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Herein, the prestretching indexes of dielectric elastomers (DE) were determined by measuring the tensile properties, and then the actuation displacement and output force of the conical dielectric elastomer actuator (CDEA) with different prestretching index and different preloading were discussed in detail, finally, the three indexes of thickness, generatrix and circumferential stretch are proposed to calculate the strain energy density of the CDEA. The experimental results show that the large prestretching ratio will cause the obvious instability of DE. Two materials showed obvious thin neck phenomenon and finally fracture. The stress–strain curve of DE shows hysteresis phenomenon under cyclic loading, and with the increase of cycle times, the unit stress and the unit hysteresis loss rate both decrease, and the change range is larger. When the prestretching of outer domain is the same, the displacement and force of CDEA increase with the increase of voltage, and the displacement curve appears parallel distribution under different voltage. The Gent model can better reflect the nonlinear deformation behavior of CDEA. This study provides valuable guidance for the design of high performance DEA and the application of dynamic DEA.

Soft actuator is the main technology that drives the development of soft robotics as it defines the capabilities of soft machines. One of the key challenges in soft actuator research is to simultaneously produce complex morphing (e.g., surface morphing) and high force. Therefore, a soft robotic pad (SRP) was developed as a new soft actuator that can potentially tackle this challenge. This article demonstrates the highly flexible customization process of the SRP regarding fabrication, morphing design, and force scaling for a challenging application: a giant manta ray robot with the Mobuliform swimming mode. Soft robotic pectoral fins (SRPFs) are customized using the SRP technology for the robot. Detailed characterization results show that the SRPFs can generate almost 100 N force or 25 Nm torque with a dual curving motion to mimic the manta ray fin kinematics. The SRPFs are able to propel the manta ray robot to a decent swimming speed with biomimetic fin motions. As a new member of the soft actuator family, SRPs exhibit strong capabilities through this manta ray robot, and they can potentially become unique tools for many engineers and researchers to tackle challenging applications requiring high force and surface morphing.

In order to satisfy the requirement of the automatic ultrasonic strengthening of an aviation blade surface, this paper puts forward a robotic compliance control strategy of contact force for ultrasonic surface strengthening. By building the force/position control method for robotic ultrasonic surface strengthening., the compliant output of the contact force is achieved by using the robot’s end-effector (compliant force control device). Based on the control model of the end-effector obtained from experimental determination, a fuzzy neural network PID control is used to optimize the compliance control system, which improves the adjustment accuracy and tracking performance of the system. An experimental platform is built to verify the effectiveness and feasibility of the compliance control strategy for the robotic ultrasonic strengthening of an aviation blade surface. The results demonstrate that the proposed method maintains the compliant contact between the ultrasonic strengthening tool and the blade surface under multi-impact and vibration conditions.

The handshake is the most acceptable gesture of greeting in many cultures throughout many centuries. To date, robotic arms are not capable of fully replicating this typical human gesture. Using multiple sensors that detect contact forces and displacements, we characterized the movements that occured during handshakes. A typical human-to-human handshake took around 3.63 s (SD = 0.45 s) to perform. It can be divided into three phases: reaching (M = 0.92 s, SD = 0.45 s), contact (M = 1.96 s, SD = 0.46 s), and return (M = 0.75 s, SD = 0.12 s). The handshake was further investigated to understand its subtle movements. Using a multiphase jerk minimization model, a smooth human-to-human handshake can be modelled with fifth or fourth degree polynomials at the reaching and return phases, and a sinusoidal function with exponential decay at the contact phase. We show that the contact phase (1.96 s) can be further divided according to the following subphases: preshake (0.06 s), main shake (1.31 s), postshake (0.06 s), and a period of no movement (0.52 s) just before both hands are retracted. We compared these to the existing handshake models that were proposed for physical human-robot interaction (pHRI). From our findings in human-to-human handshakes, we proposed guidelines for a more natural handshake movement between humanoid robots and their human partners.

This paper focuses on the problem of extracting the physical dynamic parameters which are fundamental for computing the positive-definite link mass matrix. To solve this problem, a minimal set of dynamic parameters were firstly identified by the standard least squares method. In order to simplify the dynamics model, a new set of essential dynamic parameters were calculated by eliminating the poorly identified parameters with an iterative approach. Based on these dynamic parameters with better identification quality, a universally global optimization framework was proposed here to retrieve the set of physical dynamic parameters of a serial robot, in which parameter bounds, linear and nonlinear constraints with physical consistency can be easily considered, such as the triangle inequality of the link inertia tensors, the total link mass limitations, other user-defined constraints and so on. Finally, validation experiments were conducted on the KUKA LBR iiwa 14 R820 robot. The results show that the proposed optimization framework is effective, and the identified dynamic parameters can predict the robot joint torques accurately for the validation trajectories.

Simultaneous localization and mapping(SLAM), focusing on addressing the joint estimation problem of self-localization and scene mapping, has been widely used in many applications such as mobile robot, drone, and augmented reality(AR). However, traditional state-of-the-art SLAM approaches are typically designed under the static-world assumption and prone to be degraded by moving objects when running in dynamic scenes. This article presents a novel semantic visual-inertial SLAM system for dynamic environments that, building on VINS-Mono, performs real-time trajectory estimation by utilizing the pixel-wise results of semantic segmentation. We integrate the feature tracking and extraction framework into the front-end of the SLAM system, which could make full use of the time waiting for the completion of the semantic segmentation module, to effectively track the feature points on subsequent images from the camera. In this way, the system can track feature points stably even in high-speed movement. We also construct the dynamic feature detection module that combines the pixel-wise semantic segmentation results and the multi-view geometric constraints to exclude dynamic feature points. We evaluate our system in public datasets, including dynamic indoor scenes and outdoor scenes. Several experiments demonstrate that our system could achieve higher localization accuracy and robustness than state-of-the-art SLAM systems in challenging environments.

In this study, the dielectric constants of VHB4905 and VHB4910 are measured systematically, and the capacitance, edge capacitance, dielectric loss and dielectric loss factor are analyzed. The dielectric properties are revealed from the microstructure, and the Cole-Cole model is used to characterize the dielectric properties. The results show that the dielectric constant is very sensitive to the frequency dependence, but it is sensitive to the real and imaginary parts from 102 Hz to 106 Hz. The relative errors of their permittivity are small, when the edge effect is not taken into account. At high frequency, the orientation movement of the polar group on the side chain of VHB acrylic leads to the decrease of the molecular polarizability, and the energy consumption of electric dipole, shift polarization and turn polarization leads to the decrease of the dielectric constant and the increase of the dielectric loss. The fitting correlation coefficients of dielectric constant for VHB4905 and VHB4910 are high, which describe the polarization relaxation behavior of VHB acrylic acid. The research in this paper is helpful to understand the electrical characteristics of acrylic acid, and provides a basis for the design and control of dielectric elastomer actuator.

This paper introduces the development of an anthropomorphic soft robotic hand integrated with multiple flexible force sensors in the fingers. By leveraging on the integrated force sensing mechanism, grip state estimation networks have been developed. The robotic hand was tasked to hold the given object on the table for 1.5 s and lift it up within 1 s. The object manipulation experiment of grasping and lifting the given objects were conducted with various pneumatic pressure (50, 80, and 120 kPa). Learning networks were developed to estimate occurrence of object instability and slippage due to acceleration of the robot or insufficient grasp strength. Hence the grip state estimation network can potentially feedback object stability status to the pneumatic control system. This would allow the pneumatic system to use suitable pneumatic pressure to efficiently handle different objects, i.e., lower pneumatic pressure (50 kPa) for lightweight objects which do not require high grasping strength. The learning process of the soft hand is made challenging by curating a diverse selection of daily objects, some of which displays dynamic change in shape upon grasping. To address the cost of collecting extensive training datasets, we adopted one-shot learning (OSL) technique with a long short-term memory (LSTM) recurrent neural network. OSL aims to allow the networks to learn based on limited training data. It also promotes the scalability of the network to accommodate more grasping objects in the future. Three types of LSTM-based networks have been developed and their performance has been evaluated in this study. Among the three LSTM networks, triplet network achieved overall stability estimation accuracy at 89.96%, followed by LSTM network with 88.00% and Siamese LSTM network with 85.16%.

We tackle the problem of achieving real-world autonomous driving for buses, where the task is to perceive nearby obstacles and predict their motion in the time ahead, given current and past information of the object. In this paper, we present the development of a modular pipeline for the long-term prediction of dynamic obstacles’ trajectories for an autonomous bus. The pipeline consists of three main tasks, which are the obstacle detection task, tracking task, and trajectory prediction task. Unlike most of the existing literature that performs experiments in the laboratory, our pipeline’s modules are dependent on the introductory modules in the pipeline—it uses the output of previous modules. This best emulates real-world autonomous driving and reflects the errors that may accumulate and cascade from previous modules with less than 100% accuracy. For the trajectory prediction task, we propose a training method to improve the module’s performance and attain a run-time of 10 Hz. We present the practical problems that arise from realising ready-to-deploy autonomous buses and propose methods to overcome these problems for each task. Our Singapore autonomous bus (SGAB) dataset evaluated the pipeline’s performance. The dataset is publicly available online.

One of the fundamental fields of research is motion planning. Mobile manipulators present a unique set of challenges for the planning algorithms, as they are usually kinematically redundant and dynamically complex owing to the different dynamic behavior of the mobile base and the manipulator. The purpose of this article is to systematically review the different planning algorithms specifically used for mobile manipulator motion planning. Depending on how the two subsystems are treated during planning, sampling-based, optimization-based, search-based, and other planning algorithms are grouped into two broad categories. Then, planning algorithms are dissected and discussed based on common components. The problem of dealing with the kinematic redundancy in calculating the goal configuration is also analyzed. While planning separately for the mobile base and the manipulator provides convenience, the results are sub-optimal. Coordinating between the mobile base and manipulator while utilizing their unique capabilities provides better solution paths. Based on the analysis, challenges faced by the current planning algorithms and future research directions are presented.

Object tracking is an essential aspect of environmental perception technology for autonomous vehicles. The existing object tracking algorithms can only be applied well to simple scenes. When the scenes become complex, the algorithms have poor tracking performance and insufficient robustness, and the problems of tracking drift and object loss are prone to occur. Therefore, a robust object tracking algorithm for autonomous vehicles in complex scenes is proposed. Firstly, we study the Siam-FC network and related algorithms, and analyze the problems that need to be addressed in object tracking. Secondly, the construction of a double-template Siamese network model based on multi-feature fusion is described, as is the use of the improved MobileNet V2 as the feature extraction backbone network, and the attention mechanism and template online update mechanism are introduced. Finally, relevant experiments were carried out based on public datasets and actual driving videos, with the aim of fully testing the tracking performance of the proposed algorithm on different objects in a variety of complex scenes. The results showed that, compared with other algorithms, the proposed algorithm had high tracking accuracy and speed, demonstrated stronger robustness and anti-interference abilities, and could still accurately track the object in real time without the introduction of complex structures. This algorithm can be effectively applied in intelligent vehicle driving assistance, and it will help to promote the further development and improvement of computer vision technology in the field of environmental perception.

Unmanned Aerial Vehicles (UAVs) used for humanitarian applications require simple, accessible and reliable components. For example, a communication system between UAV and the Ground Control Station (GCS) is essential in order to monitor UAV status; various communication protocols are available in the industry. Such systems must be simple for non-technical personnel (e.g., healthcare workers) to operate. In this study, a novel link failure detection and switching algorithm was proposed for a dissimilar redundant UAV communication system designed for long-range vaccine delivery in rural areas. The algorithm would ease the workload of the operators and address a research gap in the design of such algorithms. A two-layer design is proposed: A baseline layer using the heartbeat method, and optimisations to speed up local failure detection. To dynamically tune the heartbeat timeout for the algorithm’s baseline without intervention from ground operators, the modified Jacobson’s algorithm was used. Lab simulations found that the algorithm was generally accurate in converging to an optimal value, but has less satisfactory performance at poor or unpredictable connectivity, or when link switches get triggered frequently. Improvements have been suggested for the algorithm. This study contributes to ongoing research on ensuring reliable UAV communication for humanitarian purposes.

Tactile sensors are widely used by the robotics industries over decades to measure force or pressure produced by external stimuli. Piezoelectric-based pressure sensors have intensively been investigated as promising candidates for tactile sensing applications. In contrast, piezoelectric-based pressure sensors are expensive due to their high cost of manufacturing and expensive base materials. Recently, an effect similar to the piezoelectric effect has been identified in non-piezoelectric polymers such as poly(d,l-lactic acid (PDLLA), poly(methyl methacrylate) (PMMA) and polystyrene. Hence investigations were conducted on alternative materials to find their suitability. In this article, we used inexpensive atactic polystyrene (aPS) as the base polymer and fabricated functional fibers using an electrospinning method. Fiber morphologies were studied using a field-emission scanning electron microscope and proposed a unique pressure sensor fabrication method. A fabricated pressure sensor was subjected to different pressures and corresponding electrical and mechanical characteristics were analyzed. An open circuit voltage of 3.1 V was generated at 19.9 kPa applied pressure, followed by an integral output charge (ΔQ), which was measured to calculate the average apparent piezoelectric constant dapp and was found to be 12.9 ± 1.8 pC N−1. A fabricated pressure sensor was attached to a commercially available robotic arm to mimic the tactile sensing.

This paper presents fabric-based soft robotic modules with primitive morphologies, which are analogous to basic geometrical polygons—trilateral and quadrilateral. The two modules are the inflatable beam (IB) and fabric-based rotary actuator (FRA). The FRA module is designed with origami-inspired V-shaped pleats, which creates a trilateral outline. Upon pressurization, the pleats unfold, which enables propagation of angular displacement of the FRA module. This allows the FRA module to be implemented as a mobility unit in the larger assembly of pneumatic structures. In the following, we examine various ways by which FRA modules can be connected to IB modules. We studied how different ranges of motion can be achieved by varying the design of the rotary joint of the assemblies. Using a state transition-based position control system, movement of the assembled modules could be controlled by regulating the pneumatic pressurization of the FRA module at the joint. These basic modules allow us to build different types of pneumatic structures. In this paper, using IB and FRA modules of various dimensions, we constructed a soft robotic limb with an end effector, which can be attached to wheelchairs to provide assistive grasping functions for users with disabilities.

This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody’s (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb’s three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy.

Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.