MESMERIC: Machine Learning-Based Trust Management Mechanism for the Internet of Vehicles.

The emerging yet promising paradigm of the Internet of Vehicles (IoV) has recently gained considerable attention from researchers from academia and industry. As an indispensable constituent of the futuristic smart cities, the underlying essence of the IoV is to facilitate vehicles to exchange safety-critical information with the other vehicles in their neighborhood, vulnerable pedestrians, supporting infrastructure, and the backbone network via vehicle-to-everything communication in a bid to enhance the road safety by mitigating the unwarranted road accidents via ensuring safer navigation together with guaranteeing the intelligent traffic flows. This requires that the safety-critical messages exchanged within an IoV network and the vehicles that disseminate the same are highly reliable (i.e., trustworthy); otherwise, the entire IoV network could be jeopardized. A state-of-the-art trust-based mechanism is, therefore, highly imperative for identifying and removing malicious vehicles from an IoV network. Accordingly, in this paper, a machine learning-based trust management mechanism, MESMERIC, has been proposed that takes into account the notions of direct trust (encompassing the trust attributes of interaction success rate, similarity, familiarity, and reward and punishment), indirect trust (involving confidence of a particular trustor on the neighboring nodes of a trustee, and the direct trust between the said neighboring nodes and the trustee), and context (comprising vehicle types and operating scenarios) in order to not only ascertain the trust of vehicles in an IoV network but to segregate the trustworthy vehicles from the untrustworthy ones by means of an optimal decision boundary. A comprehensive evaluation of the envisaged trust management mechanism has been carried out which demonstrates that it outperforms other state-of-the-art trust management mechanisms.

Unified Accurate Attitude Control for Dual-Tiltrotor UAV with Cyclic Pitch Using Actuator Dynamics Compensated LADRC.

This paper proposes a unified attitude controller based on the modified linear active disturbance rejection control (LADRC) for a dual-tiltrotor unmanned aerial vehicle (UAV) with cyclic pitch to achieve accurate attitude control despite its nonlinear and time-varying characteristics during flight mode transitions. The proposed control algorithm has higher robustness against model mismatch compared with the model-based control algorithms. The modified LADRC utilizes the state feedbacks from the onboard sensors like IMU and Pitot tube instead of the mathematical model of the plane. It has less dependency on the accurate dynamics model of the dual-tiltrotor UAV, which can hardly be built. In contrast to the original LADRC, an actuator model is integrated into the modified LADRC to compensate for the non-negligible slow rotor flapping dynamics and servo dynamics. This modification eliminates the oscillation of the original LADRC when applied on the plant with slow-response actuators, such as propeller and rotors of the helicopter. In this way, the stability and performance of the controller are improved. The controller replaces the gain-scheduling or the control logic switching by a unified controller structure, which simplifies the design approach of the controller for different flight modes. The effectiveness of the modified LADRC and the performance of the unified attitude controller are demonstrated in both simulation and flight tests using a dual-tiltrotor UAV. The attitude control error is less than ±4° during the conversion flight. The control rising time in different flight modes is all about 0.5 s, despite the variations in the airspeed and tilt angle. The flight results show that the controller guarantees high control accuracy and uniform control quality in different flight modes.

MNNMs Integrated Control for UAV Autonomous Tracking Randomly Moving Target Based on Learning Method.

In this paper, we investigate the problem of unmanned aerial vehicles (UAVs) autonomous tracking moving target with only an airborne camera sensor. We proposed a novel integrated controller framework for this problem based on multi-neural-network modules (MNNMs). In this framework, two neural networks are designed for target perception and guidance control, respectively. The deep learning method and reinforcement learning method are applied to train the integrated controller. The training result demonstrates that the integrated controller can be trained more quickly and efficiently than the end-to-end controller trained by the deep reinforcement learning method. The flight tests with the integrated controller are implemented in simulated and realistic environments, the results show that the integrated controller trained in simulation can easily be transferred to the realistic environment and achieve the UAV tracking randomly moving target, which has a faster motion velocity. The integrated controller based on the MNNMs structure has a better performance on an autonomous tracking target than the control mode that combines with a perception network and a proportional integral derivative controller.

A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints.

In this paper, a multi-robot persistent coverage of the region of interest is considered, where persistent coverage and cooperative coverage are addressed simultaneously. Previous works have mainly concentrated on the paths that allow for repeated coverage, but ignored the coverage period requirements of each sub-region. In contrast, this paper presents a combinatorial approach for path planning, which aims to cover mission domains with different task periods while guaranteeing both obstacle avoidance and minimizing the number of robots used. The algorithm first deploys the sensors in the region to satisfy coverage requirements with minimum cost. Then it solves the travelling salesman problem to obtain the frame of the closed path. Finally, the approach partitions the closed path into the fewest segments under the coverage period constraints, and it generates the closed route for each robot on the basis of portioned segments of the closed path. Therefore, each robot can circumnavigate one closed route to cover the different task areas completely and persistently. The numerical simulations show that the proposed approach is feasible to implement the cooperative coverage in consideration of obstacles and coverage period constraints, and the number of robots used is also minimized.

A Bio-Inspired Decision-Making Method of UAV Swarm for Attack-Defense Confrontation via Multi-Agent Reinforcement Learning.

The unmanned aerial vehicle (UAV) swarm is regarded as having a significant role in modern warfare. The demand for UAV swarms with the capability of attack-defense confrontation is urgent. The existing decision-making methods of UAV swarm confrontation, such as multi-agent reinforcement learning (MARL), suffer from an exponential increase in training time as the size of the swarm increases. Inspired by group hunting behavior in nature, this paper presents a new bio-inspired decision-making method for UAV swarms for attack-defense confrontation via MARL. Firstly, a UAV swarm decision-making framework for confrontation based on grouping mechanisms is established. Secondly, a bio-inspired action space is designed, and a dense reward is added to the reward function to accelerate the convergence speed of training. Finally, numerical experiments are conducted to evaluate the performance of our method. The experiment results show that the proposed method can be applied to a swarm of 12 UAVs, and when the maximum acceleration of the enemy UAV is within 2.5 times ours, the swarm can well intercept the enemy, and the success rate is above 91%.

Optimizing the Working Space for Single-Port Laparoscopic Totally Extraperitoneal Closure of Inguinal Hernia with TPV Protocol in Infants and Young Children.

Purpose: To evaluate the outcomes of single-port laparoscopic internal ring closure of inguinal hernia, optimized according to TPV (tilt, pad, and void) protocol, in infants and young children. Methods: From August 2018 to March 2021, a prospective cohort study was conducted including 400 patients younger than 3 years with either left- or right-side inguinal hernia treated with single-port laparoscopic totally extraperitoneal (TEP) closure of the internal ring using a two-hooked core needle apparatus. Patients whose hospitalization ID ended with an odd number were included in group A (n = 200). They were surgically treated with single-port laparoscopy optimized according to TPV protocol, in which the operating table was placed at a 30° head-down tilt position (tilt), the hip was padded by 4-5 cm (pad), and the bladder was voided (void). The remaining patients in group B (n = 200) were conventionally treated with single-port laparoscopic TEP closure of the internal ring. Success rate of surgery, surgery duration, and postoperative complications of two groups were compared. Results: A significantly higher success rate of surgery was detected in group A than in group B (198/200 versus 182/200, P < .05). Regardless of unilateral or bilateral inguinal hernia, surgery duration was significantly shorter in group A than in group B (unilateral inguinal hernia, 14.38 ± 2.85 minutes versus 21.17 ± 4.47 minutes; bilateral inguinal hernia, 20.73 ± 4.58 minutes versus 28.99 ± 4.12 minutes, both P < .05). In addition, the incidence of postoperative complications was significantly lower in group A (1/200 versus 8/200). Conclusions: TPV protocol to optimize working space for single-port laparoscopic TEP closure of inguinal hernia can increase the success rate, shorten surgery duration, and decrease the incidence of postoperative complications.

Equivalence of LADRC and INDI controllers for improvement of LADRC in practical applications.

This paper explores the equivalence between the linear active disturbance rejection control (LADRC) and incremental nonlinear dynamic inversion (INDI) controllers. The equivalence is verified using an n-order, single-input-single-output, perturbed, pure integration plant which represents a class of feedback linearizable systems. And the linear extended state observer (LESO) inside the LADRC is interpreted from a filter perspective, which shows that the core of the LESO is a low-pass filter. A better parameter tuning method is proposed for the LESO. Then, based on the equivalence, an actuator model is integrated into the LADRC to improve its performance on the plant with non-negligible actuator dynamics that are ignored by the original LADRC. Through the modification, compared with the original LADRC, the bandwidth of the LESO and the whole controller is extended, so that improved performance can be achieved on the plants with low-bandwidth actuators. The equivalence between two controllers and the effectiveness of the improved LADRC are both demonstrated by experiments conducted on a quadrotor.

Moving Object Detection and Tracking by Event Frame from Neuromorphic Vision Sensors.

Fast movement of objects and illumination changes may lead to a negative effect on camera images for object detection and tracking. Event cameras are neuromorphic vision sensors that capture the vitality of a scene, mitigating data redundancy and latency. This paper proposes a new solution to moving object detection and tracking using an event frame from bio-inspired event cameras. First, an object detection method is designed using a combined event frame and a standard frame in which the detection is performed according to probability and color, respectively. Then, a detection-based object tracking method is proposed using an event frame and an improved kernel correlation filter to reduce missed detection. Further, a distance measurement method is developed using event frame-based tracking and similar triangle theory to enhance the estimation of distance between the object and camera. Experiment results demonstrate the effectiveness of the proposed methods for moving object detection and tracking.

REVIO: Range- and Event-Based Visual-Inertial Odometry for Bio-Inspired Sensors.

Visual-inertial odometry is critical for Unmanned Aerial Vehicles (UAVs) and robotics. However, there are problems of motion drift and motion blur in sharp brightness changes and fast-motion scenes. It may cause the degradation of image quality, which leads to poor location. Event cameras are bio-inspired vision sensors that offer significant advantages in high-dynamic scenes. Leveraging this property, this paper presents a new range and event-based visual-inertial odometry (REVIO). Firstly, we propose an event-based visual-inertial odometry (EVIO) using sliding window nonlinear optimization. Secondly, REVIO is developed on the basis of EVIO, which fuses events and distances to obtain clear event images and improves the accuracy of position estimation by constructing additional range constraints. Finally, the EVIO and REVIO are tested in three experiments-dataset, handheld and flight-to evaluate the localization performance. The error of REVIO can be reduced by nearly 29% compared with EVIO in the handheld experiment and almost 28% compared with VINS-Mono in the flight experiment, which demonstrates the higher accuracy of REVIO in some fast-motion and high-dynamic scenes.

Deep Reinforcement Learning-Based End-to-End Control for UAV Dynamic Target Tracking.

Uncertainty of target motion, limited perception ability of onboard cameras, and constrained control have brought new challenges to unmanned aerial vehicle (UAV) dynamic target tracking control. In virtue of the powerful fitting ability and learning ability of the neural network, this paper proposes a new deep reinforcement learning (DRL)-based end-to-end control method for UAV dynamic target tracking. Firstly, a DRL-based framework using onboard camera image is established, which simplifies the traditional modularization paradigm. Secondly, neural network architecture, reward functions, and soft actor-critic (SAC)-based speed command perception algorithm are designed to train the policy network. The output of the policy network is denormalized and directly used as speed control command, which realizes the UAV dynamic target tracking. Finally, the feasibility of the proposed end-to-end control method is demonstrated by numerical simulation. The results show that the proposed DRL-based framework is feasible to simplify the traditional modularization paradigm. The UAV can track the dynamic target with rapidly changing of speed and direction.

Comparison of Laparoscopic Orchiopexy and Traditional Inguinal Incision Orchiopexy for Palpable Undescended Testes in Cryptorchidism.

Background: Laparoscopic orchiopexy (LO) has become a standard procedure for the treatment of nonpalpable undescended testes (UDT). LO for palpable UDT is still controversial. The aim of this study is to explore the method and effect of LO procedure for palpable UDT in children suffering from cryptorchidism. Methods: A retrospective study was performed for LO and traditional inguinal incision orchiopexy (TIO) for palpable UDT. A total of 291 children were enrolled, and they were aged 9-96 months with either left- or right-side palpable inguinal canalicular testes. Patients with testes that were nonpalpable, ectopic, and retractable were excluded. One hundred seventy patients received LO and 121 patients received TIO. Patient age, operative time, and clinical outcomes were reviewed. Independent t-test and Fisher's exact test were performed by SPSS 25.0 software. Results: The mean operative time (30.77 ± 6.02 minutes versus 44.76 ± 6.70 minutes) and postoperative normal activity time (1.25 ± 0.43 days versus 2.48 ± 0.68 days) of LO were significantly shorter than those of TIO group (P < .05). Forty-seven of 49 cases (95.9%) aged <1 year successfully achieved LO. Conclusion: LO is an appropriate choice for palpable UDT, especially in younger children aged <2 years. The success rate of LO decreased with age.