Real-time 3D UAV Path Planning in Dynamic Environments with Uncertainty

Abstract

The integration of Unmanned Aerial Vehicles (UAVs) is being proposed in a spectrum of applications varying from military to civil. In these applications, UAVs are required to safely navigate in real-time in dynamic and uncertain environments. Uncertainty can be present in both the UAV itself and the environment. Through a literature study, this paper first identifies, quantifies and models different uncertainty sources using bounding shapes. Then, the UAV model, path planner parameters and four scenarios of different complexity are defined. To investigate the effect of uncertainty on path planning performance, uncertainty in obstacle position and orientation and UAV position is varied between 2% and 20% for each uncertainty source first separately and then concurrently. Results show a deterioration in path planning performance with the inclusion of both uncertainty types for all scenarios for both A* and the Rapidly-Exploring Random Tree (RRT) algorithms, especially for RRT. Faster and shorter paths with similar same success rates (>95%) result for the RRT algorithm with respect to the A* algorithm only for simple scenarios. The A* algorithm performs better than the RRT algorithm in complex scenarios.

Optimal ant colony algorithm for UAV airborne LiDAR route planning in densely vegetated areas

“…The route planning in the study area is carried out using the UAV-airborne LiDAR adaptive route planning method proposed in this paper in terms of the first stable iteration time, the total time required for the same number of iterations, the route length, and the vegetation coverage within the sample corresponding to the route nodes. Comparing the results of different route planning, it can be concluded that (1) The route planning method proposed in this paper has a faster iteration speed compared with the classical ant colony algorithm, that is, it takes less time cost to complete a route planning and has a higher optimization capability. And the vegetation coverage of the flight area can be taken into account, and the location nodes with lower vegetation coverage can be selected to complete the route planning.…”

“…At present, for the route planning problem, the research is generally focused on the improvement of classical route planning algorithms, such as the improvement of the A* algorithm, 1 classical ant colony algorithm, 2 , 3 and particle swarm algorithm 4 . In addition, there are also new algorithms inspired by existing algorithms are proposed, such as the gray wolf optimization algorithm, 5 the bat optimization algorithm, 6 and the differential evolution algorithm 7 .…”

Unmanned Aerial Vehicle-Based Automated Path Generation of Rollers for Smart Construction

“…They used the same cell decomposition as previous studies and implemented the GRASP-VND (greedy randomized adaptive search procedure-variable neighborhood descent) algorithm [12]. Zammit et al also conducted a study to identify the cause, define different path scenarios, and verify their performance using A* and RRT algorithms to solve the problem of UAVs having to fly in real time in an environment with high uncertainty [13].…”

Stabilized Platform Attitude Control Based on Deep Reinforcement Learning Using Disturbance Observer-Based

“…In fact, the combination of the perceptual advantage of deep learning and the decision-making ability of reinforcement learning has been applied in various control systems Error! Reference source not found., [11],Error! Reference source not found.Error!…”