Persistent Patrol in Stochastic Environments with Limited Sensors

Search and rescue (SAR) missions comprise search for, and provision of aid to people who are in distress or imminent danger. Providing the best possible input for the planners and search teams, up-to-date information about the terrain is of essential importance because every additional hour needed to search a person decreases probability of success. Therefore, availability of aerial images and updated terrain maps as a basis for planning and monitoring SAR missions in real-time is very important for rescuers. In this paper, we present a system for transmission of high-resolution images from an unmanned aerial vehicle (UAV) to the ground station (GS). We define and calculate data rate and transmission distance requirements between the UAV and GS in a mission scenario. Five tests were designed and carried out to confirm the viability of the proposed system architecture and modules. Test results present throughput measurements for various UAV and GS distances, antenna heights and UAV antenna yaw angles. Experimental results from the series of conducted outdoor tests show that the proposed solution using two pMDDL2450 datalinks at 2.4 GHz and a directional antenna on the receiving side can be used for a real-time transmission of high-resolution images acquired with a camera on a UAV. Achieved throughput at a UAV-GS distance of 5 km was 1.4 MB/s (11.2 Mbps). The limitations and possible improvements of the proposed system as well as future work are also discussed.

As a quite interesting subject, there are an increasing number of researches about UAV (Unmanned Aerial Vehicle) in the literature.These robots are quite suitable to dull, dirty and dangerous missions (e.g., over flights at low altitude, especially at night, and longrunning operations, which exposes the crew to extreme fatigue).Thus, an important application of these vehicles is the search operations involving multiple UAVs.However, despite the huge potential use of the UAVs, cooperative search operations with this kind of flying robots are not yet occurring.This research topic is a multidisciplinary area of study in its beginning.Several issues can be studied related to cooperative UAV for search operations, such as centralized versus decentralized control, path planning for cooperative flies, agent reasoning for UAV tactical planning, safety assessments, reliability in automatic target reconnaissance by cameras, agent coordination mechanisms applied for UAV cooperation and the application itself (e.g., patrolling, remote sensing, search and rescue operations supporting, etc.).This paper proposes a model that combines path planning algorithms, search patterns and multiagent coordination techniques to obtain a cooperative UAV model.Different path planning algorithms were studied aiming to get the most suitable to these kinds of operations, and the conclusions are presented.In addition, official documents of Search and Rescue operations are also studied in order to know the best practices already established for this kind of operations, and, finally, an overview of the coordination multi-agent theory is presented and evaluated to achieve the UAV coordination.The great goal for cooperative UAV is to achieve such performance that the performance of the group overcomes the sum of the individual performances isolatedly.Comparing two cooperative UAVs with two non-cooperative UAVs, simulations of previous work showed an average increase in efficiency by about 100%.Besides, another aspect was simulated and evaluated: the impact of the density of the initial sweep.The computational simulations show a high potential of reducing even more the time of search.Furthermore, it is worth noting that the usefulness of cooperative UAVs is not only the increase in efficiency, but also the reduction of costs and risks for the crew.

The use of unmanned aerial vehicles (UAV) is increasing today. UAVs can be divided into two parts, which are remote controlled and can travel automatically due to a certain battery problem. Recent research has also focused on the development and application of new algorithms to autonomously control these vehicles and determine the shortest flight paths. Together with these researches, UAVs are used in many civil activities such as weather forecasts, environmental studies and traffic control. Three-dimensional (3D) path planning is an important issue for autonomously moving UAVs. The shortest path for Unmanned Aerial Vehicles (UAV) is determined by using two-dimensional (2D) path planning algorithms using the obstacles in the environment, and allows UAVs to perform their environmental tasks as soon as possible. The purpose of this study is to determine the shortest path to the target point and avoiding obstacles for UAVs using the Dijkstra algorithm. It was developed to evaluate the arrival time of the UAVs in the path planning algorithm with the simulation performed in the MATLAB program. In this study, the obstacles were defined for the purpose of the building with different heights and different widths and 2D and 3D models were carried out, assuming that the UAV flies at certain heights. In addition, the flight of the UAVs in the route planning determined in the real applications was carried out and the data such as battery consumption, amount of battery spent, speed, amount of travel were examined.

In the unmanned aerial vehicle (UAV) based localization of slow-moving radio frequency (RF) sources with unknown transmitted strength of signal, such as a person with a cell phone in a search and rescue mission, the UAV navigation errors are significant sources of localization error. Although the use of a global positioning system (GPS) can reduce the UAV’s localization error significantly resulting in more accurate RF source localization, if the GPS signal is lost temporarily or permanently, the accuracy of the UAV-based localization decreases rapidly. In this paper, a simultaneous localization and mapping (SLAM)-inspired approach for simultaneous localization of UAV and RF sources (SLUS) is proposed. The proposed approach solves these two connected problems, i.e. the UAV localization and RF source localization, simultaneously to decrease the error of the UAV position estimation and the error of the RF source localization. In the proposed approach, beside the UAV position prediction, the RF source position prediction is also performed. Then the predicted states are augmented and the augmented predicted state information is corrected using range-ratio and bearing observations, i.e. RF source features, considering the unknown transmitted power. The proposed approach is simulated and the results show that the normal divergence of a target localization and the divergence of the UAV navigation in latitude and longitude channels have been eliminated using this approach. In other words, simultaneous localization of the UAV and RF sources uses the RF sources, as features in the environment, to aid the navigation system. Although the approach is similar to the mapping of RF sources in the environment, the created map would be useless after finding the RF sources. That is why SLUS has been used instead of SLAM. The main contributions of this work are: 1) performing simultaneous localization of a UAV and targets using RF signals, especially in a non-line of sight (NLOS) condition; 2) using difference of signal strength, i.e. differential received strength signal indicator (DRSSI), to eliminate the impact of unknown target signal power; and 3) simultaneous multi-target localization and tracking.

In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78%-100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it finds more survivors and rescues them faster than the other algorithms, with an average of 55%~77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% confidence for a one-month mission time.

Introduction Autonomous unmanned aerial vehicles (UAVs) are considered with increasing interest in a diverse set of civil and commercial applications, such as environmental and natural disaster monitoring, border surveillance, emergency assistance, search and rescue missions, delivery of goods, and construction [208, 132, 362, 265, 434, 110, 280, 163]. Use of single or multiple UAVs as communication relays or aerial base stations for network provisioning in emergency situations and for public safety communications has been of particular interest due to UAVs fast deployment and large coverage capabilities [331, 193, 206, 429]. The deployed aerial platform size and the number of necessary platforms are key factors in determining the on-board communication system in addition to the coverage and capacity requirements [73]. Small-scale multi-rotors are of particular interest in practice due to their ease of deployment, low acquisition and maintenance costs, high maneuverability, and ability to hover. Research and development of small-scale UAVs focused initially on control issues, such as flight stability, maneuverability, and robustness, followed by designing autonomous vehicles capable of waypoint flights with minimal user intervention. Considering the limited flight time and payload capacity of these vehicles and the area sizes the aforementioned applications might span, the interest is shifting more and more toward collaborative multiple UAV systems for efficient and successful missions. The number of UAVs might be from tens to hundreds traveling over tens of meters to kilometers for different applications (see Figure 3.1 for an illustration). Wireless communication is an essential factor not only for network provisioning with UAVs but also for successful deployment of systems consisting of multiple small-scale UAVs. It is highly likely that high-performance links and connectivity in three-dimensional space will be required for several applications (e.g., monitoring certain areas) with data delivery meeting a certain quality of service demands [35].The question arises as to which wireless technology should be employed for aerial networks that consist of air-to-ground and air-to-air links, and data needs to be delivered regardless of significant height and orientation differences. Taking into account the quality of service to be satisfied over diverse links and the high node mobility, it is not yet clear as to whether networking protocols developed for ground networks are readily deployable on UAV networks.

Chapter 21 - Event-driven programming-based path planning and navigation of UAVs around a complex urban environment

U NMANNED Aerial Vehicles (UAVs) hold good promise for autonomously carrying out complex civilian and military operations. However, many of these missions require them to fly at low altitudes, making them vulnerable to collision with both stationary as well as moving obstacles. Hence, it is vital that UAVs are equipped with autonomous capability to sense and avoid collisions, especially for the pop-up threats. When such a threat is sensed and a collision is predictedwithin a short time ahead, theUAV should be able to react and maneuver away quickly so that the collision is avoided. An algorithmwhich can assure such amaneuver is called a “reactive collision avoidance algorithm.” Since the available reaction time in such a scenario is usually is small and UAVs are usually limited by computational resources, such an algorithm should also be computationally very efficient (it should preferably be noniterative). It is also required that whilemaneuvering away, it should notmaneuver toomuch away from the obstacle either. This is both to avoid collision from other nearby obstacles and not to compromise on the overall mission objective. There are various attempts in the literature to develop algorithms for collision avoidance purpose, many of which are inspired from global path planning algorithms. The artificial potential field method is such an approach where the motion of the vehicle is guided under the influence of a potential field. The potential field (which is essentially a cost function) is designed in such a way that obstacles have repulsive fields while the destination has an attractive field. The safe path of the UAV is then found by optimizing the carefully selected cost function. To tune this basic philosophy for reactive collision avoidance, a model predictive control-based algorithm has been proposed in the literature. This algorithm essentially assures path following under safe conditions (i.e. if no collision is predicted in the near future) and invokes the potential field function when new collisions are sensed. However, in potential field based techniques the associated optimization process is typically done in an iterative manner. Because of this they are usually computationally intensive and hence are not suitable for reactive collision avoidance of airborne UAVs in general. A promising algorithm in collision avoidance and global path planning is the philosophy of rapidly-exploring random tree (RRT) [1], which has also been used for reactive collision avoidance. However, there are many concerns about the RRT approach, which can largely be attributed to the random nature of the algorithm. For example, the path predicted by RRT is usually a sting of connected straight lines that does not reflect the path followed by a vehicle with nonholonomic constraints. More important, it is a probabilistic approach and hence there is no guarantee of finding a feasible path within a limited finite time. Other graph search algorithms such as best-first search are also implemented for reactive collision avoidance [2]. However, this is not systematic approach and could result in the algorithm searching far too many nodes under some conditions. Moreover, precomputing motion primitives and saving them in a lookup table is infeasible for UAVs, which are usually resource-limited. An interesting perspective to collision avoidance problem is the minimum effort guidance [3], where an optimal control-based approach has been proposed after applying the collision cone philosophy to detect collisions. This method is computationally nonintensive as a closed form solution has been proposed. Even though this is an interesting idea, by minimizing the lateral acceleration, perhaps it imposes unwanted extra constraint on the problem formulation as reactive collision avoidance problems do not necessarily have to be carried out with minimum lateral acceleration. More important, one can observe that this formulation only assures position guarantee and no constraint is imposed on the velocity vector. Hence, even though it guides the vehicle to a carefully selected target point on the safety boundary (we call it the “aiming point”), it causes the vehicle to maneuver until this point. This can be risky as the vehicle may enter the safety ball before reaching the aiming point. Even though the collision cone based aiming point philosophy is a very good idea, the authors of this Note strongly believe that instead of only position guarantee, rather the velocity vector should be aligned towards the aiming point as soon as a collision is detected (which will automatically lead to position guarantee as well). Towards this objective, two new nonlinear guidance laws are proposed in this Note, which are named as nonlinear geometric guidance (NGG) and differential geometric guidance (DGG). These guidance laws are inspired by the philosophy of “aiming point guidance” (APG) [4], which has been proposed in missile guidance literature. It turns out that the APG is a simplified case of the NGG where the associated since function is replaced by its linear approximation (hence, for a systematic discussion, it is renamed as linear geometric guidance (LGG) in this Note). Both of the guidance algorithms proposed in this Note quickly align the velocity vector of the UAValong the aiming point within a part of the available time-togo, which ensures quick reaction and hence safety of the vehicle. The main feature of this philosophy is that they effect high maneuvering at the beginning, causing the velocity vector of the UAV to align with the aiming point direction quickly and then settling along it. Therefore there is no need to maneuver all the way until the aiming point is reached and hence the chance of the UAV entering into the safety ball is minimized. Using the point of closest approach (PCA) [5], the proposed NGG andDGGalgorithms have also been extended for collision avoidance with moving obstacles in both cooperative as well as ignorant scenarios. Mathematical correlations between the guidance laws have also been established, which show that the NGG and DGG are exactly correlated to each other with appropriate gain selections, while the LGG is an approximation of DGG. A “sphere-tracking algorithm” is also proposed in this Note where the UAV is guided to track the surface of the safety spherewhenever a brief violation of the safety boundary occurs after reaching the aiming point because of the location of the next aiming point (which may include the target in Presented as Paper 2010-8315 at the AIAA Guidance, Navigation and Control, Toronto, 2–5August 2010; received 26May 2010; revision received 4 October 2010; accepted for publication 6 October 2010. Copyright © 2010 by Radhakant Padhi. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0731-5090/11 and $10.00 in correspondence with the CCC. ∗Former Project Assistant, Department of Aerospace Engineering; anushamujumdar87@gmail.com. Associate Professor, Department of Aerospace Engineering; padhi@ aero.iisc.ernet.in. JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS Vol. 34, No. 1, January–February 2011

In response to the challenges faced by unmanned aerial vehicles (UAV) in cluttered environments such as forests, ruins, and pipelines, this study introduces a ground–air amphibious UAV specifically designed for personnel search and rescue in complex environments. By innovatively designing and applying a separation cage structure, the UAV’s capabilities for ground movement and aerial flight have been enhanced, effectively overcoming the limitations of traditional single-mode robots operating in narrow or obstacle-dense areas. This design addresses the occlusion issue of sensing components in traditional caged UAVs while maintaining protection for both the UAV itself and the surrounding environment. Additionally, through the innovative design of an H-shaped quadcopter frame skeleton structure, the UAV has gained the ability to perform steady-state aerial flight while also better adapting to the separation cage structure, achieving a reduced energy consumption and significantly improving its operational capabilities in complex environments. The experimental results demonstrate that the UAV prototype, weighing 1.2 kg with a 1 kg payload capacity, achieves a 40 min maximum endurance under full payload conditions at the endurance speed of 10 m/s while performing real-time object detection. The system reliably executes multimodal operations, including stable takeoff, landing, aerial hovering, directional maneuvering, and terrestrial locomotion with coordinated steering control.

Unmanned Aerial Vehicles (UAVs) can be an important resource when performing Search and Rescue (SAR) operations at sea, as this technology is fairly inexpensive when compared to traditional SAR approaches that use significant human resources and expensive air and naval assets, thus enabling the deployment of several UAVs simultaneously in these missions to perform rescue targets search in maritime environments.In order to maximize the usefulness of these UAVs in such operations, we propose a method which utilizes a state-of-the-art object detection network to perform real-time rescue target detection on-board the UAV, using standard RGB cameras, with minimal human intervention, thus enabling an increased vehicle autonomy and search range. Additionally, since the UAVs only relay the candidate images and locations that contain possible rescue targets, given by the onboard detector, it is possible to have several UAVs working in parallel that report back to a single human operator.We have selected the YOLOv4-tiny detection network, pretrained in the COCO dataset, and retrained it to detect rescue targets at sea. For this purpose some datasets were recorded and annotated to simulate the presence of maritime rescue targets. The proposed approach has been validated on an independent test dataset, showing that it has good detection capabilities and thus providing convincing results regarding the use of UAVs with automatic target detection capabilities in SAR missions.

Search and rescue missions are entering a new era with the advent of small scale unmanned aerial vehicles (UAVs) with communication capabilities and embedded cameras. Yet, delivering high resolution images of the supervised surface to rescuers is time-critical. To help resolving this problem, we study how UAVs can take advantage of their controlled mobility to derive the optimum strategy for data transmission. Driven by real-world aerial experiments with both airplanes and quadrocopters equipped with 802.11n technology, we show that the UAV should not necessarily transmit as soon as a wireless link is established. Instead, it should wait until it reaches a suitable distance to the receiving UAV, only to transmit when the time to move to the new location and transmit is minimal. We then apply the principle of delayed gratification, where the UAV attempts to solve the tradeoff between postponing until it reaches this minimum and the impatience to deliver as much data as soon as possible, before any physical damage on-the-fly may occur. Our empirical-driven simulations demonstrate that the optimal distance of transmission greatly depends on the interplay of actual throughput, data size, UAV cruise speed, and failure rate, and that state-of-the-art UAVs can already benefit from our approach.

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are currently employed in many military roles and a number of civilian applications. Some 32 nations are developing or manufacturing more than 250 models of UAVs and 41 countries operate some 80 types of UAVs (U. S. Department of Defense, 2005). By all accounts utilization of UAVs in military and civilian application is expanding both in the short term and long term. The two basic approaches to implementing unmanned flight, remote control and autonomy, rely predominantly on remote data communication and microprocessor technologies (U. S. Department of Defense, 2005). Advances in these technologies, which have grown exponentially since introduction, have dramatically improved the capabilities of the UAVs to address more complicated tasks. Increasing availability of low-cost computational power will stretch the boundary of what is possible to accomplish with less oversight of human operators, a feature generally called autonomy. In many civil and military flight missions the aircrafts freedom of action and movement is restricted and the path is predefined. Given the path, the control task of the aircraft is to generate the trajectory, i.e. to determine required control manoeuvres to steer the aircraft from one point to another. However, in some flight missions the path is not predefined but should dynamically be determined during the mission, e.g. in military surveillance, search & rescue missions, fire detection, and disaster operations. In this type of scenarios the goal of the UAV is to find the precise location of a searched object in an area of responsibility. Usually some uncertain a priori information about the initial location of the object is available. Since during the search operation this information may be modified due to new reports from other sources or the UAV’s observation, the path of the UAV can not be determined before starting the mission. In this chapter, we try to address this problem and introduce a framework for autonomous and dynamic UAV path planning in search operations. The rest of this chapter is organized as follows: section 2 describes the problem in general and the instance of the problem that we solve, section 3 presents history and related work, section 4 provides an overall description of the proposed solution, section 5 and 6 explains Sequential Monte Carlo (SMC) methods and how we have applied it, in section 7 simulation of a test case scenario and the obtained results are presented, and section 8 summarizes the chapter.

Intelligent robot swarms are increasingly being explored as tools for search and rescue missions. Efficient path planning and robust communication networks are critical elements of completing missions. The focus of this research is to give unmanned aerial vehicles (UAVs) the ability to self-organize a mesh network that is optimized for area coverage. The UAVs will be able to read the communication strength between themselves and all the UAVs it is connected to using RSSI. The UAVs should be able to adjust their positioning closer to other UAVs if RSSI is below a threshold, and they should also maintain communication as a group if they move together along a search path. Our approach was to use Genetic Algorithms in a simulated environment to achieve multi-node exploration with emphasis on connectivity and swarm spread.

Search and rescue (SAR) teams are trained to swiftly act and operate in a feasible way, so as to reduce the risk of death associated with accidents due to natural and manmade disasters, as well as during recreational or sports activities such as swimming, climbing and hiking. In this context, SAR teams test emergent and rapidly evolving technologies to evaluate their usefulness in different operational scenarios and to assess if these technologies can increase their effectiveness. Unmanned Aerial Vehicles (UAVs) are examples of such technologies. UAVs are agile, fast and can exhibit autonomous behavior. Therefore, there is a need to assess their usefulness and learn their limitations aiming at comprehending under which settings and contexts can seamlessly collaborate with SAR teams. In our ongoing research, we have designed and implemented an autonomous UAV platform suitable for rescue and life-saving services, namely the ROLFER (Robotic Lifeguard For Emergency Rescue) platform. With a view to assess its operational behavior, as well as of other systems of similar characteristics, in cases where unwanted and adverse events occur during its operation, a new methodology is being proposed in this paper. The proposed methodology combines, in novel way, existing methods from different domains aiming at identifying the states and conditions under which the combined SAR and UAV system could fail. The methodology aims to support SAR teams in deciding if the UAV is appropriate tool to be used in the context of an emergency.

Energy consumption is a critical parameter in the development of helicopter Unmanned Aerial Vehicles (UAVs). Today, helicopter UAVs are playing an increasingly pivotal role in various applications, from surveillance and reconnaissance to package delivery and search and rescue missions. However, their energy efficiency remains a pressing issue, as it directly impacts their operational duration and payload capacity. One of the key challenges in optimizing energy consumption is the existence of excess power during flight, arising from the intricate interplay between helicopter aerodynamic behavior and safety design. Typically, this excess energy is dissipated, resulting in a suboptimal performance and efficiency. This study investigated the behavior of excess power in a helicopter Unmanned Aerial Vehicle (UAV). Typically, this excess energy is wasted in conventional helicopters and helicopter UAVs. A dual-method approach, encompassing numerical and experimental methodologies, was employed to provide comprehensive insights into the helicopter UAV’s performance under various conditions. Computational fluid dynamics (CFD) simulations were performed to analyze the UAV’s aerodynamics. The simulations were validated by comparing the lift force with wind tunnel experimental data, resulting in acceptable deviations. The experimental analysis was conducted using a wind tunnel and a small-sized helicopter UAV. The experiments were designed to examine the excess power behavior of the UAV under two distinct flight conditions: hover and forward flight. The power output from the generator and power input from the battery were measured under various angular velocities and pitch angles. The results revealed a maximum excess power of 6.84% for hover conditions and 9.83% for forward flight conditions. This indicates that the maximum excess power percentage attributable to the helicopter UAV’s safety measure is 6.84% and that resulting from aerodynamics is 2.99%. The findings of this study contribute valuable knowledge to the optimization of helicopter UAV performance and the potential for harnessing excess power during flight operations. When this excess energy is harnessed, it can contribute significantly to the overall performance and efficiency of the UAV, potentially extending its flight duration or accommodating additional payload capacity that could potentially pave the way for the development of hybrid helicopter UAV models in the future.