Unmanned Aerial Vehicles (UAVs) are being integrated into a wide range of indoor and outdoor applications. In this light, robust and efficient path planning is paramount. An extensive literature review showed that the A* and Rapidly-Exploring Random Tree (RRT) algorithms and their variants are the most promising path planning algorithms candidates for 3D UAV scenarios. These two algorithms are tested in different complexity 3D scenarios consisting of a box and a combination of vertical and horizontal plane obstacles with apertures. The path length and generation time are considered as the performance measures. The A* with a spectrum of resolutions, the standard RRT with different stepsize constraints, RRT without step size constraints and the Multiple RRT (MRRT) with various seeds are implemented and their performance measures compared. Results confirm that all algorithms are able to generate a path in all scenarios for all resolutions, step sizes and seeds considered, respectively. Overall A*'s path length is more optimal and generation time is shorter than RRT projecting A* as a better candidate for online 3D path planning of UAVs.

Advancements in Unmanned Aerial Vehicles (UAVs) design, actuator and sensory systems and control are making such devices financially available to a wide spectrum of users with various demands and expectations. To mitigate with this ever increasing demand robust, efficient and application-specific path planning is important. This paper presents advancements over the A* and the smoothing algorithms presented in, 1 utilising the same test scenarios. Analysis of results in 1 showed a ripple in path length as the resolution changes for all scenarios considered and less than 0.1% path length improvements after certain amount of smoothing iterates. To attenuate the path length ripple, the A* ripple reduction algorithm was developed. Results show a reduction of more than 46% in terms of standard deviation with respect to the original A* algorithm without any increase in the mean path length for all scenarios. Secondly, the smoothing algorithm developed in 1 was improved to stop smoothing based on the rate of smoothing of previous iterates. Results show more than 10 multiple less path smoothing time maintaining a path length reduction especially for simple scenarios. These advancements further portray the discussed path planning algorithms as candidates to the realisation of online 3D UAV path planning.

Unmanned Aerial Vehicles (UAVs) are being integrated into a wide range of military, industrial and commercial applications. Such applications require faultless autonomous systems to coordinate, guide, navigate and control different UAVs of different sizes, designed for different purposes with different capabilities. In this regard, different path planning algorithms were developed to ensure that UAVs are supplied with collision-free path paramount to which are the A* and the RRT algorithms, a graph-based and a samplingbased algorithm respectively. Such algorithms shall ideally operate in real-time to furnish the UAV navigation system with real-time, valid, obstacle-free paths in view of changes in the environment or other external or user-defined restrictions. Owing this need, in this paper a real-time platform to assess the performance of the A* and RRT algorithm with an associated smoothing algorithm was developed and tested using 3, 3D obstacle environment with different complexities. The salient user-defined, system-defined and internal constants were independently considered and their effect on performance assessed. Results showed that the A* outperformed the RRT algorithm in both path length and computational time for all scenarios considered with difference increasing with scenario complexity. But, both algorithms can be utilised if the associated parameters are attentively chosen based on the scenario the UAV will operate as both algorithm reached a 100% success rate for all scenario at specific parameter assignments.

This paper aims to present a comparative analysis of the two most utilized graph-based and sampling-based algorithms and their variants, in view of 3D UAV path planning in complex indoor environment. The findings of this analysis outline the usability of the methods and can assist future UAV path planning designers to select the best algorithm with the best parameter configuration in relation to the specific application. An extensive literature review of graph-based and sampling-based methods and their variants is first presented. The most utilized algorithms which are the A* for graph-based methods and Rapidly-Exploring Random Tree (RRT) for the sampling-based methods, are defined. A set of variants is also developed to mitigate with inherent shortcomings in the standard algorithms. All algorithms are then tested in the same scenarios and analyzed using the same performance measures. The A* algorithm generates shorter paths with respect to the RRT algorithm. The A* algorithm only explores volumes required for path generation while the RRT algorithms explore the space evenly. The A* algorithm exhibits an oscillatory behavior at different resolutions for the same scenario that is attenuated with the novel A* ripple reduction algorithm. The Multiple RRT generated longer unsmoothed paths in shorter planning times but required more smoothing over RRT. This work is the first attempt to compare graph-based and sampling-based algorithms in 3D path planning of UAVs. Furthermore, this work addresses shortcomings in both A* and RRT standard algorithms by developing a novel A* ripple reduction algorithm, a novel RRT variant and a specifically designed smoothing algorithm.

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.