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

Abstract

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.

Bluetooth Low Energy for Close Detection in Search and Rescue Missions With Robotic Platforms: An Experimental Evaluation

“…Finally, other works focus on autonomous drone motion planning and object finding system under object detection uncertainty for outdoor environments [24].…”

Learning Dynamics and Control of a Stochastic System under Limited Sensing Capabilities

“…POMDPs are used for decision-making on a vastly diverse range of applications. In [13] the navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP), and then a motion policy is discussed once a POMDP is solved. In [14] The intrinsic trade-off between sensor power consumption and the risk of misclassifying a patient's health status is investigated, and the problem is categorized as a POMDP.…”

Desenvolvendo Simulações de Uso de Drones em Cidades Inteligentes

“…O uso de drones e outros robôs em ambientes de simulac ¸ão tem sido amplamente estudado na literatura, onde são encontradas diversas abordagens, como: compreender o entendimento do uso de sensores, operac ¸ões básicas de missões e comunicac ¸ão entre enxames de drone [5]- [9].…”