In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or dangerous areas, performing challenging operations, and providing real-time monitoring and modeling of the situation. This article aims to help readers understand the latest progress and trends in this field by synthesizing and organizing papers related to UAV search and rescue. An introduction to the various types and components of UAVs and their importance in SAR operations is settled first. Additionally, we present a comprehensive review of sensor integrations in UAVs for SAR operations, highlighting their roles in target perception, localization, and identification. Furthermore, we elaborate on the various applications of UAVs in SAR, including on-site monitoring and modeling, perception and localization of targets, and SAR operations such as task assignment, path planning, and collision avoidance. We compare different approaches and methodologies used in different studies, assess the strengths and weaknesses of various approaches, and provide insights on addressing the research questions relating to specific UAV operations in SAR. Overall, this article presents a comprehensive overview of the significant role of UAVs in SAR operations. It emphasizes the vital contributions of drones in enhancing mission success rates, augmenting situational awareness, and facilitating efficient and effective SAR activities. Additionally, the article discusses potential avenues for enhancing the performance of UAVs in SAR.

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Flying robots, also known as drones and unmanned aerial vehicles (UAVs), have found numerous applications in civilian domains thanks to their excellent mobility and reduced cost. In this paper, we focus on a scenario of a flying robot monitoring a set of targets, which are assumed to be moving as a group, to which the sparse distribution of the targets is not applicable. In particular, the problem of finding the optimal position for the flying robot such that all the targets can be monitored by the on-board ground facing camera is considered. The studied problem can be formulated as the conventional smallest circle problem if all the targets’ locations are given. Because it may be difficult to obtain the locations in practice, such as in Global Navigation Satellite Systems (GNSS) dined environments, a range-based navigation algorithm based on the sliding mode control method is proposed. This algorithm navigates the flying robot toward the farthest target dynamically, using the estimated robot–target distances from the received signal strength, until the maximum robot–target distance cannot be further reduced. It is light computation and easily implementable, and both features help to improve the energy efficiency of the flying robot because no heavy computation is required and no special sensing device needs to be installed on the flying robot. The presented solution does not directly solve the smallest circle problem. Instead, our proposed method dynamically navigates the flying robot to the center of the group of targets using the extracted distance information only. Simulations in Matlab and Gazebo have been conducted for both stationery and mobile targets to verify the effectiveness of the proposed approach.

The multi-constellation, multi-frequency Global Navigation Satellite System (GNSS) has the potential to empower precise real-time kinematics (RTK) with higher accuracy, availability, continuity, and integrity. However, to enhance the robustness of the nonlinear filter, both the measurement quality and efficiency of parameter estimation need consideration, especially for GNSS challenging or denied environments where outliers and non-Gaussian noise exist. This study proposes a nonlinear Kalman filter with adaptive kernel bandwidth (KBW) based on the maximum correntropy criterion (AMC-KF). The proposed method excavates data features of higher order moments to enhance the robustness against noise. With the wide-lane and ionosphere-free combination, a dual frequency (DF) data-aided ambiguity resolution (AR) method is also derived to improve the measurement quality. The filtering strategy based on the DF data-aided AR method and AMC-KF is applied for multi-GNSS and DF RTK. To evaluate the proposed method, the short baseline test, long baseline test, and triangle network closure test are conducted with DF data from GPS and Galileo. For the short baseline test, the proposed filter strategy could improve the positioning accuracy by more than 30% on E and N components, and 60% on U. The superiority of the proposed adaptive KBW is validated both in efficiency and accuracy. The triangle network closure test shows that the proposed DF data-aided AR method could achieve a success rate of more than 93%. For the long baseline test, the integration of the above methods gains more than 40% positioning accuracy improvement on ENU components. This study shows that the proposed nonlinear strategy could enhance both robustness and accuracy without the assistance of external sensors and is applicable for multi-GNSS and dual-frequency RTK.