Unmanned Aerial Vehicle Obstacle Avoidance Research Articles

Joint optimization of UAV communication connectivity and obstacle avoidance in urban environments using a double-map approach

AbstractCellular-connected unmanned aerial vehicles (UAVs), which have the potential to extend cellular services from the ground into the airspace, represent a promising technological advancement. However, the presence of communication coverage black holes among base stations and various obstacles within the aerial domain pose significant challenges to ensuring the safe operation of UAVs. This paper introduces a novel trajectory planning scheme, namely the double-map assisted UAV approach, which leverages deep reinforcement learning to address these challenges. The mission execution time, wireless connectivity, and obstacle avoidance are comprehensively modeled and analyzed in this approach, leading to the derivation of a novel joint optimization function. By utilizing an advanced technique known as dueling double deep Q network (D3QN), the objective function is optimized, while employing a mechanism of prioritized experience replay strengthens the training of effective samples. Furthermore, the connectivity and obstacle information collected by the UAV during flight are utilized to generate a map of radio and environmental data for simulating the flying process, thereby significantly reducing operational costs. The numerical results demonstrate that the proposed method effectively circumvents obstacles and areas with weak connections during flight, while also considering mission completion time.

An Angular Acceleration Based Looming Detector for Moving UAVs.

Visual perception equips unmanned aerial vehicles (UAVs) with increasingly comprehensive and instant environmental perception, rendering it a crucial technology in intelligent UAV obstacle avoidance. However, the rapid movements of UAVs cause significant changes in the field of view, affecting the algorithms' ability to extract the visual features of collisions accurately. As a result, algorithms suffer from a high rate of false alarms and a delay in warning time. During the study of visual field angle curves of different orders, it was found that the peak times of the curves of higher-order information on the angular size of looming objects are linearly related to the time to collision (TTC) and occur before collisions. This discovery implies that encoding higher-order information on the angular size could resolve the issue of response lag. Furthermore, the fact that the image of a looming object adjusts to meet several looming visual cues compared to the background interference implies that integrating various field-of-view characteristics will likely enhance the model's resistance to motion interference. Therefore, this paper presents a concise A-LGMD model for detecting looming objects. The model is based on image angular acceleration and addresses problems related to imprecise feature extraction and insufficient time series modeling to enhance the model's ability to rapidly and precisely detect looming objects during the rapid self-motion of UAVs. The model draws inspiration from the lobula giant movement detector (LGMD), which shows high sensitivity to acceleration information. In the proposed model, higher-order information on the angular size is abstracted by the network and fused with multiple visual field angle characteristics to promote the selective response to looming objects. Experiments carried out on synthetic and real-world datasets reveal that the model can efficiently detect the angular acceleration of an image, filter out insignificant background motion, and provide early warnings. These findings indicate that the model could have significant potential in embedded collision detection systems of micro or small UAVs.

Learning based multi-obstacle avoidance of unmanned aerial vehicles with a novel reward

In this paper, a novel reward-based learning method is proposed for unmanned aerial vehicles to achieve multi-obstacle avoidance. The Markov jump model was first formulated for the unmanned aerial vehicle obstacle avoidance problem. A distinctive reward shaping function is proposed to adaptively avoid obstacles and finally reach the target position via an optimal approach such that an adaptive Q-learning algorithm called the improved prioritized experience replay is developed. Simulation results show that the proposed algorithm can achieve autonomous obstacle avoidance in complex environments with improved performance.

Representation Enhancement-Based Proximal Policy Optimization for UAV Path Planning and Obstacle Avoidance

Path planning and obstacle avoidance are pivotal for intelligent unmanned aerial vehicle (UAV) systems in various domains, such as postdisaster rescue, target detection, and wildlife conservation. Currently, reinforcement learning (RL) has become increasingly popular in UAV decision-making. However, the RL approaches confront the challenges of partial observation and large state space when searching for random targets through continuous actions. This paper proposes a representation enhancement-based proximal policy optimization (RE-PPO) framework to address these issues. The representation enhancement (RE) module consists of observation memory improvement (OMI) and dynamic relative position-attitude reshaping (DRPAR). OMI reduces collision under partially observable conditions by separately extracting perception features and state features through an embedding network and feeding the extracted features to a gated recurrent unit (GRU) to enhance observation memory. DRPAR compresses the state space when modeling continuous actions by transforming movement trajectories of different episodes from an absolute coordinate system into different local coordinate systems to utilize similarity. In addition, three step-wise reward functions are formulated to avoid sparsity and facilitate model convergence. We evaluate the proposed method in three 3D scenarios to demonstrate its effectiveness. Compared to other methods, our method achieves a faster convergence during training and demonstrates a higher success rate and a lower rate of timeout and collision during inference. Our method can significantly enhance the autonomy and intelligence of UAV systems under partially observable conditions and provide a reasonable solution for UAV decision-making under uncertainties.

Review of unmanned aerial vehicle obstacle avoidance planning based on artificial potential field

With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.

Improved path planning method for unmanned aerial vehicles based on artificial potential field

With the advancement of unmanned aerial vehicle (UAV) technology and its widespread use in many facets of manufacturing and daily life, the need for UAV mission automation is becoming more and more practical. In order to improve the automatic obstacle avoidance and path planning performance of UAVs, this essay proposes an optimized route planning algorithm based on the artificial potential field (APF) method, which have solved the typical issue of the APF. This method chooses to conduct a pre planning trajectory of the UAV based on a rapidly expanding random tree (RRT). The pre-planned path will be split into continuous particles, and then generating intermediate waypoints. A nearby waypoint offers a gravitational force to aid the UAV in escaping the local minimum when it enters it. At the same time, taking the distance from the UAV to the obstacle and the radius of influence of the obstacle itself into consideration, dynamically adjust the gravitational and repulsive coefficients, set up a non-gravitational zone around the obstacle, which is beneficial for the UAV to elude the obstacle. And set up a repulsive limited action zone to reduce unnecessary turns in the trajectory to achieve the effect of path optimization. Considering the difficulty of single UAV missions in most cases, this paper discusses cooperative flight path planning for multiple UAVs into consideration.

Cooperative attack based on small-unit UAV swarms formation with trajectory tracking

Cooperative attack with unmanned aerial vehicles (UAVs) plays a critical role in modern military warfare. To achieve multi-swarm cooperative attack with obstacle avoidance of formation, this paper proposes a cooperative control strategy that integrates flight control and autonomous marshaling. Firstly, an improved dynamics model with virtual leader-following mode is constructed to achieve obstacle avoidance of the formation. And an improved interference fluid dynamic system (IIFDS) is applied to improve path selectivity during multi-swarm attack. Secondly, a two-layer attack framework based on distributed swarm coordinated trajectory tracking with heading angle constraints is designed to achieve autonomous clustering of the UAVs and target striking. Finally, the proposed improved dynamics model is compared with the particle swarm optimization (PSO) algorithm and artificial potential field (APF) method in terms of obstacle avoidance of formation to demonstrate its superiority, which can obtain better benefits. Furthermore, two simulations of multi-swarm cooperative attack are conducted to validate the effectiveness of the control strategy. The proposed method expands the application of UAVs attack with obstacle avoidance of formation and provides a valuable reference for modern military operations.

Autonomous aerial obstacle avoidance using LiDAR sensor fusion.

The obstacle avoidance problem of unmanned aerial vehicle (UAV) mainly refers to the design of a method that can safely reach the target point from the starting point in an unknown flight environment. In this paper, we mainly propose an obstacle avoidance method composed of three modules: environment perception, algorithm obstacle avoidance and motion control. Our method realizes the function of reasonable and safe obstacle avoidance of UAV in low-altitude complex environments. Firstly, we use the light detection and ranging (LiDAR) sensor to perceive obstacles around the environment. Next, the sensor data is processed by the vector field histogram (VFH) algorithm to output the desired speed of drone flight. Finally, the expected speed value is sent to the quadrotor flight control and realizes autonomous obstacle avoidance flight of the drone. We verify the effectiveness and feasibility of the proposed method in 3D simulation environment.

The Control Method of Autonomous Flight Avoidance Barriers of UAVs in Confined Environments

This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm. Secondly, a sliding window algorithm is used to estimate the optimal flight path of the UAV and implement obstacle avoidance control, thereby maintaining the attitude stability of the UAV during obstacle avoidance flight. Finally, experimental analysis is conducted, and the results show that the UAV has good attitude stability during obstacle avoidance flight, can autonomously follow the expected trajectory, and can avoid dynamic obstacles, achieving precise obstacle avoidance.

UAV patrol path planning based on machine vision and multi-sensor fusion

Abstract With the rapid development of unmanned aerial vehicle (UAV) technology, there are more and more fields of UAV application. This research mainly discusses the UAV patrol path planning based on machine vision and multi-sensor fusion. This article studies how to apply ultrasonic, a classic ranging sensor, to obstacle avoidance of UAVs. The designed ultrasonic obstacle avoidance system is a complete set of hardware and software systems. The hardware part consists of a forward ultrasonic module and a central signal processing module. Among them, a single-axis stabilization gimbal is designed for the forward ultrasonic module, which decouples the attitude angle of the UAV and the pitch detection angle of the ultrasonic sensor. In the central signal processing module, Kalman filtering is performed on the ultrasonic data in the four directions of front, rear, left, right, and left, and the obstacle avoidance control signal is sent to the flight controller according to the filtered sensor data. At the same time, a human–computer interaction interface is also designed to set various parameters of the obstacle avoidance system. For the route planning method of the tower, the routine steps are used to inspect the tower with a single-circuit line, and the specific targets are the insulator string, the ground wire, and the conductor. In this study, the average statistical result of the straight-line distance of the UAV patrolling 100 m is 99.80 m, and the error is only 0.2%. The fusion obstacle avoidance control method based on machine vision is suitable for the engineering application of UAV perception obstacle avoidance. The obstacle avoidance method adopted in this article can be extended to most flight control platforms, and it is a control method with broad application prospects.

EF-TTOA: Development of a UAV Path Planner and Obstacle Avoidance Control Framework for Static and Moving Obstacles

With the increasing applications of unmanned aerial vehicles (UAVs) in surveying, mapping, rescue, etc., the security of autonomous flight in complex environments becomes a crucial issue. Deploying autonomous UAVs in complex environments typically requires them to have accurate dynamic obstacle perception, such as the detection of birds and other flying vehicles at high altitudes, as well as humans and ground vehicles at low altitudes or indoors. This work’s primary goal is to cope with both static and moving obstacles in the environment by developing a new framework for UAV planning and control. Firstly, the point clouds acquired from the depth camera are divided into dynamic and static points, and then the velocity of the point cloud clusters is estimated. The static point cloud is used as the input for the local mapping. Path finding is simplified by identifying key points among static points. Secondly, the design of a trajectory tracking and obstacle avoidance controller based on the control barrier function guarantees security for moving and static obstacles. The path-finding module can stably search for the shortest path, and the controller can deal with moving obstacles with high-frequency. Therefore, the UAV can deal with both long-term planning and immediate emergencies. The framework proposed in this work enables a UAV to operate in a wider field, with better security and real-time performance.

Cerebellar Model Articulation Neural Network-Based Distributed Fault Tolerant Tracking Control with Obstacle Avoidance for Fixed-Wing UAVs

In this paper, the distributed fault-tolerant tracking control (FTTC) and obstacle avoidance problem is investigated for multiple unmanned aerial vehicles (UAVs) considering lumped disturbances and communication link faults. Firstly, a cerebellar model articulation neural network (CMANN) is introduced to esti-mate the lumped disturbances. Meanwhile, the distributed virtual leader state observers are used to address unknown communication faults. Then, a distributed nonsingular fast terminal sliding mode formation controller is implemented to track desired trajectory, and an virtual-agent artificial potential function (VAAPF) is designed to accomplish obstacle avoidance. Furthermore, the stability of the closed loop formation control systems with obstacle avoidance is proved using graph theory and Lyapunov theory. Finally, simulation results of three fixed-wing UAVs are given to show the effectiveness and good performance of the proposed scheme.

Image Quality Enhancement with Applications to Unmanned Aerial Vehicle Obstacle Detection

Aiming at the problem that obstacle avoidance of unmanned aerial vehicles (UAVs) cannot effectively detect obstacles under low illumination, this research proposes an enhancement algorithm for low-light airborne images, which is based on the camera response model and Retinex theory. Firstly, the mathematical model of low-illumination image enhancement is established, and the relationship between the camera response function (CRF) and brightness transfer function (BTF) is constructed by a common parameter equation. Secondly, to solve the problem that the enhancement algorithm using the camera response model will lead to blurred image details, Retinex theory is introduced into the camera response model to design an enhancement algorithm framework suitable for UAV obstacle avoidance. Thirdly, to shorten the time consumption of the algorithm, an acceleration solver is adopted to calculate the illumination map, and the exposure matrix is further calculated via the illumination map. Additionally, the maximum exposure value is set for low signal-to-noise ratio (SNR) pixels to suppress noise. Finally, a camera response model and exposure matrix are used to adjust the low-light image to obtain an enhanced image. The enhancement experiment for the constructed dataset shows that the proposed algorithm can significantly enhance the brightness of low-illumination images, and is superior to other similar available algorithms in quantitative evaluation metrics. Compared with the illumination enhancement algorithm based on infrared and visible image fusion, the proposed algorithm can achieve illumination enhancement without introducing additional airborne sensors. The obstacle object detection experiment shows that the proposed algorithm can increase the AP (average precision) value by 0.556.

Autonomous Navigation and Obstacle Avoidance for Small VTOL UAV in Unknown Environments

This paper takes autonomous exploration in unknown environments on a small co-axial twin-rotor unmanned aerial vehicle (UAV) platform as the task. The study of the fully autonomous positioning in unknown environments and navigation system without global navigation satellite system (GNSS) and other auxiliary positioning means is carried out. Algorithms that are based on the machine vision/proximity detection/inertial measurement unit, namely the combined navigation algorithm and indoor simultaneous location and mapping (SLAM) algorithm, are not only designed theoretically but also realized and verified in real surroundings. Additionally, obstacle detection, the decision-making of avoidance motion and motion planning methods such as Octree are also proposed, which are characterized by randomness and symmetry. The demonstration of the positioning and navigation system in the unknown environment and the verification of the indoor obstacle-avoidance flight were both completed through building an autonomous navigation and obstacle avoidance simulation system.

A high-accuracy calibration method for fusion systems of millimeter-wave radar and camera

Multi-sensor information fusion is widely used in the field of unmanned aerial vehicles obstacle avoidance flight, particularly in millimeter-wave (MMW) radar and camera fusion systems. Calibration accuracy plays a crucial role in fusion systems. The low-angle measurement accuracy of the MMW radar usually causes large calibration errors. To reduce calibration errors, a high-accuracy calibration method based on a region of interest (ROI) and an artificial potential field was proposed in this paper. The ROI was selected based on the initial calibration information and the MMW radar’s angle measurement error range from the image. An artificial potential field was established using the pixels of the ROI. Two moving points were set at the left and right ends of the ROI initially. The potential forces of the two moving points are different because the pixels of the obstacle and the background are different in the image. The two moving points were iteratively moved towards each other according to the force until their distance was less than the iteration step. The new calibration point is located in the middle of the final position of the two moving points. In contrast to the existing calibration methods, the proposed method avoids the limitations of low angle measurement accuracy by using image pixels. The experimental results show that the calibration errors decrease by 83.95% and 75.79%, which is significantly improved compared to the traditional methods and indicates the efficiency of the proposed method.

Rotate artificial potential field algorithm toward 3D real-time path planning for unmanned aerial vehicle

As one of the key technologies in the development of Unmanned Aerial Vehicle (UAV), the path planning method has an intuitive impact on the real-time performance of UAV. Most existing path planning algorithms have drawbacks in global optimization ability or optimization speed, and cannot meet the requirements of practical applications. To improve the effectiveness and practicability of the path planning algorithm for UAV obstacle avoidance, this paper proposes the Rotate Artificial Potential Field (R-APF) method based on the traditional artificial potential field method. By introducing a new potential field function and stimulating rotating repulsive force, the R-APF solves the problems of local minimum as well as inaccessibility to targets near obstacles, respectively. Convergence of the algorithm is discussed to demonstrate the feasibility of R-APF. This paper also designs and implements algorithm feasibility verification experiments, obstacle shuttle experiments, as well as running time, and efficiency comparison experiments. In addition, the improved Visual-Inertial Navigation System obstacle detection algorithm is introduced to carry out physical verification experiments. Theoretical proof and experimental results show the proposed R-APF method has higher obstacle avoidance capability, shorter running time, and higher success rate compared to other algorithms, and is promising in practical application.

Autonomous obstacle avoidance of UAV based on deep reinforcement learning1

In the intelligent unmanned systems, unmanned aerial vehicle (UAV) obstacle avoidance technology is the core and primary condition. Traditional algorithms are not suitable for obstacle avoidance in complex and changeable environments based on the limited sensors on UAVs. In this article, we use an end-to-end deep reinforcement learning (DRL) algorithm to achieve the UAV autonomously avoid obstacles. For the problem of slow convergence in DRL, a Multi-Branch (MB) network structure is proposed to ensure that the algorithm can get good performance in the early stage; for non-optimal decision-making problems caused by overestimation, the Revise Q-value (RQ) algorithm is proposed to ensure that the agent can choose the optimal strategy for obstacle avoidance. According to the flying characteristics of the rotor UAV, we build a V-Rep 3D physical simulation environment to test the obstacle avoidance performance. And experiments show that the improved algorithm can accelerate the convergence speed of agent and the average return of the round is increased by 25%.

Cooperative Obstacle Avoidance for Multiple UAVs Using Spline_VO Method.

In order to solve multiple unmanned aerial vehicle (UAV) dynamic collision avoidance, a cooperative obstacle avoidance algorithm considering UAV’s kinematic constraints has been developed. In the proposed algorithm, the useful information of UAVs is screened out by a Heartbeat information filtering mechanism and fused by the user datagram protocol (UDP) communication method, which improves communication performance among UAVs. In addition, the velocity obstacle (VO) method combined with cubic uniform B-spline curve is used to avoid obstacles and generate smooth paths, which can be applied to practical scenes. Finally, dynamic and static obstacle avoidance simulations are carried out to verify the effectiveness of the proposed algorithm.

Path Planning and Obstacle Avoidance for UAV in an Uncharted Environment

Abstract: The growing industry of unmanned aerial vehicles (UAV) requires an efficient and robust algorithm to decide the path of the UAV and avoid obstacles. The study of pathfinding algorithms is ongoing research not just useful in the domain of drones, but in other fields like video games (AI pathfinding), terrain traversal (mapped, unmapped, areal, underwater, land, etc.), and industries that require robots to deliver packages. This paper proposes a new pathfinding algorithm that aims to solve the problem of pathfinding in unknown 2-dimensional terrain. Based on a system of assumptions and using the help of a set of sensors aboard the UAV, the algorithm navigates the UAV from a start point to an endpoint while avoiding any shape or size of obstacles in between. To avoid multiple different types of “infinite loop” situations where the UAV gets stuck around an obstacle, a priority-based selector for intermediate destinations is created. The algorithm is found to work effectively when simulated in Gazebo on Robot Operating System (ROS). Keywords: Path Planning, UAV, Obstacle Avoidance, Drone Navigation, Obstacle Detection, Uncharted Environment.

Overview of Obstacle Avoidance Algorithms for UAV Environment Awareness

In complex indoor and outdoor environment, obstacle avoidance of UAV (Unmanned Aerial Vehicles) is a challenging problem. In order to realize the obstacle detection, autonomous positioning and trajectory planning of UAV flight mission in large outdoor scene, there are three main technical problems: Firstly, UAV needs to have the ability to quickly detect a variety of obstacles in outdoor scene. Secondly, in order to realize the autonomous navigation of UAV, it needs to establish the coordinates of obstacles in three-dimensional space. Thirdly, based on the above two conditions, UAV can independently plan flight path to avoid obstacles. This paper mainly introduces the use of RGB-D camera, lidar, monocular camera and binocular camera in UAV obstacle avoidance, and compares them from the sensor types, advantages disadvantages and application range. Secondly, the path planning strategy of UAV is discussed, and the existing problems and current research results of UAV trajectory planning strategy are described. Finally, it is pointed out that real-time computing, multi-sensor fusion and integrated obstacle avoidance between multi aircraft should be the research direction of autonomous obstacle avoidance navigation for UAV in the future.