers' distribution density significantly enhances the accuracy of up to 92 markers at which above it the rate of improvement becomes less pronounced. An VBTS with height of 20 mm, thickness of 2 mm, and 169 markers was found to be within the stringent perpendicularity standards of the aerospace manufacturing industry of 0. 5 8 ∘ as a root mean square error, and 1. 64 ° as a max absolute error around the roll and pitch axis of rotation. The established virtual prototyping methodology can be transferred to a wide variety of elastomer-based sensors. • Establishment of parametric virtual prototyping for vision-based tactile sensors for perpendicularity measurements. • Methods, namely: CAD generation, FEA simulation, Sim2Real gap bridging, and practical implementation. • Constructed the relationship between sensor design and accuracy, achieving optimization. • Practical robotic implementation and validation of the optimized sensor show only 0.58 degrees error. [Display omitted] [ABSTRACT FROM AUTHOR]