Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

̃nones-Grueiro, Marcos Qui; Biswas, Gautam; Ahmed, Ibrahim; Darrah, Timothy; Kulkarni, Chetan S.

doi:10.36001/ijphm.2021.v12i3.2953

Cited by 5 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The torque-load relationship model was derived via polynomial fitting of test data obtained from a publicly available dataset. The aerodynamics, DC motor, and continuous battery models were adapted from previous publications [26,[36][37][38][39] . The battery degradation model came from test data obtained from NASA' s data repository.…”

Section: Data Model Source Referencementioning

confidence: 99%

“…As a result, throttle increase is required to compensate for these effects, thus increasing the battery' s rate of drainage. In previous experiments [26,39,48] , we only considered the degradation of one motor, which resulted in a consistent position error deviation with respect to the direction of flight and the direction of wind. In real life, all of the motors can degrade simultaneously, and this would result in unpredictable trajectory deviations.…”

Section: Degradation Models and Environment Conditionsmentioning

confidence: 99%

See 1 more Smart Citation

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

Darrah¹,

Biswas²,

Frank³

et al. 2022

J Surveill Secur Saf

Self Cite

View full text Add to dashboard Cite

Maintaining safe operations in cyber physical systems is a complex task that must account for system degradation over time, since unexpected failures can result in the loss of life and property. Operational failures may be attributed to component degradation and disturbances in the environment that adversely impact system performance. Components in a CPS typically degrade at different rates, and, therefore, require continual monitoring to avoid unexpected failures. Moreover, the effects of multiple degrading components on system performance may be hard to predict. Developing and maintaining accurate physics-based system models can be expensive. Typically, it is infeasible to run a true system to failure, so researchers and practitioners have resorted to using data-driven techniques to better evaluate the effect of degrading components on overall system performance. However, sufficiently organized datasets of system operation are not readily available; the output of existing simulations is not organized to facilitate the use of data-driven machine learning techniques for prognostics. As a step toward addressing this problem, in this paper, we develop a data management framework and an end-to-end simulation testbed to generate such data. The framework facilitates the development and comparison of various system-level prognostics algorithms. We adopt a standard data-centered design methodology, combined with a model based engineering approach, to create a data management framework that address data integrity problems and facilitates the generation of reproducible results. We present an ontological design methodology centered around assets, processes, and data, and, as a proof of concept, develop an unmanned aerial vehicle (UAV) system operations database that captures operational data for UAVs with multiple degrading components operating in uncertain environments. Aim: The purpose of this work is to provide a systematic approach to data generation, curation, and storage that supports studies in fault management and system-level prognostics for real-world and simulated operations. We use a data-driven simulation-based approach to enable reliable and reproducible studies in system-level prognostics. This is accomplished with a data management methodology that enforces constraints on data types and interfaces, and decouples various parts of the simulation to enable proper links with related metadata. The goal is to provide a framework that facilitates data analysis and the development of data-driven models for prognostics using machine learning methods. We discuss the importance of systematic data management framework to support data generation with a simulation environment that generates operational data. We describe a standard framework for data management in the context of run-to-failure simulations, and develop a database schema and an API in MATLAB® and Python to support system-level prognostics analyses. Methods: A systematic approach to defining a data management framework for the study of prognostics applications is a central piece of this work. A second important contribution is the design of a Monte Carlo simulation environment to generate run to failure data for CPS with multiple degrading components. We adopt a bottom-up approach, starting with requirements and specifications, then move into functionality and constraints. With this framework, we use a Monte-Carlo simulation approach to generate data for developing and testing a variety of system-level prognostics algorithms. Results: We have developed a data management framework that can handle high dimensional and complex data generated from real or simulated systems for the study of prognostics. In this paper, we show the advantages of a well-organized data management framework for tracking high-fidelity data with high confidence for complex, dynamic CPS. Such frameworks impose data logging discipline and facilitate downstream uses for developing and comparing different data-driven monitoring, diagnostics, and system-level prognostics algorithms. Conclusions: In this paper, we demonstrate the design, development, and use of an asset, process, and data management framework for the research to develop prognostics & health management applications. This work helps fill a gap for system-level remaining useful life studies by providing a comprehensive simulation environment that can generate run to failure data, and a data management architecture that addresses the needs for system-level prognostics research. The framework is demonstrated with a Monte-Carlo simulation of a UAV system that operates multiple flights under different environmental conditions and degradation sources. This architecture for data management will enable researchers to conduct more complex experiments for a variety of cyber physical systems applications.

show abstract

Section: Data Model Source Referencementioning

confidence: 99%

Section: Degradation Models and Environment Conditionsmentioning

confidence: 99%

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

Darrah¹,

Biswas²,

Frank³

et al. 2022

J Surveill Secur Saf

Self Cite

View full text Add to dashboard Cite

show abstract

“…making (PDM), which is used to reduce the risk of failure during a vehicle's mission since it takes into account the conditions of the environment and the performance of the components (Balaban, Alonso, & Goebel, 2012). This approach has been used in studies for autonomous decision-making for planetary rovers (Narasimhan et al, 2012) and other types of vehicles, such as unmanned aerial vehicles (UAVs) for path planning (Quiñones-Grueiro, Biswas, Ahmed, Darrah, & Kulkarni, 2021). Decision-making system takes into account the health of the components.…”

Section: Introductionmentioning

confidence: 99%

Battery State-of-Health Aware Path Planning for a Mars Rover

Salinas-Camus,

Kulkarni,

Orchard

2023

PHM_CONF

View full text Add to dashboard Cite

A rover mission consists of visiting waypoints to gather scientific samples based on set requirements. However, rovers face operational uncertainties during the mission, affecting the performance of its electrical and mechanical components and overall mission success. Hence, it is critical to have a decision-making framework that is aware of the health state of the components when planning the path of the vehicle. In particular, battery degradation, and consequently the battery State of Health (SOH), can affect the optimality of decisions made by the autonomous system in the long term. This paper presents a decision-making system that incorporates information on the energy drawn from the battery (based on the velocity of the vehicle), terrain conditions, and model-based prognostic modules to assess impact on the battery state of charge (SoC). The decision-making system was formulated as a Markov Decision Process (MDP) to reach the goal destination by sending commands in a determined amount of time, while maintaining the battery SoC within the policy stated. The MDP problem was programmed using the open-source framework POMDPs.jl, which has a variety of online and offline solvers. To solve the MDP problem online, we used Monte Carlo Tree Search (MCTS). Results from simulations demonstrate the effect that battery degradation and charging plans have on decision-making.

show abstract

“…Despite some built-in redundancies, all the components are required to operate as designed to achieve required performance and avoid safety incidents (Quinones-Grueiro, Biswas, Ahmed, Darrah, & Kulkarni, 2021). Failure of components, faulty sensors, inclement weather, or even errors within the flight control software could result in erratic behavior, mismanagement of systems, or even complete failure and crashes (Quinones-Grueiro et al, 2021;Pesé, Ganesan, & Shin, 2017;Wasicek, Pese, Weimerskirch, Burakova, & Singh, 2017;Bai, ElBatt, Holland, Krishnan, & Sadekar, 2006;ElBatt, Goel, Holland, Krishnan, & Parikh, 2006;Jones, 2002;Waraksa, Fraley, Kiefer, Douglas, & Gilbert, 1988;Diem, 2001;Feser, Mc-Connell, Brandmeier, & Lauerer, 2006). These may lead to damage to the vehicle and nearby property as well as harm to humans in the current or nearby vehicles or on the ground (Bauranov & Rakas, 2019).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Anomaly Detection using Batteries in eVTOL Vehicle Propulsion Test Bed

et al. 2022

View full text Add to dashboard Cite

There is a growing variety of manned and unmanned aerial vehicles that utilize batteries as their primary power source. These vehicles are composed of a large variety of interacting components and sensors that are needed for safe operation and to carry out their respective missions. As their interactions, complexity, and numbers increase, the risk for anomalies such as degradation of components, sensor faults, and erroneous controls also increase. These anomalies pose significant risks for vehicles flying over densely populated areas or conducting critical missions. It is, therefore, crucial to detect and mitigate these anomalies. There exist several approaches for anomaly detection such as traditional rule or threshold-based methods, model-based approaches, supervised machine learning-based methods, and even unsupervised methods to detect different types of abnormal behaviors. These methods have inherent drawbacks such as lack of sensitivity, inability to detect previously unknown faults, not being robust to compromised innetwork information, or requiring sophisticated system models. To this end, we propose BDAV, a Battery-based Diagnosis for Aerial Vehicles. Assuming a vehicle’s battery is nominal, BDAV utilizes a system’s battery as root of trust to diagnose other vehicle subsystems. Simple machine learning models learn physical dependencies between battery measurements and other vehicle operational variables and an unsupervised algorithm to detect and identify anomalies.

show abstract

Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

Cited by 5 publications

References 39 publications

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

Battery State-of-Health Aware Path Planning for a Mars Rover

Unsupervised Anomaly Detection using Batteries in eVTOL Vehicle Propulsion Test Bed

Contact Info

Product

Resources

About