Accurate swimming performance monitoring has traditionally relied on wearable sensors, which can disrupt natural technique and are impractical in competitive settings. In this paper, we present a fully vision-based system for automatic swimmer analysis using overhead drone footage, removing the need for any wearable device or underwater equipment. By fine-tuning pose estimation models for aerial aquatic conditions, our approach robustly extracts full-body swimmer skeletons even under challenging scenarios such as splashes and partial occlusions. From these poses, we classify swimming strokes, compute instantaneous speed, estimate lap times, and count individual strokes. Unlike existing methods, our system provides scalable, unobtrusive, and infrastructure-free tracking. Evaluated on real-world drone-captured swimming competition data, our method achieves a median speed estimation error below 4% (under 0.05 m/s), a median lap time error of just 0.03s, and stroke count errors typically under one stroke per lap.

In this paper, a soft rotary wing robot capable of flight and control is presented. The Collapsible Airfoil Single Actuator ROtor-craft (CASARO) is a single actuator monocopter that derives its geometric properties from the Samara seed. CASARO achieves better flight efficiency, lift, and handling ergonomics by reducing its overall volume by 91.7% when collapsed and stowed. Unlike conventional rotorcraft, CASARO uses a non-rigid fabric wing to produce lift in flight. It utilizes the robot's rotational velocity to maintain tension within its fabric and airframe, providing adequate lift during its hover state. The conception, design, construction, and control of the soft monowing are demonstrated, including its capability to reduce its footprint with its soft fabric construction. To analyze the flight dynamics of CASARO, the craft is flown indoors autonomously, tracking its wing surface, craft body attitude, and position with various step inputs to observe different wing dynamics. CASARO is also capable of being deployed outdoors for real-life human-operated flight.

Artificial Potential Field (APF) methods are widely used for reactive flocking control, but they often suffer from challenges such as deadlocks and local minima, especially in the presence of obstacles. Existing solutions to address these issues are typically passive, leading to slow and inefficient collective navigation. As a result, many APF approaches have only been validated in obstacle-free environments or simplified, pseudo-3D simulations. This paper presents GO-Flock, a hybrid flocking framework that integrates planning with reactive APF-based control. GO-Flock consists of an upstream Perception Module, which processes depth maps to extract waypoints and virtual agents for obstacle avoidance, and a downstream Collective Navigation Module, which applies a novel APF strategy to achieve effective flocking behavior in cluttered environments. We evaluate GO-Flock against passive APF-based approaches to demonstrate their respective merits, such as their flocking behavior and the ability to overcome local minima. Finally, we validate GO-Flock through obstacle-filled environment and also hardware-in-the-loop experiments where we successfully flocked a team of nine drones—six physical and three virtual— in a forest environment.

This paper presents an aerial robotic platform for rapid remote elevated overhead-perching drill operations for wood health inspection. The platform features an innovative passive prismatic-gripper mechanism affixed to the aerial robot’s top, facilitating overhead drilling. The primary aim is to enhance the safety and efficiency of elevated wood structure inspection using the resistography method, which involves drilling into wooden structures to identify internal voids. The research centers on two key enabling technologies: a gripper mechanism for secure attachment to target surfaces and a tethered drill configuration for drilling operations. The novel gripper mechanism enables drilling on large planar surfaces and even small beam-width structures. The paper concludes with discussions on design simulations and drill resistance experiments, highlighting the effectiveness of the proposed approach in detecting internal cavities within wooden structures.

In this paper, the dynamics of an emerging class of rotating nature-inspired micro aerial vehicles known as the Monocopter is proven and shown to be differentially flat. By exploiting this phenomenon, trajectory tracking can now be implemented on Monocopters via feed-forward terms that are computed per the trajectory. To demonstrate this, a Monocopter in the form of a Stable Invertible Coaxial Actuated ROtorcraft (SICARO) is chosen to harness this approach fully. The SICARO is capable of flying with either side of the wing facing up and this feature determines the craft’s direction of rotation about its body Z axis as well. In addition, it has the unique feature of a coaxial motor configuration that allows for a pitching-up moment regardless of the wing side facing up. The feed-forward terms computed are fused into a cascaded nonlinear controller on the craft to ensure its effectiveness in tracking trajectories. Lastly, the flight experiments extend to both sides of the wing to validate this method as being applicable for trajectory tracking for Monocopters such as the SICARO which has an extended range of flying capabilities.

General Visual Inspection is a manual inspection process regularly used to detect and localise obvious damage on the exterior of commercial aircraft. There has been increasing demand to perform this process at the boarding gate to minimise the downtime of the aircraft and automating this process is desired to reduce the reliance on human labour. Automating this typically requires estimating a camera’s pose with respect to the aircraft for initialisation but most existing localisation methods require infrastructure, which is very challenging in uncontrolled outdoor environments and within the limited turnover time (approximately 2 hours) on an airport tarmac. Additionally, many airlines and airports do not allow contact with the aircraft’s surface or using UAVs for inspection between flights, and restrict access to commercial aircraft. Hence, this paper proposes an on-site method that is infrastructure-free and easy to deploy for estimating a pan-tilt-zoom camera’s pose and localising scan images. This method initialises using the same pan-tilt-zoom camera used for the inspection task by utilising a Deep Convolutional Neural Network fine-tuned on only synthetic images to predict its own pose. We apply domain randomisation to generate the dataset for fine-tuning the network and modify its loss function by leveraging aircraft geometry to improve accuracy. We also propose a workflow for initialisation, scan path planning, and precise localisation of images captured from a pan-tilt-zoom camera. We evaluate and demonstrate our approach through experiments with real aircraft, achieving root-mean-square camera pose estimation errors of less than 0.24 m and 2$^\circ$for all real scenes.

Feature-based methods are a popular method for camera state estimation using event cameras. Due to the spatiotemporal nature of events, all event images exhibit smearing of events analogous to motion blur for a camera under motion. As such, events must be motion compensated to derive a sharp event image. However, this presents a causality dilemma where motion prior is required to unsmear the events, but a sharp event image is required to estimate motion. While it is possible to use the IMU to develop motion prior, it has been shown that the limited dynamic range of$\pm \mathbf{2000}^{\circ}/\mathrm{s}$is insufficient for high angular rate rotorcrafts. Furthermore, smoothing of motion-compensated images due to actual event detection time latency in event cameras severely limits the performance of feature-based methods at high angular rates. This paper proposes a Fourier-based angular rate estimator capable of estimating angular rates directly on non-motion compensated event images. This method circumvents the need for external motion priors in camera state estimation and sidesteps problematic smoothing of features in the spatial domain due to motion blur. Lastly, using an NVIDIA Jetson Xavier NX, the algorithm is demonstrated to be real-time performant up to 3960°/s.

The information provided to a chasing quadrotor (Chaser) about a moving target's linear velocity during pursuit offers utility beyond simply knowing how fast the target is moving linearly. During such pursuits for the Chaser, external tracking of its linear velocity is necessary to establish stable close-loop control. The alternative, an open-loop system, relies solely on the linear velocity outputs from its dynamics which usually results in unstable flight performances due to imperfections in the model's fidelity. Thus, this paper presents a Cascading Velocity Model Predictive Control Framework (CVMPC) for the Chaser to leverage on the real-time linear velocity feedback of the target for stable open-loop linear velocity control. By using this information with Gaussian Processes (GPs), the Chaser's linear velocity outputs from its dynamics are augmented in real-time and cascaded from one control step into the next within a Model Predictive Controller. Simulations in Gazebo further verify the performance of CVMPC against a MPC with external linear velocity tracking (close-loop system) with RMSE in euclidean linear velocity and distance being less than 1m/s and 1m respectively.

In this paper, we introduce a Modular Single Actuator Monocopter (M-SAM), which is capable of flying in both singular configuration and cooperative configuration. From singular mode, M-SAMs can be manually assembled into cooperative mode, using magnetic connectors built into the body of each M-SAM unit. The design of the connectors allow for passive separation of the units without the need for a dedicated separating actuator, by harnessing the variable centrifugal force from controlled adjustment of the rotating speed of the craft. To achieve control in both configurations, we firstly studied and analyzed their full dynamic models by introducing equilibrium state and relaxed hovering condition. Next, we derived a reduced model to approximate the dynamical behavior of both singular and cooperative configuration in flight to design a generalized cyclic-based cascaded flight controller. Finally, we validated the proposed controller and separation mechanism by conducting several flight experiments for two M-SAMs in singular mode, cooperative mode as well as mid-air separating under motion capture system.

Large scale aerial deployment of miniature sensors in tough environmental conditions requires a deployment device that is lightweight, robust, and steerable. We present a novel samara-inspired autorotating craft that is capable of two flight modes (autorotating mode and diving mode) with an average glide angle of 28.9$^{\circ }$(1.81 m lateral distance per 1 m loss of altitude) in the former mode. The bidirectional transition between the two modes and directional control is achieved by using only a single actuator. Also, in order to minimize its glide angle, a design optimization methodology is presented for our prototype, diving samara autorotating wing, along with a new cyclic control strategy for directional control of autorotating descent. The dynamic model, simulated in a six degrees-of-freedom environment using the blade element theory, is integrated with genetic algorithm to derive parameters for the wing geometry, flap angle for autorotation, and the proposed cyclic control. The physical prototype autorotates at a descent velocity of 1.43 m/s and rotation speed 4.17 Hz, and is able to transit to diving mode in an average duration of 272 ms to increase its descent velocity by at least 17.6 times. At any point during the dive, it is able to transit back into autorotation in an average duration of 327 ms. Semioutdoor experiments were used to investigate the bidirectional transitions and verify the glide angle (28.9$^{\circ }$), which is much improved from the previous prototype (SAW+, 58.4$^{\circ }$). Lastly, as a demonstration of a real-life deployment scenario and environmental conditions, the prototypes were dropped from a fixed-wing unmanned aerial vehicle at a suburban test site.

General Visual Inspection is a manual inspection process regularly used to detect and localise obvious damage on the exterior of commercial aircraft. There has been increasing demand to perform this process at the boarding gate to minimize the downtime of the aircraft and automating this process is desired to reduce the reliance on human labour. This automation typically requires the first step of estimating a camera’s pose with respect to the aircraft for initialisation. However, localisation methods often require infrastructure, which can be very challenging when performed in uncontrolled outdoor environments and within the limited turnover time (approximately 2 hours) on an airport tarmac. In addition, access to commercial aircraft can be very restricted, causing development and testing of solutions to be a challenge. Hence, this paper proposes an on-site infrastructure-less initialisation method, by using the same pan-tilt-zoom camera used for the inspection task to estimate its own pose. This is achieved using a Deep Convolutional Neural Network trained with only synthetic images to regress the camera’s pose. We apply domain randomisation when generating our dataset for training our network and improve prediction accuracy by introducing a new component to an existing loss function that leverages on known aircraft geometry to relate position and orientation. Experiments are conducted and we have successfully regressed camera poses with a median error of 0.22 m and 0.73°.

Electroencephalography (EEG) is a diagnostic test that involves placing electrodes at specific locations on the human head to detect and study electrical signals of brain activity. Current practitioners use a measuring tape and wax pencil to determine electrode positions and mark them using the internationally recognized 10–20 system. This meticulous procedure is time-consuming and laborious as it is manual. Hence, in this paper, we propose a rapid and robust method to determine the optimal electrode positions using an optical motion capture system (Optitrack) and a customized stylus. The stylus affixed with reflective markers is tracked by the motion capture system as it is used to trace different regions of the head in order to estimate the head geometry utilizing the 3D coordinate data of the trace throughout time. The 21 EEG electrode positions are then algorithmically predicted using the acquired spatial coordinate data. With testing under various experimental settings, the accuracy value in terms of Root Mean Square Error (RMSE) of the predicted EEG electrode positions is less than 1 cm with half the amount of time needed. Thus, the proposed method is assured to be faster and decreases errors due to imprecise electrode placement and determination.

Robotic Wire Arc Additive Manufacturing (WAAM) utilizes a robot arm as a motion system to build 3D metallic objects by depositing weld beads one above the other in a layer by layer fashion. A key part of this approach is the process study and control of Multi-Layer Multi-Bead (MLMB) deposition, which is very sensitive to process parameters and prone to error stacking. Despite its importance, it has been receiving less attention than its single bead counterpart in literature, probably due to the higher experimental overhead and complexity of modeling. To address these challenges, this paper proposes an integrated learning-correction framework, adapted from Model-Based Reinforcement Learning, to iteratively learn the direct effect of process parameters on MLMB print while simultaneously correct for any inter-layer geometric digression such that the final output is still satisfactory. The advantage is that this learning architecture can be used in conjunction with actual parts printing (hence, in-situ study), thus minimizing the required training time and material wastage. The proposed learning framework is implemented on an actual robotic WAAM system and experimentally evaluated.

Pose estimation is of paramount importance for flight control as well as localization and navigation of Unmanned Aerial Vehicles (UAVs) to enable autonomous operations. In environments without GPS, such estimation can only be determined using onboard sensors; optical flow using a monocular camera is a popular approach. Monocopters are a class of nature inspired UAVs known as free rotors where their design and flight dynamics are inspired by the falling samara seed. With a constantly rotating body frame, free rotors introduces some unique challenges for visual perception required during optical flow sensing. This paper addresses these problems with the introduction of SHIFT (Selective Heading Image for Translation) that selects optimal images for determining translation with optical flow. It achieves this by decoupling rotation vectors about the optical axis from translation vectors in a flow field through the separate tracking of orientation and position using an Unscented Kalman Filter with phase correlation in the log-polar and spatial domain. The experiments show that SHIFT's estimation in orientation is stable even under sinusoidal excitation with a median absolute percentage errors of less than 1%. It is able to track position and orientation of a UAV accurately.

In this paper, the high angular rate estimation for simultaneous localisation and mapping (SLAM) of a Flying Li-DAR (Flydar) is presented. The proposed EKF-based algorithm exploits the sinusoidal magnetometer measurement generated by the continuously rotating airframe for estimation of the robot hovering angular velocity. Significantly, the proposed method does not rely on additional sensors other than existing IMU sensors already being used for flight stabilization. The gyro measurement and the gyro bias are incorporated as a control input and a filter state respectively to enable estimation even under gyro saturation condition. Additionally, this work proposes leveraging on the inherently rotating locomotion to generate a planar lidar scan using only a single-point laser for possible lightweight autonomy. The proposed estimation method was experimentally evaluated on a ground rotating rig up to twice the gyro saturation limit with an effective rms error of 0.0045Hz; and on the proposed aerial platform - Flydar - hovering beyond the saturation limit with a rms error of 0.0056Hz. Lastly, the proposed method for SLAM using the rotating dynamics of Flydar was demonstrated with a localisation accuracy of 0.11m.

Recent failures in real-world self-driving tests have suggested a paradigm shift from directly learning in real-world roads to building a high-fidelity driving simulator as an alternative, effective, and safe tool to handle intricate traffic environments in urban areas. To date, traffic simulation can construct virtual urban environments with various weather conditions, day and night, and traffic control for autonomous vehicle testing. However, mutual interactions between autonomous vehicles and pedestrians are rarely modeled in existing simulators. Besides vehicles and pedestrians, the usage of personal mobility devices is increasing in congested cities as an alternative to the traditional transport system. A simulator that considers all potential road-users in a realistic urban environment is urgently desired. In this work, we propose a novel, extensible, and microscopic method to build heterogenous traffic simulation using the force-based concept. This force-based approach can accurately replicate the sophisticated behaviors of various road users and their interactions through a simple and unified way. Furthermore, we validate our approach through simulation experiments and comparisons to the popular simulators currently used for research and development of autonomous vehicles.

Plane-to-plane transitioning has been a significant challenge for climbing robots. To accomplish this, additional actuator or robot module is usually required which significantly increases both size and weight of the robot. This paper presents a two-wheg miniature climbing robot with a novel passive vertical tail component which results in robust transitioning capabilities. The design decision was derived from an indepth force analysis of the climbing robot while performing the transition. The theoretical analysis is verified through a working prototype with robust transitioning capabilities whose performance follows closely the analytical prediction. The climbing robot is able to climb any slope angles, 4-way internal transitions, and 4-way external transitions. This work contributes to the understanding and advancement of the transitioning capabilities and the design of a simple climbing robot, which expands the possibilities of scaling down miniature climbing robot further.

In this work, reinforcement learning is used to develop a position controller for an underactuated nature-inspired Unmanned Aerial Vehicle (UAV). This particular configuration of UAVs achieves lift by spinning its entire body contrary to standard multi-rotors or fixed-wing aircraft. Deep Deterministic Policy Gradients (DDPG) with Ape-X Distributed Prioritized Experience Replay was used to train neural network function approximators that were implemented as the final control policy. The reinforcement learning agent was trained in simulations and directly ported over to real-life hardware. Position control tests were performed on the learned control policy and compared to a baseline PID controller. The learned controller was found to exhibit better control over the inherent oscillations that arise from the non-linear dynamics of the platform.

Advances in Unmanned Aerial Vehicle (UAV) opens venues for application such as tunnel inspection. Owing to its versatility to fly inside the tunnels, it can quickly identify defects and potential problems related to safety. However, long tunnels, especially with repetitive or uniform structures pose a significant problem for UAV navigation. Furthermore, post-processing visual data from the camera mounted on the UAV is required to generate useful information for the inspection task. In this work, we design a UAV with a single rotating camera to accomplish the task. Compared to other platforms, our solution can fit the stringent requirement for tunnel inspection, in terms of battery life, size and weight. While the current state-of-the-art can estimate camera pose and 3D geometry from a sequence of images, they assume large overlap, small rotational motion, and many distinct matching points between images. These assumptions severely limit their effectiveness in tunnel-like scenarios where the camera has erratic or large rotational motion, such as the one mounted on the UAV. This paper presents a novel solution which exploits Structure-from-Motion, Bundle Adjustment, and available geometry priors to robustly estimate camera pose and automatically reconstruct a fully-dense 3D scene using the least possible number of images in various challenging tunnel-like environments. We validate our system with both Virtual Reality application and experimentation with a real dataset. The results demonstrate that the proposed reconstruction along with texture mapping allows for remote navigation and inspection of tunnel-like environments, even those which are inaccessible for humans.

Development of mesoscale robots is gaining interest in security and surveillance domains due to their stealth and portable nature in achieving tasks. Their design and development require a host of hardware, controls, and behavioral innovations to yield fast, energy-efficient, distributed, adaptive, robust, and scalable systems. We extensively describe one such design and development process by: (1) the genealogy of our embedded platforms; (2) the key system architecture and functional layout; (3) the developed and implemented design principles for mesoscale robotic systems; (4) the various key algorithms developed for effective collective operations of mesoscale robotic swarms, with applications to urban sensing and mapping. This study includes our perception of the embedded hardware requirements for reliable operations of mesoscale robotic swarms and our description of the key innovations made in magnetic sensing, indoor localization, central pattern generator control, and distributed autonomy. Although some elements of the design process of such a complex robotic system are inevitably ad-hoc, we focus on the system-of-systems design process and the component design integration. This system-of-systems process provides a basis for developing future systems in the field, and the designs represent the state-of-the-art development that may be benchmarked against and adapted to other applications.

Traditional image stitching algorithms use transforms such as homography to combine different views of a scene. They usually work well when the scene is planar or when the camera is only rotated, keeping its position static. This severely limits their use in real world scenarios where an unmanned aerial vehicle (UAV) potentially hovers around and flies in an enclosed area while rotating to capture a video sequence. We utilize known scene geometry along with recorded camera trajectory to create cylindrical images captured in a given environment such as a tunnel where the camera rotates around its center. The captured images of the inner surface of the given scene are combined to create a composite panoramic image that is textured onto a 3D geometrical object in Unity graphical engine to create an immersive environment for end users.

This paper presents the design and fabrication of ORION-II for autonomous Intelligence, Surveillance and Reconnaissance (ISR). ORION-II is a miniature climbing robot equipped with all the necessary electronic components to achieve ISR tasks. It consists of a robot chassis (tail) carrying the electronics and two DC motors each driving a wheel-leg (wheg) with four “flaps” equipped with bilayer compliant tapes. Two types of tapes are used for attachment of ORION-II: bilayer PDMS/foam and bilayer micro-suction/foam. The two types of tapes are tested on different climbing surfaces, and the climbing performance is reported. ORION-II could climb rougher surfaces when using the PDMS/foam tape, and perform internal climbing transitions when using the micro-suction/foam tape. The total weight of ORION-II is 153.18 g as compared with 71.5 g of our previous version ORION-I.

This paper presents trajectory following control experiments of a miniature spherical rolling and spinning robot mechanism on three different types of outdoor surfaces. The research is inspired from the efficient locomotory rolling patterns of various insects in unstructured environment. A nonlinear adaptive sliding mode (ASMC) feedback method maintains the robot stability and robustness in the presence of parameter uncertainties and external disturbances. The proposed trajectory following control policy is developed, implemented and tested for the miniature spherical robot on three different types of irregular surfaces in outdoors. Trajectory following accuracy, roll angle stability and wheel velocity response are three parameters measured to evaluate robot performance. ASMC controller is compared with an integral sliding (ISMC) controller. Experimental results show that proposed control policy is able to manage an accurate trajectory following amidst robust control of a rolling and spinning robot on three types of irregular surface in practical outdoor conditions.

The seeds of Maple trees (Samara) use autorotation as a unique mechanism to disperse their seeds. By exploiting gyroscopic stability of a spinning wing, the Samara is able to cover large horizontal distance despite having no form of propulsion. We applied and adapted this natural ability in our novel concept, the Samara Autorotating Wings (SAW), and extended its stability and direction controllability by generalizing the mechanism to incorporate designs with more than 1 wing. By conceiving cyclic control, the translational motion of autorotation is regulated. A nonlinear model of SAW with $n$ wings is derived and control schemes developed to control the translational position during autorotation. Numerical simulations were performed to investigate the performance of the multi-wing SAW prototypes to track a conical spiral autorotation trajectory. Direct experiments were conducted in a vertical wind-tunnel through a special ball joint that allows z-axis translation and all three rotational degrees of freedom. Finally, free-fall drop tests are used to verify the directional controllability and performance of SAW.

Percutaneous Nephrolithotomy is a recommended treatment method for large kidney stone removal. However, the first and most important step, i.e., getting the percutaneous access to create the tract between the targeted calyx and the flank skin, is challenging as the surgeon is often occupied by several tasks at a given time. Therefore, in this paper, we propose a robotic assisted system that collaborates with the surgeon and provides assistance in order for the surgeons to focus on more critical jobs resulting in better surgical performance. A procedure for this robot including three working stages is described. This procedure allows the surgeon to choose a suitable percutaneous target using an ultrasound probe based on his or her experience and the robot will track the respiratory motion of the target kidney stone and insert the needle automatically after the surgeon releases the probe. Experiments are conducted to demonstrate the procedure with the proposed assisted robot for PCNL.

This paper presents a novel caterpillar inspired rolling gait generation and control mechanism of a spherical robot. The research investigates efficient locomotory rolling gaits of Pleurotya caterpillar in unstructured environment. A similar rhythmic rolling pattern is produced for the spherical robot locomotion. A synergetically combined feedforward - feedback control strategy is further proposed. The feedforward component is generated from centrally connected pattern generators (CPGs)in conjunction with nonlinear robot dynamics. A nonlinear integral sliding mode (ISMC) feedback method regulates these rhythmic patterns to adjust robot stability and robustness in the presence of parameter uncertainties and external disturbances. The proposed control strategy is developed, implemented and tested for the spherical robot on both smooth and irregular surfaces. The robot performance is quantified by measuring the stability in roll angle and wheel velocities. Experimental results show that proposed novel strategy is efficient in producing a stable rolling gait and robust control of a spherical robot on different types of surface conditions.

In small mobile robots, decreasing the number of actuators is usually desirable to reduce the size and weight of the robot, but it is usually at the expense of the robot's degree of freedom (DOF). This work presents the development and preliminary experimental testing of a novel Legged Piezoelectric Miniature Robot (LPMR) driven only by a single piezoelectric unimorph actuator and yet fully capable of being maneuvered to move forward, turn right, or turn left. The underactuated motion is achieved by exploiting the bending vibration modes disparity of the piezoelectric actuator at different driving frequencies and designing specific positions of the robot's legs to generate a differential-drive-like mechanism. The speed of the robot can be controlled through regulating the magnitude of the applied voltage. The proposed underactuated system is experimentally verified and a preliminary characterization of the LPMR in terms of its forward and turning speed versus applied voltage and payload is investigated and reported.

In monocopter flight, two important parameters are required for control: angular rates and heading direction. Small monocopters fly at a very high speed (more than 600rpm), which can be out of the typical gyroscope limit. Very high speed gyroscopes do exist, but the price is high and it can only measure a single axis rotation. This paper presents an alternative approach to measure angular rates by using three accelerometers. The readings of the accelerometers are subtracted to calculate the angular rates in all three axes (x, y, and z). This paper also proposes to use the Extended Kalman Filter (EKF) to estimate the heading direction based on the magnetometer reading and the angular rates. The angular rates direction is used as the vertical direction reference. The proposed method has been applied on two setups: DC Motor setup (for quantifying the method's performance) and Monocopter setup. In the DC Motor setup, the motor encoder is used as the ground truth for the heading direction. The result is compared with the usual method of using only the magnetometer to obtain the heading direction of monocopters. The EKF result is more accurate and stable even in the presence of strong magnetic disturbances. In addition, the angle of attack and the coning angle can also be determined by the proposed method.

This paper describes the Transformable HOvering Rotorcraft (THOR), a prototype Unmanned Aerial Vehicle (UAV) that explores a novel approach in combining the range and speed of a horizontal flying platform with the hovering and maneuverability of a rotor-wing. This is achieved by integrating a tailless flying wing configuration with a single-axis rotor, or monocopter. By maintaining full utilization of all aerodynamic surfaces and propulsion sources in both flight modes, this method represents the most structurally efficient approach to achieving a cruising mode and a hovering mode on the same frame. Using a dual servo and motor configuration, we propose an under-actuated system that is able to achieve controllability in 4 degrees of freedom while in its horizontal cruising mode and in 5 degrees of freedom while in its hovering mode. In both indoor and outdoor experiments, the UAV is able to transition between either flight modes seamlessly and repeatedly without the need for any additional mechanisms and actuators.

In this paper, we developed a kinematic model for a legged piezoelectric miniature robot driven by standing wave. The robot consists of a piezoelectric bending actuator with rigidly attached legs. The kinematic model allows us to compute the velocity analytically so that the robot motion can be predicted. To construct the kinematic model, the behavior of the robot at the contact instances with the ground during the up and down stages is studied. A prototype is fabricated so as to verify our kinematic model experimentally, measured using a motion capture system. The result shows that the measured speed was in close agreement with what was predicted by our model.

This paper describes the design and evaluation of a Legged Piezoelectric Miniature Robot (LPMR) propelled by standing wave vibrations at the legs, which are biologically inspired by the bounding gait locomotion of animals. The LPMR comprises of a single piezoelectric patch, a metal beam, two contact joints, two rigid legs and is able to achieve tether-less remote controlled operation. Through analysis of the bending modes of vibrations and driving frequency, a forward and backward motion of the underactuated system is achieved by choosing specific positions for the legs. At 100 V amplitude, the LPMR with the weight of 6.27 g, length of 50 mm, width of 10 mm and height of 1.5 mm achieves a maximum linear speed of 246.5 mm/s for forward motion and 302 mm/s for backward motion. The LPMR is also able to carry a payload of 100 g at a speed of 49.6 mm/s for forward motion and 87.9 mm/s for backward motion when applying 100 V amplitude. The corresponding maximum force generated by the LPMR is 9.8 mN during forward motion and 12 mN during backward motion at the same applied voltage. An experimental characterization for the LPMR in terms of speed versus applied voltage, speed versus embedded mass and blocking force for different applied voltages is explored and evaluated in this study.

Automated welding has been very effective in enhancing the quality and quantity of weld jobs along with improving the safety of the workers. Nevertheless, most existing welding robots are massive and immobile. Typically, the work-pieces are transferred to the robots for welding. This makes it difficult for many welding applications that have large scale structures e.g. shipbuilding, construction, on-site repair work, etc. In these cases, the welding robot should be able to be transported to the work-pieces. The purpose of this paper is to explore and study various recent developments in portable welding robot designs. Based on this, several strategies of designing portable welding robot are classified and discussed.

In this paper, a miniature nonholonomic spherical rolling robot capable of navigating over two dimensional surfaces is described. This 55 gram spherical robot consists of a 6 cm diameter external spherical shell driven by an internal two-wheeled differential drive cart. A gravity powered pendulum effect is produced as the internal device climbs up the internal surface of the shell, propelling the robot forward up to a speed of 0.16m/s. We derived its dynamic model using Lagrangian, and studied its dynamics and performance under various applied torques. The spherical robot is built and its overall mechanical, hardware and control architecture are elaborated. Experiments are conducted to evaluate the robot open and closed loop performance on a linear trajectory, captured using an optical motion capture system. We showed that with our implemented PD controller, the robot can follow the desired orientation.

Magnetic localization systems based on passive permanent magnets (PM) are of great interest due to their ability to provide non-contact sensing and without any power requirement for the PM. Medical procedures such as ventriculostomy can benefit greatly from real-time feedback of the inserted catheter tip. While the effects of the number of sensors on the localization accuracy in such systems has been reported, the spatial design of the sensor layout has been largely overlooked. Here in this paper, a framework for determining an optimal sensor assembly for enhanced localization performance is presented and investigated through numerical simulations and direct experiments. Two approaches are presented: one based on structured grid configuration and the other derived using Genetic Algorithms. Simulation results verified by experiments strongly suggest that the layout of the sensors not only has an effect on the localization accuracy, but also has an effect far more pronounced than improvements brought by increasing the number of sensors.

Most of magnetic localization and orientation systems use single dipole models to calculate magnetic field, which, due to the fundamental limitation of the dipole, become inaccurate as the sensors approach the surface of the magnet. Moreover, they are unable to account for geometry, magnetization and any physical imperfections of the magnetic source. This paper presents a novel method of modeling the magnetic field of axisymmetric permanent magnets with artificial neural networks (ANNs), which permits accurate field modeling even at close proximity to the magnet. ANN based field models used to characterize experimental field data of solid and annular cylindrical magnets were found to be on average at least 10 times more accurate than that of dipole based models. Using model-based localization, tracking results from following a predetermined figure `8' path were also promising, with an average error of 0.43 mm in XY plane and 0.93 mm in XZ plane from only three sensor inputs.

This paper presents a novel design approach for a two-DOF foot force sensor for a high speed running quadruped. The adopted approach harnesses the deformation property of an elastomeric material to relate applied force to measurable deformation. A lightweight, robust and compact magnetic-field based sensing system, consisting of an assembly of miniature hall-effect sensors, is employed to infer the positional information of a magnet embedded in the elastomeric material. Instead of solving two non-linear models (magnetic field and elastomeric) sequentially, a direct approach of using artificial neural networks (ANN) is utilized to relate magnetic flux density (MFD) measurements to applied forces. The force sensor, which weighs only 24.5 gms, provides a measurement range of 0 – 1000 N normal to the ground and up to ± 125N parallel to the ground. The mean force measurement accuracy was found to be within 7% of the applied forces. The sensor designed as part of this work finds direct applications in ground reaction force sensing for a running quadrupedal robot.

This paper presents an alternative control strategy for permanent magnet (PM) based spherical actuators capable of multi-DOF precision manipulation. Unlike existing control methods which rely on separate sensing systems, this direct approach utilizes magnetic field measurements for feedback and eliminates the complicated multi-DOF orientation detection in closed-loop control which may cause time-delay and affect system sampling rate. By capitalizing on the rotor magnetic field implicit dependence on orientation, the control law derivation and torque coefficient estimation can be obtained simultaneously and directly from field measurements without explicit determination of the rotor orientation, thereby improving computational efficiency and eliminating error accumulation. The control method is simulated in 2-DOF motion with a CAD model of a spherical actuator.

This paper presents a sensing method that harnesses the capacity of modern sensors to measure vector fields. This approach directly maps distributed independent field measurements to the instantaneous orientation of a spherical joint embedded with low-cost permanent magnets. Unlike existing methods which require a priori and precise field models, this direct method engages an artificial neural network to associate a collection of measurements to joint orientation. The operation of both bipolar and unipolar single and multi-axis sensors were considered and evaluated experimentally.

This paper presents a method to determine the 2-D profile and motion of a live product (such as chicken for poultry meat processing) on a moving conveyor from a lateral optical sensor that consists of an orthogonal pair of line array (LA) scanners. Unlike most line array (LA) scanners designed to provide a 2-D image of a static object, the lateral optical sensor presented here offers a practical means to detect object slippage on the conveyor in real time. Three examples are given to illustrate the effectiveness of this sensing method. The first simulates the 2-D boundary of a geometrically well-defined object on an accelerating conveyor, which offers intuitive insights on the effects of conveyor dynamics and object slippage on the accuracy of the 2-D boundary measurement. The second experimentally demonstrates the extendibility of LA sensors to detect both engineering and natural objects. The final example illustrates the application of the lateral optical sensor as a real time feedback sensor for active singulation of natural objects.

This paper presents a method to determine the 2D profile and velocity of an object on a moving conveyor from a lateral optical sensor that consists of an orthogonal pair of line array (LA) scanners. Unlike most LA scanners which are designed to provide a 2D image of a static object, the lateral optical sensor presented here offers an additional and practical means to detect object slippage on the conveyor in real time. We illustrate numerically the effectiveness of this sensing method with two illustrative examples. The first simulates the 2D boundary of a geometrically well-defined object on an accelerating conveyor, which offers intuitive insights on the effects of conveyor dynamics and object slippage on the accuracy of the 2D boundary measurement. The second demonstrates the application of the lateral optical sensor as a real time feedback sensor for active singulation of natural objects.