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In this paper, a Bayesian approach is proposed for the early detection of a drone threatening or anomalous behaviour in a surveyed region. This is in relation to revealing, as early as possible, the drone intent to either leave a geographical area where it is authorised to fly (e.g. to conduct inspection work) or reach a prohibited zone (e.g. runway protection zones at airports or a critical infrastructure site). The inference here is based on the noisy sensory observations of the target state from a non-cooperative surveillance system such as a radar. Data from Aveillant's Gamekeeper radar from a live drone trial is used to illustrate the efficacy of the introduced approach.

Jumping with controllable elevation is significant for insect-scale robots to improve terrain adaptability and to escape from risks. However, jumping robots based on soft materials with low stiffness cannot transmit displacement precisely, exhibiting poor control of jumping. Here, we propose a modified two-bars catapult mechanism combined with an asynchronous sequential releasing strategy to realize elevation controllable jumping. In this work, an 80 mg prototype robot, 56 (long) × 29 mm (wide) × 3 mm (high) mm in size, is designed with the controllable elevation range from 63° to 112°. The soft robot is mainly composed of a shape memory alloy actuator and four electrostatic pads acting as the lock/release structures. Elevation control is realized by asynchronously releasing the electrostatic pads in a small time interval (about 10 ms). A maximum jump height of 62 mm and a maximum half-distance of 41 mm are also achieved.

The motion of an object (e.g. ship, jet, pedestrian, bird, drone, etc.) is usually governed by premeditated actions as per an underlying intent, for instance reaching a destination. In this paper, we introduce a novel intent-driven dynamical model based on a continuous-time intrinsic coordinate model. By combining this model with particle filtering, a seamless approach for jointly predicting the destination and estimating the state of a highly manoeuvrable object is developed. We examine the proposed inference technique using real data with different measurement models to demonstrate its efficacy. In particular, we show that the introduced approach can be a flexible and competitive alternative, in terms of prediction and estimation performance, to other existing methods for various measurement models including nonlinear ones.

In group object tracking, the identification of the group leader can be highly beneficial for predicting the intention and future manoeuvres of objects as well as learning the underlying group behaviour traits. This paper presents an online approach for inferring dominant entities in tracked groups from observations. Unlike traditional leader-follower models, here we develop a new rotated leadership model that can capture the dynamic evolution of the interaction patterns in groups over time. Two methods, an online Gibbs sampler and deterministic particle filter, are then designed to infer sequentially the leader in group object tracking scenarios. Synthetic and real pigeon flocking data are used to demonstrate the effectiveness of the proposed techniques in terms of identifying the group leader under complex dynamics.

In this paper we present Bayesian methods for tracking scenarios in which an intrinsic coordinate model is considered and inertial mea- surements plus occasional position fixes are available. The methods are first tested using synthetic data, giving a comprehensive evalu- ation as to their performance. Further evaluation on real data also reveals our approaches can be favourable alternatives to existing in- ertial tracking/navigation models.

Respiration is an important physiological function in which air is moved into and out of the lungs. This paper designed a flexible patch for continuous detections of respiration, especially for evaluating nasal symmetry. The monitoring system comprises an integrated hot-film and two time-of-flight flow sensors on a flexible polyimide (PI) substrate. The 5 μm-thick polyimide substrate is flexible to construct wearable devices while the hot-wire flow sensor can monitor the respiration conditions and states, such as different breathing patterns of mouth or nose modes, normal or deep forms, and happy or nervous signs. Moreover, the power consumption of the system is low for possible long-term monitoring applications of sleep and medical diagnosis. The two time-of-flight sensors are aligned symmetrically on different sides of the hot-film heating element for differential signal readings, which used asymmetrical thermal distribution to reveal breathing state differences of two nostrils and they don’t require additional energy since they are located near the heating element. The system serves as a noninvasive wearable device to monitor respiratory airflow while avoiding uncomfortable nasal cannula. Various testing results show the feasibility and effectiveness of the proposed system.

The motion of a tracked object often has long term underlying dependencies due to premeditated actions dictated by intent, such as destination. Revealing this intent, as early as possible, can enable advanced intelligent system functionalities for conflict/opportunity detection and automated decision making, for instance in surveillance and human computer interaction. This paper presents a novel Bayesian intent inference framework that utilises sequential Monte Carlo (SMC) methods to determine the destination of a tracked object exhibiting unknown jump behaviour. The latter can arise from the object undertaking fast maneuvers (e.g. for obstacle avoidance) and/or due to external uncontrollable environmental perturbations. Suitable intent-driven stochastic models and inference routines are introduced. The effectiveness of the proposed approach is demonstrated using synthetic and real data.

This paper shows vacuum powered pneumatic actuators designed for wearable robots using the Kirigami of polymeric thin films for the first time. Distinctive advancements have been achieved, including (1) lightweight and the flexible thin sheet of polymeric materials made by Kirigami designs to allow either bending or twisting movements; (2) capable of shape-transforming to the surrounding such as the attachments to the surface of the cloth. The design concept, material/fabrication process, and driving mechanism described in this paper can be potentially extended to a variety of flexible devices and soft robots.

This paper reports the control and manipulation of the moving trajectories of insect-scale soft robots by the applied driving electrical voltage frequency utilizing the asymmetric structural design of the actuating mechanism. Three distinctive advancements have been achieved: (1) a simple asymmetric structural design to create uneven responses on the legs of artificial insects to realize motion controls; (2) the capability of moving forward, leftward and rightward by adjusting the applied driving frequency; and (3) a demonstration of recording the ethanol concentration map around an area in real time by carrying a gas sensor on top of the robot on a controlled path bypassing the existing obstacles. As such, this work can advance the state-of-art technologies on wirelessly controlled, unmanned robots for various potential applications.

We present a new way for the fabrication of stretchable electronics systems without photolithography procedures by exploiting the through-cut, tunnel-cut, and blind-cut modes of a commercial desktop-size vinyl cutter. Compared to previous studies involving photolithography, our method produces a batch of representative devices in significantly reduced time (by ∼69%) and cost (by ∼73%) with similar feature sizes $(100-1000\mu \mathrm{m})$ . Our inclusion of blind-cut and tunnel-cut modes facilitates the fabrication of complex stretchable electronics systems, rather than only electrodes and interconnects. Finally, we demonstrate the capacities of our photolithography-free fabrication via a stretchable temperature-mapping patch and flexible functional electronic components.

We have successfully demonstrated a stretchable gas sensor with enhanced sensitivity to hydrogen based on crumpled graphene structures. Compared with the state-of-art technologies, three distinctive advancements have been achieved: (1) enhanced sensitivity to hydrogen based on the crumpled graphene at room temperature; (2) a model on the enhanced sensing mechanism on the crumpled graphene surface; and (3) stretchable gas sensor utilizing crumpled graphene with up to 200% stretchability. As such, the proposed sensing scheme and results could open up a new class of graphene-based, stretchable gas sensing systems for environmental and biological monitoring.

© 2018 ISIF In this paper we address the challenging task of tracking a fast-moving bicycle, in the indoor velodrome environment, using inertial sensors and infrequent position measurements. Since the inertial sensors are physically in the intrinsic frame of the bike, we adopt an intrinsic frame dynamic model for the motion, based on curvilinear dynamical models for manoeuvring objects. We show that the combination of inertial measurements with the intrinsic dynamic model leads to linear equations, which may be incorporated effectively into particle filtering schemes. Position measurements are provided through timing measurements on the track from a camera-based system and these are fused with the inertial measurements using a particle filter weighting scheme. The proposed methods are evaluated on synthesised cycling datasets based on real motion trajectories, showing their potential accuracy, and then real data experiments are reported.

We establish the dynamics relative to flapping angle of the flapping wing vehicle based on the quasi-steady aerodynamics model and Newton-Euler motion equations, and describe the stability analysis method of linearized perturbation equation under active control by varying flapping frequency based on the Routh-Hurwitz stability criterion. To give a demonstration, we design a flapping wing mechanism and analyze its stability under the linear controller. As a typical benchmark, the aerodynamic and kinematic parameters of a hummingbird are used to estimate stability derivatives of the flapping-wing vehicle. The feasible region for the gain selection of our linear controller is finally obtained.

Micro hotplate (MHP) is a widely used temperature controlling platform for semiconductor gas sensors. The gas sensors performances including sensitivity, selectivity and response properties are temperature sensitive, therefore, the precise control of working temperature by the MHP is critical for these sensors. To improve the temperature control accuracy, besides the optimization of circuit structure which is commonly used, this paper also proposes a heating strategy according to the factors obtained from circuit test to achieve higher thermal accuracy by adjusting the circuit control parameters.