An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve

Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

A path planning strategy is developed for the coordinated multiple unmanned vehicles(UAVs).Firstly,an initial path from the projection start position to the target position can be devised by constructing a Voronoi diagram based upon the locations of the threats.Then the initial path is discretized for adjustment and smoothing.At last the desirable paths to the target are obtained by simulating the dynamic chain in a potential field.The result for multiple vehicles is a set of smooth and flyable paths with equal length that reduces exposure to threats.The simulation result shows that this strategy woks well and makes it possible for the simultaneous arrival of UAVs formation at the target.

Trajectory Planning for Coordinated Rendezvous of Unmanned Air Vehicles

Tracking whole-body human pose in physical human–machine interactions is challenging because of highly dimensional human motions and lack of inexpensive, nonintrusive motion sensors in outdoor environment. In this paper, we present a computational scheme to estimate the human whole-body pose with application to bicycle riding using a small set of wearable sensors. The estimation scheme is built on the fusion of gyroscopes, accelerometers, force sensors, and physical rider–bicycle interaction constraints through an extended Kalman filter design. The use of physical rider–bicycle interaction constraints helps not only eliminate the integration drifts of inertial sensor measurements but also reduce the number of the needed wearable sensors for pose estimation. For each set of the upper and the lower limb, only one tri-axial gyroscope is needed to accurately obtain the 3-D pose information. The drift-free, reliable estimation performance is demonstrated through both indoor and outdoor riding experiments.

Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors

To perch on or grasp the objective surface using the suction cup-based manipulator, the precise contact control is commonly required Improper contact angle or insufficient contact force may cause failure To enhance the tolerance to flight control insufficiency, a suction cup that comprises an inner soft cup and an outer firm cup to facilitate its engagement without reducing the adhesion stiffness is investigated The soft cup is adaptable to the angular error induced by the multicopter and the resulting adhesion force can draw the firm cup and correct the angular error between the firm cup and the surface These effects increase the engagement rate and reduce the dependence on precise control The outer firm cup is devoted to providing a large adhesion force and a stiff base for subsequent tasks To reduce the air evacuation time in the firm cup, a novel self-sealing structure is designed Based on the combined cup, we build a multifunctional aerial manipulation system which can execute perching or lateral aerial grasping tasks With the proposed prototype, the comparative flight experiments involving perching on a wall under disturbance and grasping an object are conducted The results demonstrate that our proposed suction cup outperforms the conventional cup

Adaptive Aerial Grasping and Perching With Dual Elasticity Combined Suction Cup

High-speed development of unmanned aerial vehicles extends the applications of robots from the ground to the sky, which has brought great changes to people's social life. However, most commercially available UAVs are not equipped with robotic arms. The UAV with robotic arm has the ability to manipulate surrounding objects, which will greatly expand the working scope of the UAVs. In our research, we designed and completed a practical UAV autonomous grasping system in this experiment. UAV autonomous grasping system consists of five sub-systems, including flight control system, motion capture system, wireless communication system, ground station control system and Single-DOF Gripper System. The main difficulty lies in the coupling of the dynamics between the four-rotor unmanned aerial vehicle and the manipulator on it, mathematical modeling and etc. In addition, the four-rotor UAVs need to generate the appropriate flight path according to the actual pose of the target object in order to ensure the efficiency of the task. Based on this system, we have carried on the preliminary research on flight control and trajectory planning of the UAV system equipped with the manipulator. The results of the research will be helpful to implement and improve the system in the future.

Design and research of UAV autonomous grasping system

We report the development of a human whole-body pose estimation scheme with application to rider-bicycle interactions. The estimation scheme is built on the fusion of measurements of a monocular camera on the bicycle and a set of small wearable gyroscopes attached to the rider's upper- and lower-limb and the trunk. A single feature point is collocated with each wearable gyroscope and also on the segment link where the gyroscope is not attached. An extended Kalman filter is designed to fuse the vision-inertial measurements to obtain accurate whole-body poses. The estimation design also incorporates a set of constraints from human anatomy and the physical rider-bicycle interactions. We demonstrate and compare the performance of the estimation design through multiple subjects riding experiments.

Whole-body pose estimation in physical rider-bicycle interactions with a monocular camera and a set of wearable gyroscopes

Automated manipulation of nanowires or nanotubes would enable the scalable manufacturing of nanodevices for a variety of applications. This paper presents electrophoresis-based motion planning and control of a single nanowire in liquid suspension with a simple, generic set of electrodes. We first present a dynamic model of nanowires motion in dilute suspension with a set of N × N controllable electrodes. Since the motion planning of a nanowire from one position to the target location is NP-hard, we present two heuristic algorithms and then compare these algorithms with the rapidly-exploring random tree (RRT) and A*-based approaches. The results show that the proposed algorithms obtain suboptimal minimum time trajectories. The experimental and numerical results confirm the analytical findings.

Electrophoresis-based motion planning and control of a nanowire in fluid suspension

Pose estimation of human–machine interactions such as bicycling plays an important role to understand and study human motor skills. In this paper, we report the development of a human whole-body pose estimation scheme with application to rider–bicycle interactions. The pose estimation scheme is built on the fusion of measurements of a monocular camera on the bicycle and a set of small wearable gyroscopes attached to the rider’s upperand lower-limbs and the trunk. A single feature point is collocated with each wearable gyroscope and also on the body segment link where the gyroscope is not attached. An extended Kalman filter (EKF) is designed to fuse the visual-inertial measurements to obtain the drift-free whole-body poses. The pose estimation design also incorporates a set of constraints from human anatomy and the physical rider–bicycle interactions. The performance of the estimation design is validated through ten subject riding experiments. The results illustrate that the maximum errors for all joint angle estimations by the proposed scheme are within 3 degs. The pose estimation scheme can be further extended and used in other types of physical human–machine interactions. [DOI: 10.1115/1.4035760]

/pdf/whole-body-pose-estimation-in-physical-rider-bicycle-4mvhf9e1wg.pdf

Whole-body pose estimation in physical rider-bicycle interactions with a monocular camera and wearable gyroscopes

In this paper, a thorough research to the small unmanned aerial vehicle localization technology based on the fusion of optical flow and inertial navigation information has been conducted An experiment platform based on Pixhawk and PX4FLOW is built to test the performance of optical flowAnertial localization system Experiment results shows that this system can work at both indoor and outdoor environment In the indoor environment, the optical flowAnertial positioning system has better accuracy compared with the system using pure optical flow information In the outdoor environment, the results proves that this system is a potential solution for UAV positioning in GPS-denied environment-with an error of 5 meters

A UAV positioning strategy based on optical flow sensor and inertial navigation

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