A decision-theoretic approach to detection-based target search with a UAV

Gupta, Aayush; Bessonov, Daniel; Li, Patrick

doi:10.1109/iros.2017.8206423

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…A recent research work towards real-world tests shows a UAV motion planner implementation using the SARSOP POMDP solver [23] onboard a small UAV (UAVs with a maximum take-off weight (MTOW) of 13.5 kg [24]). However, the system was only validated using trivial targets and not with humanoid-shaped mannequins [25].…”

Section: Introductionmentioning

confidence: 99%

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

et al. 2021

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Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR.

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Section: Introductionmentioning

confidence: 99%

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

et al. 2021

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“…The task of searching for targets is relevant to many realworld scenarios [2,4,6,7]. Most generally, this problem can be expressed as a Partially Observable Markov Decision Process (POMDP), which models planning under uncertainty.…”

Section: Related Workmentioning

confidence: 99%

“…Autonomous target search in cluttered environments is a challenging problem relevant for a wide range of applications, e.g., finding victims in SaR operations [1,2], monitoring vegetation in precision agriculture [3,4], patrolling military borders [5], and tracking endangered species [6]. With recent technological advances, UAVs are rapidly gaining popularity as a aerial data acquisition tool for this task.…”

Section: Introductionmentioning

confidence: 99%

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Meera

Popović

Millane

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.

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“…State uncertainly are typically handled by Partially Observable MDP (see [14] for a recent reference). POMDP, described in Section 3.2, add some complexity to the MDP problem as the belief into the actual state is probabilistic.…”

Section: Related Workmentioning

confidence: 99%

Context/Resource-Aware Mission Planning Based on BNs and Concurrent MDPs for Autonomous UAVs

Hireche

Dezan

Mocanu

et al. 2018

Sensors

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This paper presents a scalable approach to model uncertainties within a UAV (Unmanned Aerial Vehicle) embedded mission manager. It proposes a concurrent version of BFM models, which are Bayesian Networks built from FMEA (Failure Mode and Effects Analysis) and used by MDPs (Markov Decision Processes). The models can separately handle different applications during the mission; they consider the context of the mission including external constraints (luminosity, climate, etc.), the health of the UAV (Energy, Sensor) as well as the computing resource availability including CPU (Central Processing Unit) load, FPGA (Field Programmable Gate Array) use and timing performances. The proposed solution integrates the constraints into a mission specification by means of FMEA tables in order to facilitate their specifications by non-experts. Decision-making processes are elaborated following a “just enough” quality management by automatically providing adequate implementation of the embedded applications in order to achieve the mission goals, in the context given by the sensors and the on-board monitors. We illustrate the concurrent BFM approach with a case study of a typical tracking UAV mission. This case also considers a FPGA-SoC (FPGA-System on Chip) platform into consideration and demonstrates the benefits to tune the quality of the embedded applications according to the environmental context.

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A decision-theoretic approach to detection-based target search with a UAV

Cited by 16 publications

References 12 publications

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Context/Resource-Aware Mission Planning Based on BNs and Concurrent MDPs for Autonomous UAVs

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