A key problem in multiagent multitask systems is optimizing conflict-free strategies, especially when task-assignment is coupled with path-planning. Incomplete information exacerbates this complexity, leading to frequent conflicts, such as redundant agents performing the same task. Different from the existing single-type game model, this article introduces a distributed mixed cooperative-noncooperative (DMCN) model that considers nondifferentiable constraints. In order to deal with nondifferentiable task layer constraints, we use approximation operators and splitting schemes to transform the original optimization function into the primal-dual differentiable function. In order to obtain more stable solutions, a distributed approximate gradient descent optimization algorithm and conflict resolution mechanism are proposed, which enhances the convergence of our method. We use Lyapunov theory to verify the exponential convergence of the algorithm in the time range. Simulation and experiments demonstrate the superiority of this method and its applicability in engineering applications.

With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an off-road environment because of its uncertainty and diversity. A single type of sensor finds it hard to satisfy the needs of obstacle detection because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop multi–sensor fusion and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the off-road environments and providing users with a guideline for selecting sensors based on their performance requirements and application environments. State-of-the-art multi-sensor fusion methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.

Permission mechanisms serve as the main measure to protect users privacy and security in Android applications. Modern smartphone operating systems (Android 6.0 and later versions) prompt users to regulate permissions using ask-on-first-use policy. Much research has been done to dynamically regulate permissions depending on user preferences and contexts in modern operation systems. However, all these techniques have limitations-they heavily rely on users' current or historical decisions on granting permissions, ignoring the fact that users are not experts on privacy protection, i.e., whether a permission shall be granted. In this work, we propose a system to automatically recommend runtime-permission to users. The main idea behind is that the application descriptions reflecting functional information can be used to analyze whether a permission is needed by the application. In more details, using description mining, we extract multiple topics and build a topic-permission mapper. Given an application as input, we first decide which topics it belongs to and then recommend the permissions according to the topic-permission mapper. As the output, besides binary recommendation of "allow" or "deny" recommendations, we provide explanations for the recommendations to uncover the reason for users. We implemented our approach in a tool- AutoPer, and evaluated the approach using 28,850 Android applications from Google Play. The experiments show that our approach achieves a fairly good performance with an accuracy of 81.0%, which demonstrates the effectiveness of AutoPer for permission recommendation.

Recommendation service in mobile app markets decently helps users choose their preferred apps. Though a lot of recommendation service models are proposed in recent years, it is still challenging to tackle extreme sparse app data and get a relatively satisfactory recommendation performance. The reason can be concluded as that traditional recommendation models either focus on limited features or stand aside from deep training. In this paper, we propose knowledge-based deep factorization machine (KDFM), a recommendation model inspired by techniques of factorization machine and attentive deep learning, and apply it in the recommendation service for mobile apps. The KDFM aims to make full use of the rich categorical and textual knowledge in the app market for better performance. To achieve this goal, a topical attention representation component, which contains three typical parts (Word2Vec, BiLSTM and Topical Attention), is constructed. Such representation not only avoids the dimension explosion brought by traditional models, but also preserves the textual semantics for better recommendation. Through extensive experiments conducted on a large number of collected app samples, the KDFM achieves better performance compared with state-of-art rating recommendation models in terms of the rating prediction. In addition, the benefits brought by the usage of attention mechanism and topical representation are confirmed through the comparison experiments.

Access control is a significant strategy to protect security and privacy. Due to electronization of health information, medical access control attracts lots of attention in the research community. The existing medical access control approaches mainly focus on doctors' roles and behaviors, such as role-based access control (RBAC) and risk-based access control. However, various therapeutic regimens can also lead to unauthorized access. The current researches do not consider access control authorization in terms of therapeutic regimens. In this work, we present an access control model based on therapeutic regimen. Our model, TRAC, proposes an access strategy by analyzing feasibility of therapeutic regimens. Our experiments show the effectiveness of TRAC in terms of precision. Moreover, our model also contributes to choosing better therapeutic regimens for patients.

We consider the problem in which a supervisor or remote pilot provides a real-time linear velocity reference to a multirotor aerial robot, either through a traditional remote control handset, a modern haptic interface, or semi-autonomous guidance control system. In all such cases, the goal is to servo-control the vehicle's velocity to the set point as quickly and as efficiently as possible. The challenge is to achieve this robustly in the presence of unknown wind disturbances and in situations in which the vehicle moves into global position system (GPS) denied environments (indoors, urban canyons, forests) where estimation of the vehicle's velocity is challenging. These situations include unclutterred environments, poor visibility environments caused by poor lighting, and poorly textured visual environments where laser- and vision-based sensors become unreliable. The approach taken is to develop a coupled nonlinear complementary velocity aided attitude filter that provides estimates of both the inertial and body-fixed frame linear velocities, as well as the attitude of a multirotor aerial vehicle, that functions effectively even when only the inertial measurement unit and barometric sensor measurements are available. When full inertial velocity measurements are available (from GPS, Vicon, or a vision system), the filter additionally estimates the external wind speed. In this paper, we formally present the proposed filter along with experimental results and a comparison of the filter to recent results in the literature and in situations in which inertial reference frame velocities are available intermittently. The proposed filter is computationally simple to implement, easy to calibrate and tune, and provides an excellent base level functionality for modern multirotor aerial robotic systems that will be required to function robustly in a variety of environments.

This paper introduces a novel constraint in the master joystick workspace, termed the dynamic kinesthetic boundary, that aids a pilot to navigate an aerial robotic vehicle through a cluttered environment. The proposed approach exploits spatial cues by projecting the remote environment into a hard boundary in the master device workspace that provides a pilot with a natural representation of approaching obstacles. The approach is distinguished from classical force feedback approaches by allowing normal operation of the vehicle in free flight and only imposing constraints when approaching and interacting with the environment. A key advantage is that contact with the environment constraint is immediately perceptible to a pilot, allowing them to make suitable adjustments to their inputs. Modulation of the velocity reference for the slave robot ensures obstacle avoidance while allowing a vehicle to approach as close as desired to an object, albeit at a slow speed. A comprehensive user study was performed to systematically test the proposed algorithm and comparisons to two existing state-of-the-art approaches are provided to demonstrate the relative performance of the proposed approach.

Force feedback is a powerful sensory cue that can provide a user, remotely teleoperating a vertical takeoff and landing (VTOL) aerial robotic vehicle, with an enhanced perception of the robot motion and interaction with the local environment. The accepted framework for mobile vehicle teleoperation, inspired by decades of research into manipulator teleoperation, maps joystick displacements to vehicle velocity set points and reflects interactive forces from the vehicle to the human operator. This paper proposes a novel admittance configured haptic interface where the force input from the user is used as a velocity set point and the joystick position is servo-controlled to reflect the vehicle velocity. This paper provides a comparative study, both analytic and experimental, of the two interfaces to investigate the performance and perceptual differences in the context of teleoperation of a VTOL aerial robot.

This paper presents a novel intuitive multi-modal force feedback interface for teleoperation of mobile robotic vehicles. Two different force feedback interfaces are considered: a force feedback joystick and a novel force feedback trackball. The joystick considered is based on the admittance user interface developed by the authors in earlier work and is configured to servo velocity of the vehicle. The force feedback trackball is configured to map vehicle velocity directly to trackball velocity, exploiting the effectively infinite workspace of the trackball to overcome the classical challenge of servo controlling a slave with infinite workspace using a master device with finite workspace. A key contribution of the paper is to provide a modeling framework, based on the bond graph formalism, that allows the energy consistent modeling of input from an admittance joystick as reference to an internal velocity regulation loop for the vehicle. Once this is implemented it is straightforward to interconnect multiple input devices, and in particular the trackball device, using standard interconnection rules in bond graphs. Experiments were performed, and the outcomes verify the feasibility and effectiveness of the proposed interface.

This paper considers the question of providing effective feedback of vehicle dynamic forces to a pilot in haptic teleoperation of aerial robots. We claim that the usual state-of-the-art haptic interface, based on research motivated by robotic manipulator slaves and virtual haptic environments, does a poor job of reflecting dynamic forces of a mobile robotic vehicle to the user. This leads us to propose a novel force feedback user interface for mobile robotic vehicles with dynamics. An analysis of the closed-loop force-displacement transfer functions experienced by the master joystick for the classical and the new approach clearly indicate the advantages of the proposed formulation. Both the classical and the proposed approach have been implemented in the teleoperation of a quadrotor vehicle and we present quantitative and cognitive performance data from a user study that corroborates the expected performance advantages.

This paper introduces a novel dynamic kinesthetic boundary to aid a pilot to navigate an aerial robotic vehicle through a cluttered environment. Classical haptic teleoperation interfaces for aerial vehicles utilize force feedback to provide the pilot with a haptic feel of the robot's interaction with an environment. The proposed approach constructs a dynamic kinesthetic boundary on the master device that provides the pilot with hard boundaries in the haptic workspace to indicate approaching obstacles. An advantage of the proposed approach is that when the vehicle is flying free of obstacles, then the haptic feedback of the joystick can be used to provide a more natural feel of the vehicle dynamics. Furthermore, rather than a gradual onset of virtual potential forces that are felt in the classical approaches, a pilot encountering the dynamic kinesthetic boundary is immediately aware of the presence of the obstacle and can act accordingly. The approach is implemented on an admittance haptic joystick to ensure that the haptic boundaries are faithfully rendered. We prove that in the case of perfect velocity tracking, the proposed algorithm will ensure the vehicle never colliding with the environment. Experiments were conducted on a robotic platform and the results provide verification of the novel approach.

This paper describes the theory and practice for a stable haptic teleoperation of a flying vehicle. It extends passivity-based control framework for haptic teleoperation of aerial vehicles in the longest intercontinental setting that presents great challenges. The practicality of the control architecture has been shown in maneuvering and obstacle-avoidance tasks over the internet with the presence of significant time-varying delays and packet losses. Experimental results are presented for teleoperation of a slave quadrotor in Australia from a master station in the Netherlands. The results show that the remote operator is able to safely maneuver the flying vehicle through a structure using haptic feedback of the state of the slave and the perceived obstacles.