Unmanned Aerial Vehicle Obstacle Avoidance Research Articles

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.

A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV

In this paper, a bio-inspired path planning algorithm in 3D space is proposed. The algorithm imitates the basic mechanisms of plant growth, including phototropism, negative geotropism and branching. The algorithm proposed in this paper solves the dynamic obstacle avoidance path planning problem of Unmanned Aerial Vehicle (UAV) in the case of unknown environment maps. Compared with other path planning algorithms, the algorithm has the advantages of fast path planning speed and fewer route points, and can achieve the effect of low delay real-time path planning. The feasibility of the algorithm is verified in the Gazebo simulator based on the Robot Operating System (ROS) platform. Finally, an actual UAV autonomous obstacle avoidance path planning experimental platform is built, and a UAV obstacle avoidance path planning flight test is carried out based on this actual environment.

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

Unmanned aerial vehicle (UAV) formation has a wide range of applications as it can increase the success rate of task and improve the reliability of system. Formation control and obstacle avoidance are two related key problems and are studied based on the consensus theory in this paper. First, two forms of kinetic models for UAV are described, and the standard model and the model with autopilot are deployed to apply in obstacle avoidance and formation control respectively. The constraints on the maneuverability of UAV are proposed. Based on the standard consensus algorithm, the three degrees of freedom (DOFs) of UAV in the improved consensus algorithm (ICA) are converted to be consistent with the DOFs describing the relative positions among UAVs, which make it possible to add the formation information to the standard consensus algorithm. The constraints on the maneuverability of UAV are also met by using the proposed minimal adjustment strategy. In the obstacle avoidance problem, the particle swarm optimization (PSO) algorithm is introduced, and the static obstacles and dynamic obstacles are dealt with by the ICA-PSO algorithm and the model predictive control (MPC)-PSO algorithm respectively, and the two algorithms can be combined to cope with more complicated situations. Simulation results demonstrate that the ICA can make UAVs with different initial states form the specified formation while satisfying all the constraints, and a safe and efficient flight for UAV formation can be ensured with the obstacle avoidance algorithm.

Research on the Multiagent Joint Proximal Policy Optimization Algorithm Controlling Cooperative Fixed-Wing UAV Obstacle Avoidance.

Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability.

Three‐dimensional obstacle avoidance for UAV based on reinforcement learning and RealSense

With the increasingly widespread application of unmanned aerial vehicle (UAV), safety issues such as effectiveness of obstacle avoidance have been paid more attentions. The classical obstacle avoidance algorithms are mostly suitable for mobile robots, but these algorithms are not ideal for UAV using in three-dimensional space. Most of the three-dimensional obstacle avoidance algorithms which are more effective using RGB image data as input. Thus, a large amount of image data is involved in complex computing process. This study proposes an effective obstacle avoidance algorithm for UAV with less input data and fewer sensors based on RealSense and reinforcement learning. It combines the feature map of the depth image of RealSense as the input data of reinforcement learning and the current direction of flight of UAV to calculate the direction and angle of avoiding. The proposed algorithm that implements real-time obstacle avoidance for UAV has been verified by simulation and tested in three-dimensional space scenario.

Automatic obstacle avoidance of quadrotor UAV via CNN-based learning

In this paper, a CNN-based learning scheme is proposed to enable a quadrotor unmanned aerial vehicle (UAV) to avoid obstacles automatically in unknown and unstructured environments. In order to reduce the decision delay and to improve the robustness for the UAV, a two-stage end-to-end obstacle avoidance architecture is designed, where a forward-facing monocular camera is used only. In the first stage, a convolutional neural network (CNN)-based model is adopted as the prediction mechanism. Utilizing three effective operations, namely depthwise convolution, group convolution and channel split, the model predicts the steering angle and the collision probability simultaneously. In the second stage, the control mechanism maps the steering angle to an instruction that changes the yaw angle of the UAV. Consequently, when the UAV encounters an obstacle, it can avoid collision by steering automatically. Meanwhile, the collision probability is mapped as a forward speed to maintain the flight or stop going forward. The presented automatic obstacle avoidance scheme of quadrotor UAV is verified by several indoor/outdoor tests, where the feasibility and efficacy have been demonstrated clearly. The novelties of the method lie in its low sensor requirement, light-weight network structure, strong learning ability and environmental adaptability.

Obstacle Recognition and Avoidance for UAVs Under Resource-Constrained Environments

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.

Applications and Prospects of Agricultural Unmanned Aerial Vehicle Obstacle Avoidance Technology in China.

With the steady progress of China’s agricultural modernization, the demand for agricultural machinery for production is widely growing. Agricultural unmanned aerial vehicles (UAVs) are becoming a new force in the field of precision agricultural aviation in China. In those agricultural areas where ground-based machinery have difficulties in executing farming operations, agricultural UAVs have shown obvious advantages. With the development of precision agricultural aviation technology, one of the inevitable trends is to realize autonomous identification of obstacles and real-time obstacle avoidance (OA) for agricultural UAVs. However, the complex farmland environment and changing obstacles both increase the complexity of OA research. The objective of this paper is to introduce the development of agricultural UAV OA technology in China. It classifies the farmland obstacles in two ways and puts forward the OA zones and related avoidance tactics, which helps to improve the safety of aviation operations. This paper presents a comparative analysis of domestic applications of agricultural UAV OA technology, features, hotspot and future research directions. The agricultural UAV OA technology of China is still at an early development stage and many barriers still need to be overcome.

Robust nonlinear control approach to nontrivial maneuvers and obstacle avoidance for quadrotor UAV under disturbances

Abstract In this paper, we present an onboard robust nonlinear control approach for quadrotor Unmanned Aerial Vehicles (UAVs) in the environments with disturbances and obstacles. The complete framework consists of an attitude controller based on the solution of global output regulation problems for SO( 3 ), a backstepping-like position controller, a 6 -dimensional wrench observer to estimate the unknown force and torque disturbances, and an online trajectory planner based on a model predictive control method with obstacle avoiding constraints. We prove the strong convergence properties of the proposed method both in theory and via real-robot experiments. The control approach is onboard implemented on a quadrotor UAV, and has been validated through intensive experiments and compared with other nonlinear control methods for waypoint navigation and large-tilted path following tasks in the presence of external disturbances, e.g. wind gusts. The presented approach has also been evaluated in the scenarios with randomly located obstacles.

An evaluation of stereo and laser‐based range sensing for rotorcraft unmanned aerial vehicle obstacle avoidance

AbstractWe present an evaluation of stereo vision and laser‐based range sensing for rotorcraft unmanned aerial vehicle (RUAV) obstacle avoidance. Our focus is on sensors that are suitable for mini‐RUAV class vehicles in terms of weight and power consumption. The study is limited to the avoidance of large static obstacles such as trees. We compare two commercially available devices that are representative of the state of the art in two‐dimensional scanning laser and stereo‐based sensing. Stereo is evaluated with three different focal length lenses to assess the tradeoff between range resolution and field of view (FOV). The devices are evaluated in the context of obstacle avoidance through extensive flight trials with an RUAV. We discuss the merits and limitations of each sensor type, including sensing range, FOV, accuracy, and susceptibility to lighting conditions. We show that the stereo device fitted with 8‐mm lenses has a better sensing range and vertical FOV than the laser device; however, it relies on careful calibration and is affected by high‐contrast outdoor lighting conditions. The laser has a wider horizontal FOV and is more reliable at detecting obstacles that are within a 20‐m range. Overall the laser produced superior obstacle avoidance performance, with a success rate of 84% compared to 42% for 8‐mm stereo. © 2012 Wiley Periodicals, Inc.

Optimization-Based Safety Analysis of Obstacle Avoidance Systems for Unmanned Aerial Vehicles

The integration of Unmanned Aerial Vehicles (UAVs) in airspace requires new methods to certify collision avoidance systems. This paper presents a safety clearance process for obstacle avoidance systems, where worst case analysis is performed using simulation based optimization in the presence of all possible parameter variations. The clearance criterion for the UAV obstacle avoidance system is defined as the minimum distance from the aircraft to the obstacle during the collision avoidance maneuver. Local and global optimization based verification processes are developed to automatically search the worst combinations of the parameters and the worst-case distance between the UAV and an obstacle under all possible variations and uncertainties. Based on a 6 Degree of Freedom (6DoF) kinematic and dynamic model of a UAV, the path planning and collision avoidance algorithms are developed in 3D space. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is a simple and widely used method. Different optimization algorithms are applied and compared in terms of the reliability and efficiency.

AUTONOMOUS UAV FUZZY LOGIC NAVIGATION AND OBSTACLE AVOIDANCE CONTROLLER

Unmanned Aerial Vehicles (UAVs) are defined by the United States Department of Defense (DOD) as powered, aerial vehicles that do not carry a human operator. UAVs control systems have assumed an increasingly important role in the development and advancement of modern civilization and technology. In recent years, intelligent control of autonomous navigation of UAVs has gained much interest mainly: neural networks, non-linear adaptive control, fuzzy logic as well as combinations of these methods, such as, neuro—fuzzy control, or fuzzy logic and evolutionary or genetic algorithms. The purpose of this paper is to develop an autonomous UAV fuzzy Logic navigation and obstacle avoidance controller that is capable to perform a way point navigation and obstacles avoidance along its flight. The navigation and obstacle avoidance behavior is transformed via fuzzy rules. Multi-platform simulation package is developed to perform closed loop on-line environment sensing. Several simulation tests have been conducted on the new controller. These tests show that the proposed controller achieves the desired performance with respect to its sensors capability and limitations.