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Path planning has received more and more attention due to its indispensability in autonomous vehicle (AV). Generally speaking, the stability of AV is not fully considered in path planning, therefore, the planned path may be detrimental for maintaining the stability. And this problem is even more acute for roll stability in complex 3D environment, such as off-road terrain environment. To enhance the roll stability on off-road terrain, a 3D gradient reconstruction-based path planning method (3DGRB-PPM) is presented in this work. The 3DGRB-PPM can keep the roll angle at a low level even in complex 3D environment, thus greatly enhancing the roll stability. The 3DGRB-PPM includes two parts, a gradient reconstruction unit and an adaptive fusion unit. In order to ensure the roll stability of AV in path planning, the gradient reconstruction unit is designed by constructing two potential fields, a joint potential field of gradient and roll angle for enhancing the roll stability and a 3D artificial potential field for reaching the destination. In order to coordinate these two potential fields, an adaptive fusion unit is designed by fuzzy inference rule. The simulation is implemented on the Matlab-Carsim co-simulation platform, and the simulation results show that the path planned by 3DGRB-PPM has good performance with enhanced roll stability. [ABSTRACT FROM AUTHOR]

The use of the unmanned aerial vehicle (UAV) has been regarded as a promising technique in both military and civilian applications. However, due to the lack of relevant laws and regulations, the misuse of illegal drones poses a serious threat to social security. In this paper, we develop a trajectory planner based on particle swarm optimization and a proposed surveillance area importance updating mechanism aimed at deriving three-dimensional (3D) optimal surveillance trajectories for multiple monitoring drones. We also propose a multi-objective fitness function in accordance with energy consumption, flight risk, and surveillance area priority in order to evaluate the trajectories generated by the proposed trajectory planner. Simulation results show that the trajectories generated by the proposed trajectory planner can preferentially visit important areas while obtaining a high fitness value in various practical situations.

In this paper, we study the three-dimensional (3D) path planning for a cellular-connected unmanned aerial vehicle (UAV) to minimize its flying distance from given initial to final locations, while ensuring a target link quality in terms of the expected signal-to-interference-plus-noise ratio (SINR) at the UAV receiver with each of its associated ground base stations (GBSs) during the flight. To exploit the location-dependent and spatially varying channel as well as interference over the 3D space, we propose a new radio map based path planning framework for the UAV. Specifically, we consider the channel gain map of each GBS that provides its large-scale channel gains with uniformly sampled locations on a 3D grid, which are due to static and large-size obstacles (e.g., buildings) and thus assumed to be time-invariant. Based on the channel gain maps of GBSs as well as their loading factors, we then construct an SINR map that depicts the expected SINR levels over the sampled 3D locations. By leveraging the obtained SINR map, we proceed to derive the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. We further propose a grid quantization approach where the grid points in the SINR map are more coarsely sampled by exploiting the spatial channel/interference correlation over neighboring grids. Then, we solve an approximate SPP over the reduced-size SINR map (graph) with reduced complexity. Numerical results show that the proposed solution can effectively minimize the flying distance/time of the UAV subject to its communication quality constraint, and a flexible trade-off between performance and complexity can be achieved by adjusting the grid quantization ratio in the SINR map. Moreover, the proposed solution significantly outperforms various benchmark schemes without fully exploiting the channel/interference spatial distribution in the network. [ABSTRACT FROM AUTHOR]