UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments

Abstract

Response efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. These efforts have been further improved with advances in autonomous behaviours such as obstacle avoidance, take-off, landing, hovering and waypoint flight modes. However, most UAVs lack autonomous decision making for navigating in complex environments. This limitation creates a reliance on ground control stations to UAVs and, therefore, on their communication systems. The challenge is even more complex in indoor flight operations, where the strength of the Global Navigation Satellite System (GNSS) signals is absent or weak and compromises aircraft behaviour. This paper proposes a UAV framework for autonomous navigation to address uncertainty and partial observability from imperfect sensor readings in cluttered indoor scenarios. The framework design allocates the computing processes onboard the flight controller and companion computer of the UAV, allowing it to explore dangerous indoor areas without the supervision and physical presence of the human operator. The system is illustrated under a Search and Rescue (SAR) scenario to detect and locate victims inside a simulated office building. The navigation problem is modelled as a Partially Observable Markov Decision Process (POMDP) and solved in real time through the Augmented Belief Trees (ABT) algorithm. Data is collected using Hardware in the Loop (HIL) simulations and real flight tests. Experimental results show the robustness of the proposed framework to detect victims at various levels of location uncertainty. The proposed system ensures personal safety by letting the UAV to explore dangerous environments without the intervention of the human operator.

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

“…The procedure to model ∆p u (k) follows the system identification process presented in [27]. The primary workflow is defined as follows:…”

“…Research works on onboard decision-making in small UAVs for autonomous navigation under object detection uncertainty from Convolutional Neural Network (CNN) models are scarce. Some of the closest approaches are the studies from Sandino et al [26,27], which describe a framework and POMDP problem formulation for a SAR application in a global navigation satellite system (GNSS)-denied environment using a sub-2 kg UAV. The framework was tested in simulation and with real flight tests indoors, simulating a collapsed building scenario.…”

Indoor Mapping of Magnetic Fields Using UAV Equipped with Fluxgate Magnetometer

“…The indoor applications of UAVs are usually related to search and rescue applications [ 22 ] involving people or object detection in cluttered indoor environments [ 23 ] or UAV-based emergency monitoring and response systems for indoor hazard monitoring and building evacuation purposes [ 24 ]. They can be used for the location of the causes of emergency situations, for example, gas sources [ 25 ].…”

A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments

“…Research work focusing on multiple UAVs for SAR missions can be broadly divided into two main areas: (i) swarm systems, which consist of a large number of smaller drones with lower quality sensors and a lower level of autonomy [11]- [13], and (ii) cooperative systems, which contain only several larger UAV platforms with more sensors and more powerful on-board processors to enable more complex onboard decisions than smaller drones [2], [14], [15]. There is considerable amount of research on SAR missions in GPS-denied environments with a single UAV [16]- [20]. In contrast, using a team of UAVs to find a target in cluttered and GPS-denied scenarios with efficiency and cooperation is still a challenging problem.…”