A leveling mechanism for the platform based on booms-constraint control of aerial vehicle

In order to achieve an automatic leveling function for work platforms of aerial vehicles with mixed-booms( MAV) in full elevating domain,an auto-leveling mechanism for the platform is proposed based on a control method of booms-constraint,where mixed-boom structures and elevating characteristics are considered. Three models of constraint strategies include non-constraint model,elevating constraint model and lowering constraint model,which is designed to meet the leveling requirements in full working extent. Through the hydro-mechatronic unified modeling,a virtual prototype model is set up based on the auto-leveling mechanism,and leveling performances of the platform are studied during booms elevating to the maximum working height and extent. Simulation results show that the control method of booms-constraint can realize auto-leveling of the platform under two typical working conditions,meanwhile a leveling deviation appears at the constrained point,but the platform inclination is adjusted in the permissible range. The control method does not only restrict booms' freedom elevating to a certain extent,but also impacts the booms extending to the maximum working range. Experimental results verify that the auto-leveling mechanism based on booms-constraint control is valid and rational,which provides an effective technology approach for development of the platform leveling of MAV.

Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network

In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied.Furthermore,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm.For the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm.The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.

The Innovation and Development of Internet of Vehicles

With the advancements in wireless sensor networks, Internet of Vehicles(IOV) has shown great potential in aiding to ease traffic congestion. In IOV, vehicles can easily exchange information with other vehicles and infrastructures, thus, the development of IOV will greatly improve vehicles safety, promote green information consumption and have a profound impact on many industries. The purpose of this paper is to promote the innovation and development of IOV. Firstly, this paper presents general requirements of IOV such as guidelines, basic principles, and the goal of development. Secondly, we analyze critical applications, crucial support, and business model to promote the industrial development of IOV. Finally, this paper proposes some safeguard measures to further promote the development of IOV.

Design and implementation of path planning algorithm for vehicle parking

Parking is an important and indispensable skill for drivers. With rapid urban development, the automatic parking assistant system is one of the key components in future automobiles. Path planning is always essential for solving parking problems. In this paper, a path planning method is proposed for parking using straight lines and circular curves of different radius without collisions with obstacles. The parking process is divided into two steps in which the vehicle reaches the goal state through the intermediate state from the initial state. The intermediate state will be selected from the intermediate state set with a certain criterion in order to avoid obstacles. Similarly, an appropriate goal state will be selected based on the size of the parking lot. In addition, an automatic parking system is built, which effectively achieves the parking lot perception, path planning and performs parking processes in the environment with obstacles. The result of simulations and experiments demonstrates the feasibility and practicality of the proposed method and the automatic parking system.

Generation and Selection of Driver-Behavior-Based Transferable MotionPrimitives

To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm,based on the driver-behavior-based transferable motion primitives(MPs), a general motion-planning framework for offline generation and online selection of MPs is proposed. Optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, a layered, unequal-weighted MP selection framework is proposed that utilizes a combination of environmental constraints, nonholonomic vehicle constraints,trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of MP types and achieves diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes a unique MP library to achieve online extension of MP sequences. The results show that the proposed motion-planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but can also transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.