Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Using acoustic sensor technologies to create a more terrain capable unmanned ground vehicle

Unmanned Ground Vehicle’s (UGV) have to cope with the most complex range of dynamic and variable obstacles and therefore need to be highly intelligent in order to cope with navigating in such a cluttered environment. When traversing over different terrains (whether it is a UGV or a commercial manned vehicle) different drive styles and configuration settings need to be selected in order to travel successfully over each terrain type. These settings are usually selected by a human operator in manned systems on what they assume the ground conditions to be, but how can an autonomous UGV ‘sense’ these changes in terrain or ground conditions? This paper will investigate noncontact acoustic sensor technologies and how they can be used to detect different terrain types by listening to the interaction between the wheel and the terrain. The results can then be used to create a terrain classification list for the system so in future missions it can use the sensor technology to identify the terrain type it is trying to traverse, which creating a more autonomous and terrain capable vehicle. The technology would also benefit commercial driver assistive technologie

AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints

We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure