An official website of the United States government
This paper presents a novel mission-oriented path planning algorithm for a team of Unmanned Aerial Vehicles (UAVs). In the proposed algorithm, each UAV takes autonomous decisions to find its flight path towards a designated mission area while avoiding collisions to stationary and mobile obstacles. The main distinction with similar algorithms is that the target destination for each UAV is not apriori fixed and the UAVs locate themselves such that they collectively cover a potentially time-varying mission area. One potential application for this algorithm is deploying a team of autonomous drones to collectively cover an evolving forest wildfire and provide virtual reality for firefighters. We formulated the algorithm based on Reinforcement Learning (RL) with a new method to accommodate continuous state space for adjacent locations. To consider a more realistic scenario, we assess the impact of localization errors on the performance of the proposed algorithm. Simulation results show that the success probability for this algorithm is about 80% when the observation error variance is as high as 100 (SNR:-6dB).
more »
« less
This content will become publicly available on September 6, 2026
Unmanned Aerial Vehicles‐Based Mission Reliability in Collaborative, Heterogeneous, and Dynamic Environments
ABSTRACT Unmanned aerial vehicles (UAVs) are revolutionizing a wide range of military and civilian applications. Since mission failures caused by malfunctions of UAVs can incur significant economic losses, modeling and ensuring the reliability of UAV‐based mission systems is a crucial area of research with challenges posed by multiple dependent phases of operations and collaborations among heterogeneous UAVs. The existing reliability models are mostly applicable to single‐UAV or homogeneous multi‐UAV systems. This paper advances the state of the art by proposing a new analytical modeling method to assess the reliability of a multi‐phased mission performed by heterogeneous collaborative UAVs. The proposed method systematically integrates an integral‐based Markov approach with a binary decision diagram‐based combinatorial method, addressing inter‐ and intraphase collaborations as well as phase‐dependent configurations of heterogeneous UAVs for accomplishing different tasks. As demonstrated by a detailed analysis of a two‐phase rescue mission performed by six UAVs, the proposed method has no limitations on UAV's time‐to‐failure and time‐to‐detection distributions. Another contribution is to formulate and solve UAV allocation problems, achieving a balance between mission success probability and total cost. Given the uncertainties inherent in the mission scenario, the random phase duration problem is also examined.
more »
« less
- Award ID(s):
- 2302094
- PAR ID:
- 10634536
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Quality and Reliability Engineering International
- ISSN:
- 0748-8017
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance.more » « less
-
null (Ed.)In this paper an Unmanned Aerial Vehicles (UAVs) - enabled dynamic multi-target tracking and data collection framework is presented. Initially, a holistic reputation model is introduced to evaluate the targets' potential in offloading useful data to the UAVs. Based on this model, and taking into account UAVs and targets tracking and sensing characteristics, a dynamic intelligent matching between the UAVs and the targets is performed. In such a setting, the incentivization of the targets to perform the data offloading is based on an effort-based pricing that the UAVs offer to the targets. The emerging optimization problem towards determining each target's optimal amount of offloaded data and the corresponding effort-based price that the UAV offers to the target, is treated as a Stackelberg game between each target and the associated UAV. The properties of existence, uniqueness and convergence to the Stackelberg Equilibrium are proven. Detailed numerical results are presented highlighting the key operational features and the performance benefits of the proposed framework.more » « less
-
null (Ed.)Unmanned Aerial Vehicles (UAVs) are increasingly used by emergency responders to support search-and-rescue operations, medical supplies delivery, fire surveillance, and many other scenarios. At the same time, researchers are investigating usage scenarios in which UAVs are imbued with a greater level of autonomy to provide automated search, surveillance, and delivery capabilities that far exceed current adoption practices. To address this emergent opportunity, we are developing a configurable, multi-user, multi-UAV system for supporting the use of semi-autonomous UAVs in diverse emergency response missions. We present a requirements-driven approach for creating a software product line (SPL) of highly configurable scenarios based on different missions. We focus on the process for eliciting and modeling a family of related use cases, constructing individual feature models, and activity diagrams for each scenario, and then merging them into an SPL. We show how the SPL will be implemented through leveraging and augmenting existing features in our DroneResponse system. We further present a configuration tool, and demonstrate its ability to generate mission-specific configurations for 20 different use case scenarios.more » « less
-
UAVs are deployed in various applications including disaster search-and-rescue, precision agriculture, law enforcement and first response. As UAV software systems grow more complex, the drawbacks of developing them in low-level languages become more pronounced. For example, the lack of memory safety in C implies poor isolation between the UAV autopilot and other concurrent tasks. As a result, the most crucial aspect of UAV reliability-timely control of the flight-could be adversely impacted by other tasks such as perception or planning. We introduce JCopter, an autopilot framework for UAVs developed in a managed language, i.e., a high-level language with built-in safe memory and timing management. Through detailed simulation as well as flight testing, we demonstrate how JCopter retains the timeliness of C-based autopilots while also providing the reliability of managed languages.more » « less
