With the emergence of connected autonomous vehicle (CAV) technologies, autonomous intersection management (AIM) has become a preferred approach to manage traffic flows at intersections. In our earlier work, we identified potential execution discrepancies between CAV local planners and global AIM systems. We have also developed a Vehicle-to-Vehicle (V2V) based local planner to enhance safety under perturbation conditions. Building on these findings, this study further assesses safety in scenarios with connection losses by employing onboard motion predictors (OMPs) to compensate for information gaps caused by intermittent connectivity. Results show that our scheme maintains safety under modest connection loss using simple extrapolation prediction replacement while incorporating advanced OMP helps near-miss scenarios under severe connection loss.

Multi-task missions for unmanned aerial vehicles (UAVs) involving inspection and landing tasks are challenging for novice pilots due to the difficulties associated with depth perception and the control interface. We propose a shared autonomy system, alongside supplementary information displays, to assist pilots to successfully complete multi-task missions without any pilot training. Our approach comprises of three modules: (1) a perception module that encodes visual information onto a latent representation, (2) a policy module that augments pilot’s actions, and (3) an information augmentation module that provides additional information to the pilot. The policy module is trained in simulation with simulated users and transferred to the real world without modification in a user study (\(\mathbf{n=29}\)), alongside alternative supplementary information schemes including learnt red/green light feedback cues and an augmented reality display. The pilot’s intent is unknown to the policy module and is inferred from the pilot’s input and UAV’s states. The assistant increased task success rate for the landing and inspection tasks from [16.67% & 54.29%] respectively to [95.59% & 96.22%]. With the assistant, inexperienced pilots achieved similar performance to experienced pilots. Red/green light feedback cues reduced the required time by 19.53% and trajectory length by 17.86% for the inspection task, where participants rated it as their preferred condition due to the intuitive interface and providing reassurance. This work demonstrates that simple user models can train shared autonomy systems in simulation, and transfer to physical tasks to estimate user intent and provide effective assistance and information to the pilot.

This article studies a distributed consensus law to achieve a coordinated trochoidal formation among a team of mobile agents. The mobile agents are modeled as double integrators. We generalize an existing consensus algorithm for double-integrator dynamics (1) to achieve trochoidal patterns under a general network topology and (2) to extend the pattern formation in a 3-D plane. Since the trochoidal patterns are 2-D curves, the proposed control law allows us to specify the 2-D plane in the 3-D space on which the patterns will be formed. We modify the standard consensus protocol and manipulate the gains to create trochoidal motion through local information exchange by appropriately placing the eigenvalues of the system. Trochoids have been used in multifaceted robotic applications such as area coverage, hurricane sampling, and orbit designs for satellites, apart from their aesthetic appeal. Computer simulations and multi-robot experiments are presented to validate the theoretical findings.

Multi-task missions for unmanned aerial vehicles (UAVs) involving inspection and landing tasks are challenging for novice pilots due to the difficulties associated with depth perception and the control interface. We propose a shared autonomy system, alongside supplementary information displays, to assist pilots to successfully complete multi-task missions without any pilot training. Our approach comprises of three modules: (1) a perception module that encodes visual information onto a latent representation, (2) a policy module that augments pilot's actions, and (3) an information augmentation module that provides additional information to the pilot. The policy module is trained in simulation with simulated users and transferred to the real world without modification in a user study (n=29), alongside supplementary information schemes including learnt red/green light feedback cues and an augmented reality display. The pilot's intent is unknown to the policy module and is inferred from the pilot's input and UAV's states. The assistant increased task success rate for the landing and inspection tasks from [16.67%&54.29%] respectively to [95.59%&96.22%]. With the assistant, inexperienced pilots achieved similar performance to experienced pilots. Red/green light feedback cues reduced the required time by 19.53% and trajectory length by 17.86% for the inspection task, where participants rated it as their preferred condition due to the intuitive interface and providing reassurance. This work demonstrates that simple user models can train shared autonomy systems in simulation, and transfer to physical tasks to estimate user intent and provide effective assistance and information to the pilot.

Parkinson's disease is a chronic and progressive neurodegenerative disorder with an estimated 10 million people worldwide living with PD. Since early signs are benign, many patients go undiagnosed until the symptoms get severe and the treatment becomes more difficult. The symptoms start intermittently and gradually become continuous as the disease progresses. In order to detect and classify these minute differences between gaits in early PD patients, we propose to use dynamic time warping (DTW). For a given set of gait data from a patient, the DTW algorithm computes the difference between any two gait cycles in the form of a warping path, which reveals small time differences between gait cycles. Once the time-warping information between all possible pairs of gait cycles is used as the main source of gait features, K-means clustering is used to extract the final features. These final features are fed to a simple logistic regression to easily and successfully detect early PD symptoms, which was reported as challenging using conventional statistical features. In addition, the use of DTW ensures that the obtained results are not affected by the differences in the style and speed of walking of a subject. Our approach is validated for the gait data from 83 subjects at early stages of PD, 10 subjects at moderate stages of PD, and 73 controls using the Leave-One-Out and N-fold cross-validation techniques, with a detection accuracy of over 98%. The high classification accuracy validated from a large data set suggests that these new features from DTW can be effectively used to help clinicians diagnose the disease at the earliest. Even though PD is not completely curable, early diagnosis would help clinicians to start the treatment from the beginning thereby reducing the intensity of symptoms at later stages.

Novice pilots find it difficult to operate and land unmanned aerial vehicles (UAVs), due to the complex UAV dynamics, challenges in depth perception, lack of expertise with the control interface and additional disturbances from the ground effect. Therefore we propose a shared autonomy approach to assist pilots in safely landing a UAV under conditions where depth perception is difficult and safe landing zones are limited. Our approach comprises of two modules: a perception module that encodes information onto a compressed latent representation using two RGB-D cameras and a policy module that is trained with the reinforcement learning algorithm TD3 to discern the pilot’s intent and to provide control inputs that augment the user’s input to safely land the UAV. The policy module is trained in simulation using a population of simulated users. Simulated users are sampled from a parametric model with four parameters, which model a pilot’s tendency to conform to the assistant, proficiency, aggressiveness and speed. We conduct a user study (n = 28) where human participants were tasked with landing a physical UAV on one of several platforms under challenging viewing conditions. The assistant, trained with only simulated user data, improved task success rate from 51.4% to 98.2% despite being unaware of the human participants’ goal or the structure of the environment a priori. With the proposed assistant, regardless of prior piloting experience, participants performed with a proficiency greater than the most experienced unassisted participants.

The routine inspection of railheads for defects such as wear and surface cracks is a tedious process, which, if not detected, can alter the wheel-rail contact interaction leading to catastrophic events. This study investigates and implements a railhead measurement method using image-based three-dimensional reconstruction, which enables rapid scanning of railheads and production of detail cross-sectional measurements for rail-wheel interface analysis. A complete workflow with a methodology for reconstructing railheads from images and extracting cross-sectional measurements from the reconstructed model is presented. In order to validate the proposed method in the field, a mobile automated system was equipped with an array of cameras specifically spaced to cover the areas of interest on the railhead. The system can automatically transverse along the railhead, acquiring images synchronously. Two case studies in the laboratory environment and the real railway site have been performed to evaluate the performance and accuracy against industry practices. The results show that the proposed method can accurately measure the railhead cross-sectional profile at an root mean square error (RMSE) less than 0.3 mm compared with MiniProf. Furthermore, continuous cross-sectional data and intuitive color information are provided by our method which can help inspectors to locate defects easily and more efficiently.

Fins play a vital role in improving the directional stability of aerial vehicles. However, airships are characterized by an inherent directional instability due to undersized fins. In this paper, a systematic approach to the design of airship fins is proposed. A constrained optimization problem is formulated to identify the optimal location, span, and chord of the airship fins. A semi-empirical aerodynamic model is used to formulate the objective function that represents the directional instability of an airship. The validity of the numerical solution acquired by minimizing the objective function with other design constraints is shown through a series of wind tunnel experiments. The result of the experiments confirms that the outcome of the fin optimization problem exhibits the best performance among the test cases, which validates the proposed methodology for designing the fins of the airship.

Modelling of dynamics of an airship is essential in the prediction of the flight characteristics, stability analysis and the design of control laws for autonomous operation. In the dynamics model that was proposed by Ashraf and Choudhry [1], deviations were observed between the simulation results and the validation data since the lengths and areas associated with the calculation of aerodynamic coefficients were not normalised. This paper documents the revisions proposed to the incorporation of semi-empirical aerodynamic estimation technique in the dynamics modelling of an airship as well as the normalisation of aerodynamic coefficients. Simulations are performed on the revised airship dynamics model and are compared with the simulation results of preceding models. A satisfactory agreement is seen between the revised model and the validation data, both in terms of magnitude and trends.

This paper extends our previous work on a pneumatic bending module and presents two more modules for rotational and translational motions. In these modules, antagonistic chambers enveloped by rigid shells are adopted to realize bidirectional actuation, and they are characterized by safe actuation, enhanced torque/force output, independent stiffness tuning, and real-time position control. Due to their mechanical modularity, they can be conveniently assembled into robotic systems with multiple degrees of freedom (DoFs) according to different requirements. A complete workflow is presented including the module design, fabrication, theoretical modelling, controller design, and experimental validation. A reconfigurable robotic arm with high dexterity is also assembled using these modules, demonstrating the effectiveness of the proposed modules to develop robotic systems for safe, forceful, and precise tasks.

Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.

This paper presents a control architecture for reference tracking of a small autonomous airship with input constraints. Nonlinear receding horizon optimization is used in order to generate a reference trajectory for the low-level controller to track. A simplified lateral dynamics model for an airship is also presented in this paper to be used for prediction. To investigate the efficacy of the proposed control algorithm, it is then implemented on a simulation platform and tested on a 6-degrees-of-freedom airship model to track a straight line and circular trajectory in the presence of wind disturbance. The simulation results indicate that the proposed planner generates a feasible trajectory for the low-level controller to track. A significant improvement in the tracking performance of the airship is also seen by the introduction of the planner.

There are many potential applications to utilise aerial robots in hazardous tunnel-like environments. For example, aiding human operators with inspections of small railway culverts or mineral mappings of mining tunnels. Nevertheless, such confined environments pose many challenges for quadcopters to navigate through. Suspended dust particles, poor lighting conditions and featureless/excessive features in the surroundings make localisation difficult. Furthermore, the fluid interactions between the rotors' downwash and the surfaces of the surroundings create aerodynamic disturbances, which threaten the quadcopter's stability and increase its risk of collision in the restricted confined space, not to mention the longitudinal wind gusts. This paper presents our findings on the characteristics of these aerodynamic disturbances, the Tunnel Effects for quadcopters, in a 1.5m(W) x 1.5m(H) square cross section tunnel through a series of experiments. A semi-autonomous system is proposed with self-stabilisation in the vertical and lateral axes while a pilot provides commands in heading and the longitudinal direction of the tunnel for performing required tasks such as tunnel wall inspections. We propose a cross-sectional localisation scheme using Hough Scan Matching with a simple kinematic Kalman filter for providing reliable vertical and lateral position information. An integral backstepping (IBS) controller is designed and implemented to enable quadcopters to robustly fly in tunnel-like confined environments. The proposed system is tested in simulated tunnel environments and a real railway tunnel with various reference trajectories, and the IBS controller has shown superior tracking performance in comparison with a PID controller despite of the existence of the Tunnel Effects.

There is a growing concern about water scarcity and the associated decline in Australia’s agricultural production. Efficient water use as a natural resource requires more precise and adequate monitoring of crop water use and irrigation scheduling. Therefore, accurate estimations of evapotranspiration (ET) at proper spatial–temporal scales are critical to understand the crop water demand and uptake and to enable optimal irrigation scheduling. Remote sensing (RS)-based ET estimation has been adopted as a method for large-scale applications when the detailed spatial representation of ET is required. This research aimed to estimate instantaneous ET using very-high-resolution (VHR) multispectral and thermal imagery (GSD < 8 cm) collected using a single flight of a UAV over a high-density peach orchard with a discontinuous canopy. The energy balance component estimation was based on the high-resolution mapping of evapotranspiration (HRMET) model. A tree-by-tree ET map was produced using the canopy surface temperature and the leaf area index (LAI) resampled at the corresponding scale via a systematic feature segmentation method based on pure canopy extraction. Results showed a strong linear relationship between the estimated ET and the leaf transpiration (n = 42) measured using a gas exchange sensor, with a coefficient of determination (R2) of 0.89. Daily ET (5.5 mm d−1) derived from the instantaneous ET map was comparable with daily crop ET (6.4 mm d−1) determined by the meteorological approach over the study site. The proposed approach has important implications for mapping tree-by-tree ET over horticultural fields using VHR imagery.

In this paper, we consider the problem of estimating a scalar field using mobile sensor networks equipped with sensors which can measure the value of the field at their instantaneous location. The scalar field to be estimated is assumed to be represented by positive definite radial basis kernels and we use techniques from adaptive control and Lyapunov analysis to prove the stability of the proposed estimation algorithm. The convergence of the estimated parameter values to the true values is guaranteed by planning the motion of the mobile sensors to satisfy persistence-like conditions.

Unmanned aerial vehicles (UAVs) are often used for navigating dangerous terrains, however they are difficult to pilot. Due to complex input-output mapping schemes, limited perception, the complex system dynamics and the need to maintain a safe operation distance, novice pilots experience difficulties in performing safe landings in obstacle filled environments. In this work we propose a shared autonomy approach that assists novice pilots to perform safe landings on one of several elevated platforms at a proficiency equal to or greater than experienced pilots. Our approach consists of two modules, a perceptual module and a policy module. The perceptual module compresses high dimensionality RGB-D images into a latent vector trained with a cross-modal variational auto-encoder. The policy module provides assistive control inputs trained with the reinforcement algorithm TD3. We conduct a user study (n = 33) where participants land a simulated drone with and without the use of the assistant. Despite the goal platform not being known to the assistant, participants of all skill levels were able to outperform experienced participants while assisted in the task.

Novel applications of soft pneumatic actuation in minimally invasive surgery (MIS) are proposed due to its relatively safe robot–environment interactions. Although the inherent compliance of soft robots makes them suitable for surgery, their low force output and complicated system response and behavior may limit their potential as practical MIS instruments. In this paper, three lobster-inspired antagonistic modules are proposed to realize bidirectional translational, bending and rotational motions and variable stiffness in centimeter scale. Their modular design enables flexible combinations of articulated shafts to satisfy end-effector workspace requirements in MIS. Theoretical models are proposed to relate the input pressure, deformation, output force/torque and stiffness, which provide quantitative solutions for independent adjustment on the deformation and stiffness of each module. A series of experimental results show that the proposed modules can deliver sufficient force and torque output for MIS applications, and they can be conveniently assembled into articulated shafts featuring safe actuation, high dexterity, stiffness tuning and reconfigurability.

Objective: The objective of this study is to examine the effectiveness of an accelerometer-based compact system in detecting and quantifying drug-induced parkinsonism (DIP) in patients with schizophrenia. Method: A pilot study controlled clinical trial comprising 6 people with schizophrenia and 11 control subjects was conducted at Alfred Health, Melbourne. Participants had their movements assessed using Barnes Akathisia Rating Scale (BARS), Simpson Angus Scale (SAS) and Movement Disorder Society Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS III) followed by an assessment of gait using three triaxial accelerometers. Results: Median BARS, SAS, MDS-UPDRS III and accelerometer scores were significantly higher for patients with schizophrenia than controls. Accelerometers detected three times more rest tremor than clinical rating scales. Patients with schizophrenia had 70% of their dynamic acceleration at frequencies between 4 and 10 Hz, which is almost twice that observed in the control population (38%). Accelerometer scores were significantly correlated with BARS scores. Conclusion: Accelerometers were able to accurately detect patients with DIP better than some clinical rating scale including the SAS. Further larger-scale studies must be conducted to further demonstrate the accuracy of accelerometers in detecting DIP.

This paper describes a new type of bending module inspired, in part, by the musculoskeletal structure of the lobster leg joint. The bending module proposed combines enhanced torque output, reconfigurability in assembling, safe compliant actuation, and accurate control on its mechanical performance. In this module, antagonistic soft chambers are enveloped by exoskeleton shells, and the bending angle and the stiffness can be independently adjusted by controlling the input pressure in the two chambers. Theoretical models are developed to characterize the relationships between the input pressure, bending angle, and stiffness, and a controller for angle control and stiffness tuning is constructed with experimental validation. The fabricated module can reach the maximum torque output of 109.7 N•mm under 40 kPa and the stiffness range from 40 to 220 N•mm / rad, demonstrating its capacity to fulfill both safe interactions and forceful tasks.