Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs

The goal of Multi-Objective Path Planning (MOPP) is to find Pareto-optimal paths for autonomous agents with respect to several optimization goals like minimizing risk, path length, travel time, or energy consumption. In this work, we formulate a MOPP for Unmanned Aerial Vehicles (UAVs). We utilize a path representation based on Non-Uniform Rational B-Splines (NURBS) and propose a hybrid evolutionary approach combining an Evolution Strategy (ES) with the exact Dijkstra algorithm. Moreover, we compare our approach in a statistical analysis to state-of-the-art exact (Dijkstra's algorithm), gradient-based (L-BFGS-B), and evolutionary (NSGA-II) algorithms with respect to calculation time and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 2D urban path planning scenario based on real-world data exported from OpenStreetMap. The examination's results indicate that our approach is able to find significantly better solutions for the formulated problem than standard Evolutionary Algorithms (EAs). Moreover, the proposed method is able to obtain more diverse sets of trade-off solutions for different objectives than the standard exact approaches. Thus, the method combines the strengths of both approaches

Three-Dimensional Urban Path Planning for Aerial Vehicles Regarding Many Objectives

Planning flight paths for unmanned aerial vehicles in urban areas requires consideration of safety, legal, and economic aspects as well as attention to social factors for gaining public acceptance. To solve this many-objective path planning problem in the three-dimensional space, we propose a hybrid framework combining an exact Dijkstra search and a metaheuristic evolutionary optimization. Given a start and an endpoint, we optimize a path regarding the risk in case of a system failure, the radio signal disturbance between the aerial vehicle and a ground station, the energy consumption, and the noise immission on city residents. The optimization includes constraints for static obstacle collision avoidance and compliance with the minimum flight altitude. The result is a set of smooth and three-dimensional paths that realize different trade-offs between the defined objectives. As an example, we consider an urban transportation application for aerial vehicles in San Francisco. For all tests, we use real-world data from OpenStreetMap. In a statistical evaluation, we test the efficiency of our framework against different state-of-the-art optimizers. Moreover, we extend the framework with two features that allow the user to integrate arbitrary objectives and unknown scenarios into the path planning framework

Multi-objective 3D Path Planning for UAVs in Large-Scale Urban Scenarios

In the context of real-world path planning applications for Unmanned Aerial Vehicles (UAVs), aspects such as handling of multiple objectives (e.g., minimizing risk, path length, travel time, energy consumption, or noise pollution), generation of smooth trajectories in 3D space, and the ability to deal with urban environments have to be taken into account jointly by an optimization algorithm to provide practically feasible solutions. Since the currently available methods do not allow for that, in this paper, we propose a holistic approach for solving a Multi-Objective Path Planning (MOPP) problem for UAVs in a three-dimensional, large-scale urban environment. For the tackled optimization problem, we propose an energy model and a noise model for a UAV, following a smooth 3D path. We utilize a path representation based on 3D Non-Uniform Rational B-Splines (NURBS). As optimizers, we use a conventional version of an Evolution Strategy (ES), two standard Multi-Objective Evolutionary Algorithms (MOEAs) – NSGA2 and MO-CMA-ES, and a gradient-based L-BFGS-B approach. To guide the optimization, we propose hybrid versions of the mentioned algorithms by applying an advanced initialization scheme that is based on the exact bidirectional Dijkstra algorithm. We compare the different algorithms with and without hybrid initialization in a statistical analysis, which considers the number of function evaluations and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 3D urban path planning scenario in New York City, based on real-world data exported from OpenStreetMap. The examination’s results indicate that hybrid initialization is the main factor for the efficient identification of near-optimal solutions

Three-Dimensional Urban Path Planning for Aerial Vehicles Regarding Many Objectives

Planning flight paths for unmanned aerial vehicles in urban areas requires consideration of safety, legal, and economic aspects as well as attention to social factors for gaining public acceptance. To solve this many-objective path planning problem in the three-dimensional space, we propose a hybrid framework combining an exact Dijkstra search and a metaheuristic evolutionary optimization. Given a start and an endpoint, we optimize a path regarding the risk in case of a system failure, the radio signal disturbance between the aerial vehicle and a ground station, the energy consumption, and the noise immission on city residents. The optimization includes constraints for static obstacle collision avoidance and compliance with the minimum flight altitude. The result is a set of smooth and three-dimensional paths that realize different trade-offs between the defined objectives. As an example, we consider an urban transportation application for aerial vehicles in San Francisco. For all tests, we use real-world data from OpenStreetMap. In a statistical evaluation, we test the efficiency of our framework against different state-of-the-art optimizers. Moreover, we extend the framework with two features that allow the user to integrate arbitrary objectives and unknown scenarios into the path planning framework