UAV Collision Avoidance Research Articles

Recent Developments and Future Prospects in Collision Avoidance Control for Unmanned Aerial Vehicles (UAVS): A Review

The industry has been significantly enhanced by recent developments in UAV collision avoidance systems. They made collision avoidance controllers for self-driving drones both affordable and hazardous. These low-maintenance, portable devices provide continuous monitoring in near-real time. It is inaccurate due to the fact that collision avoidance controllers necessitate trade-offs regarding data reliability. Collision avoidance control research is expanding significantly and is disseminated through publications, initiatives, and grey literature. This paper provides a concise overview of the most recent research on the development of autonomous vehicle collision avoidance systems from 2017 to 2024. In this paper, the state-of-the-art collision avoidance system used in drone systems, the capabilities of the sensors used, and the distinctions between each type of drone are discussed. The pros and cons of current approaches are analyzed using seven metrics: complexity, communication dependency, pre-mission planning, resilience, 3D compatibility, real-time performance, and escape trajectories.

ЗАСТОСУВАННЯ ФІЛЬТРА ЧАСТОЧОК ДО ЗАДАЧ САМОЛОКАЛІЗАЦІЇ ЛИШЕ ЗА ВІДСТАННЮ ДО ДЖЕРЕЛ РАДІОСИГНАЛІВ

This work is aimed at searching for a relatively accurate and computationally efficient algorithm for range-only self-localization. Key requirement to the data sources is cost effectiveness. The radio signal strength indicator (RSSI)-based distance estimation model was chosen, due to wide availability of hardware and cost effectiveness. Key requirements to the algorithm are robustness towards noise introduced by RSSI-based distance estimation errors and flexibility to introduce additional data sources. Particle filter algorithm was chosen, because it satisfies both requirements, although it has bigger computational costs comparing to Kalman filter. A simulation environment was created to conduct experiments with assumed transmitted signal strength of 1W and square root signal decay law. A Particle filter-based actor was implemented. Actor starts by translating geo-spatial coordinates of beacons into relative planar cartesian coordinates, generates particles around beacons. Particles are placed with uniform distribution within a torus with co-planar circular axis radius equal to distance estimation. In each experiment, simulation software walks actor through a pre-defined path, compares obtained position estimates with expected state and calculates MAE and RMSE. Obtained results were satisfactory, in some cases showing MAE=2.5981 and RMSE=2.8474 after short stabilization period of few iterations. This period exists due to suboptimal initial particles placement. A Kalman filter-based actor was implemented as a comparison. A reference Kalman filter-based implementation showed slightly worse overall results with MAE=12.5199 and RMSE=13.2238. This can be explained by a lack of input from IMU. Further research will be directed towards UAV swarm self-localization and collision avoidance.

Cognitive Control Architecture for the Practical Realization of UAV Collision Avoidance.

A highly intelligent system often draws lessons from the unique abilities of humans. Current humanlike models, however, mainly focus on biological behavior, and the brain functions of humans are often overlooked. By drawing inspiration from brain science, this article shows how aspects of brain processing such as sensing, preprocessing, cognition, obstacle learning, behavior, strategy learning, pre-action, and action can be melded together in a coherent manner with cognitive control architecture. This work is based on the notion that the anti-collision response is activated in sequence, which starts from obstacle sensing to action. In the process of collision avoidance, cognition and learning modules continuously control the UAV's repertoire. Furthermore, simulated and experimental results show that the proposed architecture is effective and feasible.

Study on dynamic collision avoidance during UAV inspection based on improved graph theory network algorithm

Abstract With the development of the times, UAVs are gradually promoted and popularized in military and civil fields, and the future airspace will also face more security risks. This paper combines the graph theory network algorithm to plan the initial path of UAV inspection and completes the dynamic collision avoidance path planning in the process of UAV inspection through the established joint model of UAV inspection sensing and avoidance. At the same time, the ant colony algorithm is introduced to improve the graph theory network algorithm to solve the dynamic collision avoidance optimal path in the process of UAV inspection. On this basis, simulation experiments of path planning design for UAV collision avoidance and kinematic simulation with two-fold priority judgment are carried out, and the kinematic parameters corresponding to the collision avoidance path are selected as the analysis anchor points. The extreme values of horizontal speed, climb speed, trajectory inclination angular rate, and heading angular rate are 31.6 m/s, 3 m/s, 5.7°/s, and 18.4°/s, respectively, which are within the given constraints, and verify the reasonableness and effectiveness of the proposed optimization scheme. The average time of the proposed algorithm is 0.0458s, which is much lower than the corresponding 0.7105s of the original scheme algorithm, through data comparison of simulation experiments and experimental validation. Using a graph theory network algorithm, the proposed optimization scheme is more efficient and stable and has a higher success rate for collision avoidance.

Trajectory Planning with Collision Avoidance for Multiple Quadrotor UAVs Using DMPC

Trajectory Planning with Collision Avoidance for Multiple Quadrotor UAVs Using DMPC

GrpAvoid: Multigroup Collision-Avoidance Control and Optimization for UAV Swarm.

Collision-avoidance control for UAV swarm has recently drawn great attention due to its significant implications in many industrial and commercial applications. However, traditional collision-avoidance models for UAV swarm tend to focus on avoidance at individual UAV level, and no explicit strategy is designed for avoidance among multiple UAV groups. When directly applying these models for multigroup UAV scenarios, the deadlock situation may happen. A group of UAVs may be temporally blocked by other groups in a narrow space and cannot progress toward achieving its goal. To this end, this article proposes a modeling and optimization approach to multigroup UAV collision avoidance. Specifically, group level collision detection and adaption mechanism are introduced, efficiently detecting potential collisions among different UAV groups and restructuring a group into subgroups for better collision and deadlock avoidance. A two-level control model is then designed for realizing collision avoidance among UAV groups and of UAVs within each group. Finally, an evolutionary multitask optimization method is introduced to effectively calibrate the parameters that exist in different levels of our control model, and an adaptive fitness evaluation strategy is proposed to reduce computation overhead in simulation-based optimization. The simulation results show that our model has superior performances in deadlock resolution, motion stability, and distance maintenance in multigroup UAV scenarios compared to the state-of-the-art collision-avoidance models. The model optimization results also show that our model optimization method can largely reduce execution time for computationally-intensive optimization process that involves UAV swarm simulation.

An Effective Strategy for Collision Avoidance of Multiple UAVs With Unknown Acceleration

An Effective Strategy for Collision Avoidance of Multiple UAVs With Unknown Acceleration

Wireless ultraviolet light MIMO assisted UAV direction perception and collision avoidance method

In order to solve the problem of UAV’s direction perception and collision prevention in the limited radio communication environment, we use wireless ultraviolet (UV) communication as an auxiliary communication method in UAVs. We improve the traditional artificial potential field (APF) method, design a UV- MIMO model installed on the UAV, and propose the improved artificial potential field (IAPF) method. On this basis, the determination of the threat source direction in IAPF algorithm is further simplified, and the improved artificial potential field based on ultraviolet MIMO (IAPF-UVMIMO) algorithm is proposed. Experimental results show that IAPF effectively solves the problems of unreachable targets, path oscillations and local minimums that exist in the traditional artificial potential field method. In addition, IAPF-UVMIMO also improves the tolerance of the threat source direction. Compared with IAPF, the number of collisions with actual collision threats is reduced by 22.6%.

A Multi-Agent Deep Reinforcement Learning Approach for Practical Decentralized UAV Collision Avoidance

This letter proposes an improved deep reinforcement learning controller for the decentralized collision avoidance problem. Using a unique architecture incorporating ‘long-short term memory cells’ and a reward function inspired from gradient-based approaches, the controller outperforms existing techniques in environments with variable numbers of agents. The design of the reward function is also evaluated to critique existing deep reinforcement learning approaches and used to establish a better result. The proposed controller is subsequently tested in simulation and a real-world 3-dimensional drone environment: outperforming the predominant classical approach in the literature by a significant margin.

Reduced Simulation: Real-to-Sim Approach toward Collision Detection in Narrowly Confined Environments

Recently, several deep-learning based navigation methods have been achieved because of a high quality dataset collected from high-quality simulated environments. However, the cost of creating high-quality simulated environments is high. In this paper, we present a concept of the reduced simulation, which can serve as a simplified version of a simulated environment yet be efficient enough for training deep-learning based UAV collision avoidance approaches. Our approach deals with the reality gap between a reduced simulation dataset and real world dataset and can provide a clear guideline for reduced simulation design. Our experimental result confirmed that the reduction in visual features provided by textures and lighting does not affect operating performance with the user study. Moreover, by conducting collision detection experiments, we verified that our reduced simulation outperforms the conventional cost-effective simulations in adaptation capability with respect to realistic simulation and real-world scenario.

Robust 3D-Trajectory and Time Switching Optimization for Dual-UAV-Enabled Secure Communications

This paper investigates a dual-unmanned aerial vehicle (UAV)-enabled secure communication system, in which, a UAV moves around to send confidential messages to a mobile user while another cooperative UAV transmits artificial noise signals to confuse malicious eavesdroppers. Both UAVs have energy constraints and the location information of eavesdroppers is imperfect. We consider a worst-case secrecy rate maximization problem of the mobile user over all time slots. This optimization problem is solved by jointly designing the three-dimensional (3D) trajectory of UAVs and the time allocation (recharging and service or jamming) under practical constraints including maximum UAV speed, UAV collision avoidance, UAV positioning error, and UAV energy harvesting. Specifically, we adopt a more practical UAV-ground channel model with both large-scale and small-scale fading components. Due to the non-convex feasible region constructed by the complicated constraints, directly finding the optimal solution of the original problem is intractable. To address this issue, we decouple the original optimization problem into three subproblems and develop an iterative algorithm to find its suboptimal solution by using the block coordinate descent technique. To solve each subproblem, certain advanced optimization tools, such as integer relaxation, S-procedure, and successive convex approximation techniques, are utilized. Numerical simulation results are provided to corroborate the theoretical derivations and to evaluate the performance of the proposed algorithm. Additionally, the numerical results assist to draw new insights on the 3D UAV trajectory by comparing the performance with conventional two-dimensional (2D) schemes.

Sampling based path planning algorithm for UAV collision avoidance

Sampling based path planning algorithm for UAV collision avoidance

Explanation of the choice of the radar sensor structure for the UAV collision avoidance system based on SDR

Explanation of the choice of the radar sensor structure for the UAV collision avoidance system based on SDR

Joint Trajectory Optimization and Spectrum Access for Cognitive UAV Networks

In this article, we consider a cognitive unmanned aerial vehicle network (CUAVN), which consists of a leading unmanned aerial vehicle (LUAV) and a group of following unmanned aerial vehicles (FUAVs), and where the FUAVs fly irregularly from a data collection point to the next data collection point and transmit collected data to the LUAV. Our goal is to maximize the achievable throughput of the CUAVN by jointly adapting the FUAVs sensing duration, the trajectory of the FUAVs, and the transmit power of the FUAVs under the constraints of maximum interference, maximum UAV speed, UAV collision avoidance and the total flying periods. In the challenging scenarios, the original non-convex problem is addressed by iteratively optimizing three decoupled subproblems: sensing duration optimization, FUAVs trajectory optimization, and FUAVs power allocation optimization. Since there are highly non-convex objective function and constraints in the FUAV trajectory optimization subproblem, we convert it into a more tractable convex problem by utilizing difference-of-convex (DC) functions. Furthermore, a three dimensional (3D) spatial-temporal sensing scheme based on UAVs is introduced into cognitive UAV systems with multiple primary transmit-receive pairs (PTRs) to further improve spectrum efficiency. Simulation results show that the achievable throughput of the FUAVs with the proposed scheme rises by approximately 53% compared with the FUAV fixed trajectory flight scheme and by approximately 58% in comparison with the only time-dimension spectrum sensing scheme.

UAV Collision Avoidance Based on Varying Cells Strategy

This paper presents a trajectory planning strategy for collision avoidance in unmanned aerial vehicles. First, a varying cell strategy is proposed to integrate aerodynamic constraints into trajectory planning. Basic avoidance actions in the varying cell strategy are adapted accordingly to go through different cells, enabling more flexible avoidance maneuvers. Second, given the limited decision-making time involved, offline and online path planning methods are developed to increase the convergence rate. Finally, Monte Carlo simulations demonstrate that the proposed method satisfies aerodynamic constraints, while both the convergence and collision avoidance rates are better than that achieved by fixed cell-based methods.

Nonlinear Control for Autonomous Trajectory Tracking While Considering Collision Avoidance of UAVs Based on Geometric Relations

Trajectory tracking with collision avoidance for a multicopter is solved based on geometrical relations. In this paper, a new method is proposed for a multicopter to move from the start position to a desired destination and track a pre-planned trajectory, while avoiding collisions with obstacles. The controller consists of two parts: First, a tracking control is introduced based on the errors between the relative position of the multicopter and the reference path. Second, once the obstacles with a high possibility of collision are detected, a boundary sphere/cylinder of the obstacle is generated by the dimensions of the vehicle and the obstacles, so as to define the safety and risk areas. Afterwards, from the relation between the vehicle’s motion direction, and the tangential lines from the vehicle’s current position to the sphere/cylinder of the obstacle, a collision detection angle is computed to decide the fastest direction to take in order to avoid a collision. The obstacle/collision avoidance control is activated locally when an object is close, and null if the vehicle moves away from the obstacles. The velocity control law and the guidance law are obtained from the Lyapunov stability. In addition, a proportional controller is used at the end of vehicle’s journey to ensure the vehicle stops at the target position. A numerical simulation in different scenarios was performed to prove the effectiveness of the proposed algorithm.

A fast finite-time convergent guidance law with nonlinear disturbance observer for unmanned aerial vehicles collision avoidance

In this paper, a fast finite-time convergent guidance law with nonlinear disturbance observer is proposed for the problem of unmanned aerial vehicle collision avoidance. First, a linear time-varying collision avoidance model based on a collision cone is established. Then a fast finite-time convergent guidance law and a nonlinear disturbance observer are designed to ensure the safety of UAV collision avoidance. In addition, the stability of a nonlinear collision avoidance system is proved by the finite-time convergence stability theory, and the stability conditions are used to design the guidance coefficients. The simulation results conclusively demonstrate that this method can achieve collision avoidance in the presence of an unknown acceleration of obstacle. Moreover, the performance of collision avoidance using this method is greater than the collision avoidance method based on normal finite time convergent guidance.

Design of Moving Object Detector Based on Modified GMM Algorithm for UAV Collision Avoidance

Design of Moving Object Detector Based on Modified GMM Algorithm for UAV Collision Avoidance

A Hybrid Dynamic Window Approach for Collision Avoidance of VTOL UAVs

This paper describes an obstacle collision avoidance technique that considers the dynamics of a VTOL UAV. The dynamic characteristics and controller structure of VTOL UAVs such as helicopters and tiltrotors vary greatly depending on the flight condition. This paper proposes a hybrid dynamic window approach (DWA) which combines a holonomic DWA, non-holonomic DWA and the flight dynamics of a helicopter to avoid collisions with obstacles. Based on the results of obstacle avoidance simulations, the proposed method not only quickly reaches the target point along the optimal path in various obstacle environments but also is superior to the existing DWA without falling into the local minima by expanding the search space from two dimensions to three dimensions.

Real-time Collision Avoidance and Path Optimizer for Semi-autonomous UAVs.

Whilst UAV offers a potentially cheaper and more localized observation platform than current satellite or land-based approaches, it requires an advance path planner to reveal its true potential, particularly in real-time missions. Manual control by human will have limited line-of-sights and prone to errors due to careless and fatigue. A good alternative solution is to equip the UAV with semi-autonomous capabilities that able to navigate via a pre-planned route in real-time fashion. In this paper, we propose an easy-and-practical path optimizer based on the classical Travelling Salesman Problem and adopts a brute force search method to re-optimize the route in the event of collisions using range finder sensor. The former utilizes a Simple Genetic Algorithm and the latter uses Nearest Neighbour algorithm. Both algorithms are combined to optimize the route and avoid collision at once. Although many researchers proposed various path planning algorithms, we find that it is difficult to integrate on a basic UAV model and often lacks of real-time collision detection optimizer. Therefore, we explore a practical benefit from this approach using on-board Arduino and Ardupilot controllers by manually emulating the motion of an actual UAV model prior to test on the flying site. The result showed that the range finder sensor provides a real-time data to the algorithm to find a collision-free path and eventually optimized the route successfully.