Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

Abstract

This research compares 3D versus 4D (three spatial dimensions and the time dimension) multi-objective and multi-criteria path-planning for unmanned aerial vehicles in complex dynamic environments. In this study, we empirically analyse the performances of 3D and 4D path planning approaches. Using the empirical data, we show that the 4D approach is superior over the 3D approach especially in complex dynamic environments. The research model consisting of flight objectives and criteria is developed based on interviews with an experienced military UAV pilot and mission planner to establish realism and relevancy in unmanned aerial vehicle flight planning. Furthermore, this study incorporates one of the most comprehensive set of criteria identified during our literature search. The simulation results clearly show that the 4D path planning approach is able to provide solutions in complex dynamic environments in which the 3D approach could not find a solution.Keywords: Unmanned aerial vehicles, UAV, path planning, modelling, simulation, 3D path planning, 4D path planning Vol. 66, No. 6, November 2016, pp. 651-664, DOI : 10.14429/dsj.66 , VOL. 66, NO. 6, NOVEMbER 2016 652 model used to create flight paths in 4D environment. The simulation model is more applicable and realistic compared to other studies examined during the literature review. The study includes the most comprehensive set of requirements including the ones overlooked in a wide range of studies. This study extends existing literature 1,3,20,21,[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] by placing a special importance on aviation rules and utilisation considerations. The model generates suitable paths that address UAV performance limitations, environmental factors, basic aviation rules, flight dynamics, UAV utilisation considerations and user requirements. It helps in online and offline planning of optimal paths based on distance, fuel consumption, or time objectives, while implementing the described flight criteria. To build the simulation model (SM), we first built a conceptual model (CM) to structure the problem 68 . In the simulations, for each objective, various scenarios are created by changing the number of static and dynamic obstacles and target types. In each scenario, the path planning approach found the shortest and least costly flight paths. The path search is performed by A* heuristic algorithm proven to be complete and optimal. Additionally, in the experiments, A* algorithms with different heuristic parameters are compared using various scenarios in static and dynamic environments under different constraints. Since current UAVs have to make predictions and estimations about the possible future locations of mobile objects, the simulations also include path searches in time varying environments represented with a 4D grid. The 4D grid is constructed using a combination of 3D grids. Each 3D grid is a possible configuration of the world space at a specific time. A time-dimensional search space consider...

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“…like doctrines these are not released to the public. The most appropriate tactics may be developed using simulations in dynamic and realistic operational environments [71][72][73][74][75][76] . Furthermore, using UAV simulations, the most suitable sensor configurations 74 can be investigated for better tactics development.…”

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“…A comparison of 3D versus 4D (three spatial dimension and time) path planning for UAVs was presented by Cicibas et al. 31 to establish that the result of 4D path planning is better than 3D in a complex dynamic environment. Target tracking and obstacle avoidance by UAVs in a complex dynamic environment was also discussed by Yao et al.…”

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