Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra

Abstract

The trajectory planning for UAVs in the dynamic environments is a challenging task. Many restrictions should be taken into consideration, including dynamic terrain collision, no-fly zone criteria, power and fuel criteria and so on. However, some methods treat dynamic restrictions as static in order to reduce cost and obtain efficient and acceptable paths. To achieve optimal, efficient and acceptable paths, we first use a high-dimension matrix, Extended Hierarchical Graph (EHG), to model the unexplored dynamic grid environment and to convert weather conditions to passable coefficient. The original shortest path planning task is then translated to plan the safest path. Therefore, we propose a new forward-exploration and backward-navigation algorithm, Matrix Alignment Dijkstra (MAD), to pilot UAVs. We use matrix alignment operation to simulate the parallel exploration process of all cells from time t to t+1. This exploration process can be accelerated on a GPU. Finally, we recall the optimal path according to the navigator matrix. In addition, we can achieve UAV path planning from one source cell to multiple target cells within one single run. We validate our method on a real dynamic weather dataset and get competitive results in both efficiency and accuracy. We analyse the performance of MAD on a grid-based benchmark dataset and artificial maze data. Simulated results show MAD is especially helpful when the grid-based environment is large-scale and dynamic.

Rerouting UAVs in Dynamic Environment Based on Optimised Interfered Fluid Dynamical System

“…Inspired by the study from Wang, J. et al 15 , we derived an analytical solution when adverse environmental constraints are integrated into the IFDS framework using a constraint matrix. This matrix represents the current environmental conditions and any unpredictable changes such as rain, congestion, or no-fly zones.…”

“…For instance, the average runtime of the algorithm in Zhang, X. et al's work 13 exceeded a minute, making it unsuitable for real-time applications. In Wang, J. et al's 15 work, the dynamic environment is transformed into a high-dimensional matrix known as the Extended Hierarchical Graph (EHG). Real meteorological data were assigned to each layer in EHG to represent the environment at each time, and the safety possibility coefficients were assigned to individual cells.…”

Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network

“…However, its time complexity and space complexity are relatively high, which is not suitable for complex environments. In order to avoid the sequential bottleneck problem that may occur in Dijkstra's algorithm, Wang et al [26] first used the extended hierarchical graph to model the dynamic grid environment, then used the matrix alignment method to perform parallel exploration in the simulated environment, and finally traced the safe path according to the navigator matrix. This method shows a good performance in a large-scale dynamic grid environment.…”

Autonomous Obstacle Avoidance and Trajectory Planning for Mobile Robot Based on Dual-Loop Trajectory Tracking Control and Improved Artificial Potential Field Method

“…The Dijkstra algorithm has high flexibility and can be modified according to specific needs in practical applications to achieve the expected results [16][17][18][19]. However, this algorithm has high memory consumption, and due to the need to maintain two sets, it requires a lot of additional memory space.…”