Cooperative Obstacle Avoidance for Multiple UAVs Using Spline_VO Method

A new consensus theory-based method for formation control and obstacle avoidance of UAVs

In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

Unmanned Aerial Vehicles (UAVs) are required to have Autonomous Collision Avoidance System (ACAS) to resolve conflicts, especially when flying in the National Airspace System (NAS). This paper focused on the non-cooperative concept for avoidance, where UAVs face rogue obstacle that does not cooperatively share their flight data, nor they follows the rule of the air. UAVswill rely on its on-board sensor for avoidance, in space and time range that is limited. The limitation make the entire process of sense, detect, and avoid (SDA) mostly not applicable, and sense and avoid (SA) manner is more preferable and safe. A method called SA-VO that extend the use of the known Velocity Obstacle (VO-) method is introduce to handle the problem. The method produce more efficient avoidance in the non-cooperative space than SA manner of avoidance, without fully run the SDA process. Probability of collision map based on ranges of obstacle range of positions and velocity is predefine to replace the detection part of SDA. The method is then tested using simulations of encounters between UAV and an obstacle. The simulations show that SA-VO can be a middle ground between the SDA and SA manner of avoidance.

This manuscript presents an application of the multi-layer control scheme to guide a formation of unmanned aerial vehicles (UAV) in positioning and trajectory tracking missions. In such case, each part of the formation control problem is dealt by an individual layer, which is independent module dealing with a specific part of the navigation problem. These layers are responsible to generate the desired path of the formation, to provide the desired posture of the robots, and to establish the control signal of each robot to reach their desired positions. The formation controller here introduced is able to coordinate the robots to the desired formation, including the possibility of time-varying position and/or shape, while a nonlinear underactuated controller previously proposed is responsible to guide the UAVs to their desired positions. The stability analysis of the closed-loop system is demonstrated in the sense of Lyapunov, resulting that the formation errors are ultimately bounded. In the sequel, a strategy to guide a formation of multiple unmanned aerial vehicles (MUAV) to accomplish positioning and trajectory-tracking tasks is also proposed. Delaunay triangulation is here used to split the platoon of UAVs in N-triangles, which are individually guided by the multi-layer control scheme (MLCS). The advantage of the MLCS in such proposal is the maintenance of the the Jacobian matrix order, regardless of the number of robots in the formation, which reduces the complexity of the control problem. Simulation results demonstrate the ability to accomplish the aforementioned tasks, thus validating the proposal.

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

Unmanned surface vehicle (USV) path planning is a crucial technology for achieving USV autonomous navigation. Under global path planning, dynamic local obstacle avoidance has become the primary focus for safe USV navigation. In this study, a USV autonomous dynamic obstacle avoidance method based on the enhanced velocity obstacle method is proposed in order to achieve path replanning. Through further analysis of obstacles, the obstacle geometric model set in the conventional velocity obstacle method was redefined. A special triangular obstacle geometric model was proposed to reconstruct the velocity obstacle region. The collision time was predicted by fitting the previously gathered data to the detected obstacle’s distance, azimuth, and other relevant data. Then, it is combined with the collision risk to determine when obstacle avoidance should begin and end. In order to ensure safe driving between path points, the international maritime collision avoidance rules (COLREGs) are incorporated to ensure the accuracy of obstacle avoidance. Finally, through numerical simulations of various collision scenarios, it was determined that, under the assumption of ensuring a safe encounter distance, the maximum change rates of USV heading angle are optimized by 17.54%, 58.16%, and 28.63% when crossing, head-on, and overtaking, respectively. The results indicate that, by optimizing the heading angle, the enhanced velocity obstacle method can avoid the risk of ship rollover caused by an excessive heading angle during high-speed movement and achieve more accurate obstacle avoidance action in the event of a safety encounter.

This paper proposes a method of unmanned aerial vehicle (UAV) formation with connecting devices to take advantage of the aerodynamic advantages of the UAV fleet during close range formation flight. By combining the theory of multi-rigid-body system with the traditional six-degree-of-freedom equations of aircraft, a multi-rigid-body dynamics model of the connected UAV formation is established, which can describe the dynamic characteristics of the UAV formation when the formation changes. On this basis, the attitude control system of the UAV formation consisting of UAVs with only one control surface is designed, and the full-state non-linear numerical simulation model of the UAV formation is built in Simulink. The simulation results validate the rationality of the modeling method, and show that the attitude control system designed can effectively deal with the difficulties caused by the coupling of longitudinal and lateral controls and the complex aerodynamic forces of UAV formation.

This paper analyzes the motion capability of multiple fixed-wing unmanned aerial vehicle (UAV) formations by taking speed and heading rate constraints of individual UAVs into account. Unlike most of the existing literature investigating multi-UAV formation flying problems, which mainly focus on planner/controller design in order to achieve desired configurations, this paper aims at providing a quantitative description of the UAV formation maneuverability, which can serve as a metric for evaluating formation trajectory plans. Three common formations, namely perfect rigid formation, semiperfect rigid formation, and flexible formation are considered in this paper. Motion capabilities of these formations are analyzed in terms of turning performance. In particular, the feasible set of the formation heading rate is employed as the evaluation metric, based on which the relationships among these three formations are discussed. It has been proven that perfect rigid formation suffers from a rather limited mobility, while flexible formation has the largest feasible set of the formation heading rate in some situations. An interesting finding of this paper is that flexible formation is not necessarily more agile than semiperfect rigid formation with the same formation width, due to the special stall speed constraint of fixed-wing UAVs. Besides rigorous mathematical derivations, simulations have also been performed to further support the theoretical analyses.

Unmanned Aerial Vehicle (UAV) is a new type of flying work tool with the ability to execute tasks. Under strong electromagnetic interference, communication can be carried out by ultraviolet (UV) .Ultraviolet Communication was a communication mode used 200nm~280nm wavelength ultraviolet light as an information carrier. It has the advantage of all-weather operations, non-line-of-sight (NLOS) communication and strong anti- interference. When performing a mission, the UAV formation usually needs to form a fixed formation during the flight to ensure the stability of the link communication network. Moreover, due to the limited energy carried by the UAV, the energy consumption of UAV network should be reduced as much as possible on the basis of maintaining communication in order to maximize the flight time of UAV. The UAVs form persistent formation that need to know the location information of all the UAVs in the formation. When the GPS is not working properly, a kind of two dimensional (2D) localization algorithm which based on the wireless UV ranging method provides relative location information for the UAV formation. This paper proposes a persistent formation generation algorithm for the location information of UAVs that are not shared, the algorithm enables the UAV formation to generate a stable network topology with the least communication links and low energy consumption.

Unmanned aerial vehicles (UAV) have been widely used in many fields. UAV formation flight can complete more assignments than single UAV. Collision avoidance is an important part of UAV formation flight. In this paper, we put forward a three-dimensional method of dynamic collision avoidance for UAV formation flight based on the theory of velocity obstacle. The collection of all possible accelerations of the UAV can be obtained by the input of UAV. Considering that the relative velocity between the UAV and obstacle must be out of the collision cone, we can obtain the collection of feasible accelerations next moment. Then we choose the acceleration which is able to let the UAV closest to the target next moment in the collection as the real acceleration. The maximum and minimum velocity should also be set up. Each UAV can determine the best acceleration next time for itself. In addition to formation keeping, this paper also presents how to split the formation on the basis of the direction of each UAV. Numerical simulation is used to test the performance of this method at the end of this paper.

This paper addressed a well-documented open problem regarding unmanned aerial vehicles (UAVs) formation. Considering dynamic system features, a novel optimal formation algorithm for UAV system is presented in this paper. The UAV formation optimization problem can be first modeled as a linear quadratic optimization problem based on the analysis of the discrete UAV tracking error dynamic model. Subsequently, in order to achieve the purpose of minimizing the error of the system state including speed and position, a new optimal formation control design method is proposed. Finally, the effectiveness of the algorithm is verified by numerical simulation.

Existing resource-intensive obstacle avoidance techniques are hard to be applied on the small-size Unmanned Aerial Vehicles (UAVs) that have limited sensing and computation capacity. Therefore, it is necessary to develop an obstacle recognition and avoidance scheme which works under resource-constrained environments. To this backdrop, this paper first presents an obstacle recognition model based on monocular vision feature points. Afterwards, an obstacle recognition algorithm is put forward, whose computational complexity is low. Then, an obstacle avoidance method is proposed to regulate the obstacle-avoidance path of a UAV until it arrives its destination. To evaluate the effectiveness of the presented algorithms, we design and implement a simulation platform on Objective Modular Network Testbed in C++ (OMNeT++), and conduct a series of experiments. Experimental results show that the proposed model and algorithms can effectively guide a micro UAV to its destination using only an embedded processor and 0.5kg extra load. Even in poor communication conditions, the UAV can independently avoid obstacles and reach the destination only by acquiring the destination coordinates from the ground station.

The development of hierarchical structures of unmanned aerial vehicles (UAVs) increases the efficiency of unmanned aerial systems. The grouping of UAVs increases the region of recognition or force of assault. Achieving these requirements is possible through a UAV formation. The UAVs in the formation must be controlled and managed by a commander, but the commander cannot control individual UAVs. The UAVs within the formation have assigned specific individual tasks, so is possible to achieve the flight of the formation with minimum collisions between UAVs and maximized equipment utilization. This paper aims to present a method of formation control for multiple UAVs that allows dynamic changes in the constellations of UAVs. The article includes the results of tests and research conducted in real-world conditions involving a formation capable of adapting its configuration. The results are presented as an element of research for the autonomy swarm, which can be controlled by one pilot/operator. The control of a swarm consisting of many UAVs (several hundred) by one person is now a current problem. The article presents a fragment of research work on high-autonomy UAV swarms. Here is presented a field test that focuses on UAV constellation control.

This paper proposes an algorithm for cooperative guidance of an unmanned aerial vehicle (UAV) formation following a set of waypoints. Waypoints are linked by straight lines to form a sequence, which effectively tasks the formation to cooperatively follow a rectilinear path between these points. Unlike in earlier known papers, we propose a Fermat’s spiral rather than a circular arc as a fillet to smooth the trajectory. This is because a Fermat’s spiral has zero curvature at its origin, which helps avoid breaks in curvature observable when using straight lines and circular arcs. To make a UAV formation and to follow the path, we propose a novel guidance vector field that is direction- and magnitude-nonuniform. This is where the novelty of this paper lies, as it proposes the vector field method for following a Fermat’s spiral. Known studies used line-of-sight (LOS) guidance to that end. In that case, the UAV formation shapes itself using consensus-based coordination topology, i.e. the group has no leader. To test the algorithm for cooperative waypoint following when smoothing is provided by fillets, we utilized complete nonlinear models of two fixed-wing UAVs, each equipped with a tuned autopilot. Numerical simulation shows the proposed approach efficient.

With the development of technologies related to unmanned vehicles, including unmanned aerial vehicle and unmanned surface vehicle, unmanned vehicles play a pivotal role in maritime management and cruise activities. Meanwhile, lots of evidence shows that a single unmanned vehicle is not qualified for operation under complex environment, whereas the unmanned aerial vehicle–unmanned surface vehicle formation consists of multiple unmanned aerial vehicles and unmanned surface vehicles, which can enhance the performance of the single unmanned vehicle significantly. However, the cooperative operation performance of the unmanned aerial vehicle–unmanned surface vehicle formation is constrained by its communication capacity. Consequently, a cooperative communication framework is designed for the unmanned aerial vehicle–unmanned surface vehicle formation. A distributed dynamic network topology is constructed for fulfilling effective communication for the unmanned aerial vehicle–unmanned surface vehicle formation based on ad hoc network. Experiments are carried out by employing the built unmanned aerial vehicle–unmanned surface vehicle formation platform. Results verify the superiority of the designed cooperative communication framework for the unmanned aerial vehicle–unmanned surface vehicle formation.