A Utility-Based Path Planning for Safe UAS Operations with a Task-Level Decision-Making Capability

Kaya, Uluhan C.; Dogan, Atilla; Huber, Manfred

doi:10.1109/smc.2019.8914226

Cited by 3 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address risk awareness, Rubio-Hervas et al use the Gaussian process model to introduce a tradeoff between the distance and path integral risk (Rubio-Hervas, Gupta, and Ong 2018) based on prior information. Following a similar route, and serving as the basic risk framework for the work in this paper, (Kaya, Dogan, and Huber 2019), utilized a Multi-variate Gaussian Mixture Model representation to capture risks in a Probabilistic Risk Exposure Map (PREM) and used it to perform risk-aware path planning. This, in an extended form, will form the basis for risk assessment in this work.…”

Section: Prior Work and Backgroundmentioning

confidence: 99%

“…PREMs (Kaya, Dogan, and Huber 2019) capture the spatial and temporal distribution of ground objects relevant to safety considerations as a distributed map used for risk evaluation along the path. PREMs represent the risk distribution in the form of a Multi-variate Gaussian Mixture Model, allowing them to efficiently capture and access risks of different forms and over large operational areas.…”

Section: Probabilistic Risk Exposure Mapsmentioning

confidence: 99%

“…Both prior and online data are then used to generate individual risk maps which are fused using a probabilistic filtering approach to generate a merged risk map that contains all known potential risks collected from every source. Based on the risk present in the final risk map, a modified version of the multi-T-RRT* algorithm (Kaya, Dogan, and Huber 2019) is used to generate the best available low-risk UAS trajectory towards the goal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning Sensor Based Risk Map Augmentation for Risk Aware UAS Operation

Huber

2023

FLAIRS

View full text Add to dashboard Cite

Unmanned Aerial Systems (UAS) have become increasingly popular and have been identified as a good platform for a range of tasks from surveillance and inspection to delivery and maintenance. In many of these applications these systems have to operate in environments that are frequented by people or that contain sensitive infrastructure and in which the operation of UAS thus poses physical risk in terms of damage in case of vehicle failure or psychological or privacy risks which would make their operation less acceptable. To increase the use of these systems it is thus important that they can take into account these risks when determining navigation strategies. While this can sometimes be done based on prior information, such as street and building plans in cities, a priori information is often not complete, making it essential that risk representations can be augmented in real time based on sensor information. This paper presents an approach to risk map augmentation that uses learned risk identification from aerial pictures to fuse additional information with prior data into a dynamically changing risk map that allows effective re-planning of navigation strategies.

show abstract

Section: Prior Work and Backgroundmentioning

confidence: 99%

Section: Probabilistic Risk Exposure Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning Sensor Based Risk Map Augmentation for Risk Aware UAS Operation

Huber

2023

FLAIRS

View full text Add to dashboard Cite

show abstract

“…The number of edges, m, is calculated using (15) where N is the size of an N by N grid (therefore n = N × N ). This paper's FPAA computation complexity estimation builds on previous analysis, [24], [29], and it uses scale factors to account for the analog system parameters of programming time, bias prog , and node voltage measurement read-out times, sf meas .…”

Section: A Complexity Analysismentioning

confidence: 99%

“…Previous Multi-Objective path planning has been accomplished using techniques such as genetic algorithms [10], Pareto fronts [11], A* [12], Multi-Step A* [13], Multi-Objective D* lite [14], Rapidly Exploring Random Tree (RRT) based algorithms [15], [16], Neuromorphic systems [17], and Dijkstra's algorithm [11], [18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Path Planning for Autonomous Robots Using Reconfigurable Analog VLSI

Koziol

2020

IEEE Access

View full text Add to dashboard Cite

This paper presents a Multi-Objective path planning approach using reconfigurable Analog-Very-Large-Scale-Integrated (AVLSI) circuits. It is significant because it is the first example of floating-gate based analog resistive grid circuits used for Multi-Objective path planning. The two path planning objectives are 1) minimizing path length and 2) minimizing path cost. Three hardware experimental results are presented that implement the approach using a Field Programmable Analog Array (FPAA) circuit. First, an example demonstrates a simple proof-of-concept. Second, an example shows how the FPAA solution compares to an entire solution set for a specific Start and Goal path planning problem. Third, an example shows how the FPAA solution compares to two edge-cases. The edge-cases are the two ideals: ideal lowest cost path, and ideal shortest distance path. Based on these foundational proof-of-concept hardware results, larger environment grids than are currently implementable on the FPAA hardware were simulated to predict performance if a custom FPAA application specific integrated circuit (ASIC) was built for this Multi-Objective path planning purpose. Finally, analysis is presented to address this method's computational complexity.

show abstract

Revue Systématique de la Littérature sur le Soutien à la Sécurité des Opérations de Drones

Rakotonarivo

Conversy

Drougard

et al. 2021

32e Conférence Francophone Sur l'Interaction Homme-Machine

View full text Add to dashboard Cite

Dans cet article, nous nous intéressons au soutien à la sécurité des opérations de drones. Il s'agit d'un enjeu primordial pour les exploitants qui doivent se conformer au cadre légal. Nous présentons tout d'abord les systèmes de drones et la réglementation européenne. Ensuite nous décrivons une revue systématique de la littérature sur cette thématique. L'identification des approches principales en résulte : "Voir & Éviter", Interactions Humain-Machine et Facteurs Humains (IHM/FH), intégrité de l'aéronef, analyses de sécurité, planification de trajectoires, géorepérage, cyber-sécurité et gestion du trafic de drones. Nous analysons plus en détail les contributions liées à l'IHM en identifiant les tâches utilisateurs, les recommandations pour la conception d'interactions améliorant la sécurité des opérations de drones et les perspectives de recherche. Enfin, nous discutons des aspects peu couverts dans les articles examinés.

show abstract

A Utility-Based Path Planning for Safe UAS Operations with a Task-Level Decision-Making Capability

Cited by 3 publications

References 7 publications

Learning Sensor Based Risk Map Augmentation for Risk Aware UAS Operation

Learning Sensor Based Risk Map Augmentation for Risk Aware UAS Operation

Multi-Objective Path Planning for Autonomous Robots Using Reconfigurable Analog VLSI

Revue Systématique de la Littérature sur le Soutien à la Sécurité des Opérations de Drones

Contact Info

Product

Resources

About