Towards Automated 3D Search Planning for Emergency Response Missions

Abstract

The ability to efficiently plan and execute automated and precise search missions using unmanned aerial vehicles (UAVs) during emergency response situations is imperative. Precise navigation between obstacles and time-efficient searching of 3D structures and buildings are essential for locating survivors and people in need in emergency response missions. In this work we address this challenging problem by proposing a unified search planning framework that automates the process of UAV-based search planning in 3D environments. Specifically, we propose a novel search planning framework which enables automated planning and execution of collision-free search trajectories in 3D by taking into account low-level mission constrains (e.g., the UAV dynamical and sensing model), mission objectives (e.g., the mission execution time and the UAV energy efficiency) and user-defined mission specifications (e.g., the 3D structures to be searched and minimum detection probability constraints). The capabilities and performance of the proposed approach are demonstrated through extensive simulated 3D search scenarios.

Distributed Estimation and Control for Jamming an Aerial Target With Multiple Agents

“…In this figure, the target ground truth trajectory is marked with yellow circles and the estimated trajectory is marked with orange squares. Figure 4(g) shows more clearly the agent positions at time-steps t = [1,3,6,9,12], along with their sensing area which is approximated by regular dodecahedra. As can be observed, the agents position themselves in such a way so that the target is included inside their sensing area, but without causing interference to each other.…”

“…In this scenario, agent 1 (blue), 2 (turquoise), and 3 (green) are initialized at locations [25,63,30], [107,20,30], and [117, 25,30], respectively. The target is distributed according to N (µ 0 , P 0 ), where µ = [55, 30,30,3,3,1] (shown in yellow) and P 0 = diag([2, 2, 2, 0.8, 0.8, 0.8]), and the rest of the system parameters are set as discussed in Sec. 7.1.…”

“…In recent years, the interest in unmanned aerial vehicle (UAV) technologies for various application areas and industry sectors has soared. UAVs or drones, which were initially utilized as an elite high-tech military technology, have nowadays become widely available and have found widespread use in various domains including emergency response [1]- [5] and precision agriculture [6], [7] just to name a few. According to the latest Drone Market Report [8], the global market size for drones is predicted to grow to 42.8B by 2025.…”

Coordinated CRLB-based Control for Tracking Multiple First Responders in 3D Environments

“…where 6 denotes the state of the j th target at time t, which is composed of the target's position (x j t (x), x j t (y), x j t (z)), and velocity ( ẋj t (x), ẋj t (y), ẋj t (z)) components in 3D Cartesian coordinates. The control input u j t ∈ R 3 denotes the applied control force which allows the target to change its direction and speed, and the term ν t is the process noise which models the uncertainty on the target's state, and which is distributed according to a zero mean multi-variate Gaussian distribution with covariance matrix Q, i.e., ν t ∼ N (0, Q).…”

UAV-based Receding Horizon Control for 3D Inspection Planning

“…The interest in unmanned aerial vehicles (UAVs), and their utilization in various applications domains such as security [1]- [4], emergency response [5]- [7], monitoring/searching [8]- [12], air-traffic management [13], [14], and automated infrastructure inspection [15]- [18] has peaked in the last years. The majority of the application domains mentioned above require some form of coverage planning [19], which in general requires the UAV to be able to plan an optimal trajectory for the coverage of a specific set of points or objects inside an area of interest.…”