A Range-Based Algorithm for Autonomous Navigation of an Aerial Drone to Approach and Follow a Herd of Cattle.

This paper proposes an algorithm that will allow an autonomous aerial drone to approach and follow a steady or moving herd of cattle using only range measurements. The algorithm is also insensitive to the complexity of the herd's movement and the measurement noise. Once arrived at the herd of cattle, the aerial drone can follow it to a desired destination. The primary motivation for the development of this algorithm is to use simple, inexpensive and robust sensing hence range sensors. The algorithm does not depend on the accuracy of the range measurements, rather the rate of change of range measurements. The proposed method is based on sliding mode control which provides robustness. A mathematical analysis, simulations and experimental results with a real aerial drone are presented to demonstrate the effectiveness of the proposed method.

Towards Fully Autonomous UAVs: A Survey.

Unmanned Aerial Vehicles have undergone rapid developments in recent decades. This has made them very popular for various military and civilian applications allowing us to reach places that were previously hard to reach in addition to saving time and lives. A highly desirable direction when developing unmanned aerial vehicles is towards achieving fully autonomous missions and performing their dedicated tasks with minimum human interaction. Thus, this paper provides a survey of some of the recent developments in the field of unmanned aerial vehicles related to safe autonomous navigation, which is a very critical component in the whole system. A great part of this paper focus on advanced methods capable of producing three-dimensional avoidance maneuvers and safe trajectories. Research challenges related to unmanned aerial vehicle development are also highlighted.

Optimal Deployment of Charging Stations for Aerial Surveillance by UAVs with the Assistance of Public Transportation Vehicles.

To overcome the limitation in flight time and enable unmanned aerial vehicles (UAVs) to survey remote sites of interest, this paper investigates an approach involving the collaboration with public transportation vehicles (PTVs) and the deployment of charging stations. In particular, the focus of this paper is on the deployment of charging stations. In this approach, a UAV first travels with some PTVs, and then flies through some charging stations to reach remote sites. While the travel time with PTVs can be estimated by the Monte Carlo method to accommodate various uncertainties, we propose a new coverage model to compute the travel time taken for UAVs to reach the sites. With this model, we formulate the optimal deployment problem with the goal of minimising the average travel time of UAVs from the depot to the sites, which can be regarded as a reflection of the quality of surveillance (QoS) (the shorter the better). We then propose an iterative algorithm to place the charging stations. We show that this algorithm ensures that any movement of a charging station leads to a decrease in the average travel time of UAVs. To demonstrate the effectiveness of the proposed method, we make a comparison with a baseline method. The results show that the proposed model can more accurately estimate the travel time than the most commonly used model, and the proposed algorithm can relocate the charging stations to achieve a lower flight distance than the baseline method.

Human Motion Intent Description Based on Bumpless Switching Mechanism for Rehabilitation Robot.

This paper aims to improve the performance of an electromyography (EMG) decoder based on a switching mechanism in controlling a rehabilitation robot for assisting human-robot cooperation arm movements. For a complex arm movement, the major difficulty of the EMG decoder modeling is to decode EMG signals with high accuracy in real-time. Our recent study presented a switching mechanism for carving up a complex task into simple subtasks and trained different submodels with low nonlinearity. However, it was observed that a "bump" behavior of decoder output (i.e., the discontinuity) occurred during the switching between two submodels. The bumps might cause unexpected impacts on the affected limb and thus potentially injure patients. To improve this undesired transient behavior on decoder outputs, we attempt to maintain the continuity of the outputs during the switching between multiple submodels. A bumpless switching mechanism is proposed by parameterizing submodels with all shared states and applied in the construction of the EMG decoder. Numerical simulation and real-time experiments demonstrated that the bumpless decoder shows high estimation accuracy in both offline and online EMG decoding. Furthermore, the outputs achieved by the proposed bumpless decoder in both testing and verification phases are significantly smoother than the ones obtained by a multimodel decoder without a bumpless switching mechanism. Therefore, the bumpless switching approach can be used to provide a smooth and accurate motion intent prediction from multi-channel EMG signals. Indeed, the method can actually prevent participants from being exposed to the risk of unpredictable loads.

Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary.

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.

Reactive Autonomous Navigation of UAVs for Dynamic Sensing Coverage of Mobile Ground Targets.

This paper addresses a problem of autonomous navigation of unmanned aerial vehicles (UAVs) for the surveillance of multiple moving ground targets. The ground can be flat or uneven. A reactive real-time sliding mode control algorithm is proposed that navigates a team of communicating UAVs, equipped with ground-facing video cameras, towards moving targets to increase some measure of sensing coverage of the targets by the UAVs. Moreover, the Voronoi partitioning technique is adopted to reduce the movement range of the UAVs and decrease the revisit times of the targets. Extensive computer simulations, from the simple case with one UAV and multiple targets to the complex case with multiple UAVs and multiple targets, are conducted to demonstrate the performance of the developed autonomous navigation algorithm. The scenarios where the terrain is uneven are also considered. As shown in the simulation results, although the additional VP technique leads to some extra computation burden, the usage of the VP technique considerably reduces the target revisit time compared to the algorithm without this technique.

Scheduling of a Parcel Delivery System Consisting of an Aerial Drone Interacting with Public Transportation Vehicles.

This paper proposes a novel parcel delivery system which consists of a drone and public transportation vehicles such as trains, trams, etc. This system involves two delivery schemes: drone-direct scheme referring to delivering to a customer by a drone directly and drone-vehicle collaborating scheme referring to delivering a customer based on the collaboration of a drone and public transportation vehicles. The fundamental characteristics including the delivery time, energy consumption and battery recharging are modelled, based on which a time-dependent scheduling problem for a single drone is formulated. It is shown to be NP-complete and a dynamic programming-based exact algorithm is presented. Since its computational complexity is exponential with respect to the number of customers, a sub-optimal algorithm is further developed. This algorithm accounts the time for delivery and recharging, and it first schedules the customer which leads to the earliest return. Its computational complexity is also discussed. Moreover, extensive computer simulations are conducted to demonstrate the scheduling performance of the proposed algorithms and the impacts of several key system parameters are investigated.

Continuous Description of Human 3D Motion Intent Through Switching Mechanism.

Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.

Real-Time Robust and Optimized Control of a 3D Overhead Crane System.

A new and advanced control system for three-dimensional (3D) overhead cranes is proposed in this study using state feedback control in discrete time to deliver high performance trajectory tracking with minimum load swings in high-speed motions. By adopting the independent joint control strategy, a new and simplified model is developed where the overhead crane actuators are used to design the controller, with all the nonlinear equations of motions being viewed as disturbances affecting each actuator. A feedforward control is then designed to tackle these disturbances via computed torque control technique. A new load swing control is designed along with a new motion planning scheme to robustly minimize load swings as well as allowing fast load transportation without violating system's constraints through updating reference trolley accelerations. The stability and performance analysis of the proposed discrete-time control system are demonstrated and validated analytically and practically.

Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring.

This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and determine their locations is developed. Moreover, it is proved that this algorithm is asymptotically optimal in the sense that the ratio of the number of drones required by this algorithm and the minimum number of drones converges to one as the area of the ground region tends to infinity. The proof is based on Kershner's theorem from combinatorial geometry. Illustrative examples and comparisons with other existing methods show the efficiency of the developed algorithm.

Proactive Deployment of Aerial Drones for Coverage over Very Uneven Terrains: A Version of the 3D Art Gallery Problem.

The paper focuses on surveillance and monitoring using aerial drones. The aim is to estimate the minimal number of drones necessary to monitor a given area of a very uneven terrain. The proposed problem may be viewed as a drone version of the 3D Art Gallery Problem. A computationally simple algorithm to calculate an upper estimate of the minimal number of drones together with their locations is developed. Computer simulations are conducted to demonstrate the effectiveness of the proposed method.

Towards the Internet of Flying Robots: A Survey.

The Internet of Flying Robots (IoFR) has received much attention in recent years thanks to the mobility and flexibility of flying robots. Although a lot of research has been done, there is a lack of a comprehensive survey on this topic. This paper analyzes several typical problems in designing IoFR for real applications, including wireless communication support, monitoring targets of interest, serving a wireless sensor network, and collaborating with ground robots. In particular, an overview of the existing publications on the coverage problem, connectivity of flying robots, energy capacity limitation, target searching, path planning, flying robot navigation with collision avoidance, etc., is presented. Beyond the discussion of these available approaches, some shortcomings of them are indicated and some promising future research directions are pointed out.

An Intelligent Robotic Hospital Bed for Safe Transportation of Critical Neurosurgery Patients Along Crowded Hospital Corridors.

We present a novel design of an intelligent robotic hospital bed, named Flexbed, with autonomous navigation ability. The robotic bed is developed for fast and safe transportation of critical neurosurgery patients without changing beds. Flexbed is more efficient and safe during the transportation process comparing to the conventional hospital beds. Flexbed is able to avoid en-route obstacles with an efficient easy-to-implement collision avoidance strategy when an obstacle is nearby and to move towards its destination at maximum speed when there is no threat of collision. We present extensive simulation results of navigation of Flexbed in the crowded hospital corridor environments with moving obstacles. Moreover, results of experiments with Flexbed in the real world scenarios are also presented and discussed.

A sliding mode-based starling-like controller for implantable rotary blood pumps.

Clinically adequate implementation of physiological control of a rotary left ventricular assist device requires a sophisticated technique such as the recently proposed method based on the Frank-Starling mechanism. In this mechanism, the stroke volume of the heart increases in response to an increase in the volume of blood filling the left ventricle at the end of diastole. To emulate this process, changes in pump speed need to automatically regulate pump flow to ensure that the combined output of the left ventricle and pump match the output of the right ventricle across changing cardiovascular states. In this approach, we exploit the linear relationship between estimated mean pump flow (Q ̅ est) and pump flow pulsatility (PIQp) in a tracking control algorithm based on sliding mode control. The immediate response of the controller was assessed using a lumped parameter model of the cardiovascular system (CVS) and pump from which could be extracted both Q ̅ est and PIQp. Two different perturbations from the resting state in the presence of left ventricular failure were tested. The first was blood loss requiring a reduction in pump flow to match the reduced output from the right ventricle and to avoid the complication of ventricular suction. The second was exercise, requiring an increase in pump flow. The sliding mode controller induced the required changes in Qp within approximately five heart beats in the blood loss simulation and eight heart beats in the exercise simulation without clinically significant transients or steady-state errors.

Self biofeedback control of oxygen consumption (Vo2) during cycling exercise: based on its real time estimate.

The aim of this paper is to develop the self biofeedback (SBF) control of oxygen consumption (Vo2) during cycling exercise. The developed system uses an estimator that can predict Vo2 in real time by using the measurements of heart rate (HR), respiratory rate (RespR) and frequency of exercising activity, this terms is known as Exercise Rate (ER). The biofeedback command is given to the exercising subject in terms of the desired action required by the subject to achieve the targeted Vo2 (Vo2target) profile. The desired action is determined by the SBF system based on the current estimates of Vo2 and is communicated to the exercising subject by flashing an indicator on the computer screen. The results obtained in this study demonstrate that the estimator developed for cycling exercise is capable of estimating Vo2 in real time. The developed system is tested on six healthy male subjects. The obtained results show that the SBF system performs well with the average steady state error in terms of Root Mean Square Error (RMSE) of 1 ml/min/Kg during low intensity exercise and with RMSE of 1.6426 ml/min/Kg during high intensity exercise.

Physiological control of implantable rotary blood pumps for heart failure patients.

In general, patient variability and diverse environmental operation makes physiological control of a left ventricular assist device (LVAD) a complex and complicated problem. In this work, we implement a Starling-like controller which adjusts mean pump flow using pump flow pulsatility as the feedback parameter. The linear relationship between mean pump flow and pump flow pulsatility forms the desired flow of the Starling-like controller. A tracking control algorithm based on sliding mode control (SMC) has been implemented. The controller regulates the estimated mean pulsatile flow (Qp) and flow pulsatility (PIQp) generated from a model of the assist device. A lumped parameter model of the cardiovascular system (CVS) was used to test the control strategy. The immediate response of the controller was evaluated by inducing a fall in left ventricle (LV) preload following a reduction in circulating blood volume. The simulation supports the speed and robustness of the proposed strategy.

Modeling aortic valve closure under the action of a ventricular assist device.

The support of a failing heart with pump devices has been an essential element in cardiac health care for several decades. It is therefore important to understand the left ventricular response to the pumping action of these devices when connected to the native heart. Furthermore, monitoring of aortic valve opening and closure is important in avoiding valve stenosis and thrombogenesis during pump support. This paper reports the first steps in simulating the effects of outlet pump pressure on aortic valve closure of the heart assisted by an implantable blood pump. A two-dimensional fluid structure interaction aortic valve model is presented with blood flow in left ventricular chamber using the Arbitrary Lagrangian-Eulerian Finite Element Method formulation to predict the AV closure during outflow of blood from the left ventricle into the left ventricular assist device (LVAD).

Estimation of cardiac output and total peripheral resistance in preterm infants by arterial waveform analysis.

This study investigated whether arterial blood pressure waveform analysis could be useful for estimating left ventricular outflow (LVO) and total peripheral resistance (TPR) in preterm infants. A cohort of 27 infants were studied, with 89 measurements of left ventricular outflow (LVO) using Doppler echocardiography and arterial pressure using catheters, performed in 0, 12, 24 and 36 hours after birth. TPR was computed as mean arterial pressure divided by LVO. The diastolic decay rate (1/τ) was obtained via fitting an exponential function to the last one third of each arterial pulse, with the mean rate computed from 50 pulses selected from each infant. This decay rate was considered to be inversely related to TPR while positively related to LVO. The results of regression analysis have confirmed that the diastolic decay rate had significant positive and negative relationships with LVO and TPR respectively(r = 0.383, P = 0.0002 and r = -0.379, P = 0.0002 respectively). These preliminary results demonstrated the potential utility of arterial pressure waveform analysis for estimating LVO and TPR in preterm infants, but more advanced multi-parameter models may be needed to improve accuracy of the estimation.

Detrended fluctuation analysis of blood pressure in preterm infants with intraventricular hemorrhage.

Very preterm infants are at high risk of death and serious permanent brain damage, as occurs with intraventricular hemorrhage (IVH). Detrended fluctuation analysis (DFA) that quantifies the fractal correlation properties of physiological signals has been proposed as a potential method for clinical risk assessment. This study examined whether DFA of the arterial blood pressure (ABP) signal could derive markers for the identification of preterm infants who developed IVH. ABP data were recorded from a prospective cohort of 30 critically ill preterm infants in the first 1-3 h of life, 10 of which developed IVH. DFA was performed on the beat-to-beat sequences of mean arterial pressure (MAP), systolic blood pressure (SBP) and pulse interval, with short-term exponent (α1, for timescale of 4-15 beats) and long-term exponent (α2, for timescale of 15-50 beats) computed accordingly. The IVH infants were found to have higher short-term scaling exponents of both MAP and SBP (α1 = 1.06 ± 0.18 and 0.98 ± 0.20) compared to the non-IVH infants (α1 = 0.84 ± 0.25 and 0.78 ± 0.25, P = 0.017 and 0.038, respectively). The results have demonstrated that fractal dynamics embedded in the arterial pressure waveform could provide useful information that facilitates early identification of IVH in preterm infants.

Developments in control systems for rotary left ventricular assist devices for heart failure patients: a review.

From the moment of creation to the moment of death, the heart works tirelessly to circulate blood, being a critical organ to sustain life. As a non-stopping pumping machine, it operates continuously to pump blood through our bodies to supply all cells with oxygen and necessary nutrients. When the heart fails, the supplement of blood to the body's organs to meet metabolic demands will deteriorate. The treatment of the participating causes is the ideal approach to treat heart failure (HF). As this often cannot be done effectively, the medical management of HF is a difficult challenge. Implantable rotary blood pumps (IRBPs) have the potential to become a viable long-term treatment option for bridging to heart transplantation or destination therapy. This increases the potential for the patients to leave the hospital and resume normal lives. Control of IRBPs is one of the most important design goals in providing long-term alternative treatment for HF patients. Over the years, many control algorithms including invasive and non-invasive techniques have been developed in the hope of physiologically and adaptively controlling left ventricular assist devices and thus avoiding such undesired pumping states as left ventricular collapse caused by suction. In this paper, we aim to provide a comprehensive review of the developments of control systems and techniques that have been applied to control IRBPs.