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

Hohmann, Nikolas; Bujny, Mariusz; Adamy, Jürgen; Olhofer, Markus

doi:10.1109/cec55065.2022.9870265

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…To design a mission, tasks such as mission clearance, analysis and optimization need to be performed. While most research in mobility-related mission design focuses on optimizing the agent's trajectory [1]- [3], we believe that using declarative, relational models can provide a more holistic and symbolic view of the navigation problem. Our approach aims to improve UAV navigation by providing a system that leverages, among other data sources, crowd-sourced maps to provide insights into the agent's freedom of movement within a legal framework modelled by probabilistic logic.…”

Section: Introductionmentioning

confidence: 99%

Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs

Kohaut,

Flade,

Dhami

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Advanced Air Mobility (AAM) is a growing field that demands a deep understanding of legal, spatial and temporal concepts in navigation. Hence, any implementation of AAM is forced to deal with the inherent uncertainties of humaninhabited spaces. Enabling growth and innovation requires the creation of a system for safe and robust mission design, i.e., the way we formalize intentions and decide their execution as trajectories for the Unmanned Aerial Vehicle (UAV). Although legal frameworks have emerged to govern urban air spaces, their full integration into the decision process of autonomous agents and operators remains an open task. In this work we present ProMis, a system architecture for probabilistic mission design. It links the data available from various static and dynamic data sources with legal text and operator requirements by following principles of formal verification and probabilistic modeling. Hereby, ProMis enables the combination of low-level perception and high-level rules in AAM to infer validity over the UAV's state-space. To this end, we employ Hybrid Probabilistic Logic Programs (HPLP) as a unifying, intermediate representation between perception and action-taking. Furthermore, we present methods to connect ProMis with crowd-sourced map data by generating HPLP atoms that represent spatial relations in a probabilistic fashion. Our claims of the utility and generality of ProMis are supported by experiments on a diverse set of scenarios and a discussion of the computational demands associated with probabilistic missions.

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Section: Introductionmentioning

confidence: 99%

Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs

Kohaut,

Flade,

Dhami

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Evolutionary computation for unmanned aerial vehicle path planning: a survey

Jiang,

Xu,

Zheng

et al. 2024

Artif Intell Rev

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The applications of hybrid approach combining exact method and evolutionary algorithm in combinatorial optimization

Duan

Jiang

Dai

et al. 2023

Journal of Computational Design and Engineering

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Combinatorial optimization problems have very important applications in information technology, transportation, economics, management, network communication, and other fields. Since the problem size in real-scenario application is in large-scale, the demand for real-time and efficient solving approaches increases rapidly. The traditional exact methods guarantee the optimality of the final solution, but these methods can hardly solve the problem in acceptable time due to extremely high computational costs. Heuristic approaches can find feasible solutions in a limited time, while these approaches cannot meet the demand of solution quality. In recent years, hybrid algorithms based on exact methods and heuristic algorithms show outstanding performance in solving large-scale combinatorial optimization problems. The hybridization not only overcomes the shortcomings from single algorithm but also fully utilizes the search ability for population-based approaches as well as the interpretability in exact methods, which promotes the application of combinatorial optimization in real-world problems. This paper reviews existing studies on hybrid algorithms combining exact method and evolutionary computation, summarizes the characteristics of the existing algorithms, and directs the future research.

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

Cited by 8 publications

References 32 publications

Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs

Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs

Evolutionary computation for unmanned aerial vehicle path planning: a survey

The applications of hybrid approach combining exact method and evolutionary algorithm in combinatorial optimization

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