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This paper aims to propose an online local trajectory planner for safe and fast trajectory generation that combines the jerk-limited trajectory (JLT) generation algorithm and the particle swarm optimization (PSO) algorithm. A trajectory switching algorithm is proposed to improve the trajectory tracking performance. The proposed system generates smooth and safe flight trajectories online for quadrotors.

First, the PSO algorithm method can obtain the optimal set of target points near the path points obtained by the global path searching. The JLT generation algorithm generates multiple trajectories from the current position to the target points that conform to the kinetic constraints. Then, the generated multiple trajectories are evaluated to pick the obstacle-free trajectory with the least cost. A trajectory switching strategy is proposed to switch the unmanned aerial vehicle (UAV) to a new trajectory before the UAV reaches the last hovering state of the current trajectory, so that the UAV can fly smoothly and quickly.

The feasibility of the designed system is validated through online flight experiments in indoor environments with obstacles.

The proposed trajectory planning system is integrated into a quadrotor platform. It is easily implementable onboard and computationally efficient.

The proposed local planner for trajectory generation and evaluation combines PSO and JLT generation algorithms. The proposed method can provide a collision-free and continuous trajectory, significantly reducing the required computing resources. The PSO algorithm locally searches for feasible target points near the global waypoint obtained by the global path search. The JLT generation algorithm generates trajectories from the current state toward each point contained by the target point set. The proposed trajectory switching strategy can avoid unnecessary hovering states in flight and ensure a continuous and safe flight trajectory. It is especially suitable for micro quadrotors with a small payload and limited onboard computing power.

The purpose of this paper is to analyze the effect and mechanism of platform incentives on users’ knowledge collaboration performance (KCP) and the configuration leading to high KCP in online knowledge communities (OKCs) in the post-COVID-19 pandemic era from a cross-culture perspective.

A survey method and a standard questionnaire were applied. The data was analyzed using multiple regression and fuzzy set qualitative comparative analysis.

The results indicate that, for both kinds of users, self-enhancement and communication positively affect the KCP. User engagement significantly mediates the relationship between communication and KCP and knowledge absorptive capacity moderates the relationship between user engagement and KCP. In contrast, material incentive positively affects the KCP of Chinese users, while hurting the cross-cultural sample. And the promotion of KCP for cross-cultural samples does not require a higher engagement and knowledge absorptive capacity, while paying more attention to short-term interests, such as communication and self-enhancement.

The study only divides users into Chinese and cross-cultural foreign users, without a distinction between foreign users in different countries. In addition, the research is based on cross-sectional data and failed to try to explore the long-term effects of these incentives from the time dimension.

This study explores the incentive mechanism and configuration of OKC platforms to achieve high KCP for different users from a cross-cultural perspective. It provides new ideas and solutions for precise incentives for users of OKC platforms.