Unmanned Aerial Vehicle Autonomous Control Research Articles

Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)

There is a strong trend in the development of control systems for multi-rotor unmanned aerial vehicles (UAVs), where minimization of a control signal effort is conducted to extend the flight time. The aim of this article is to shed light on the problem of shaping control signals in terms of energy-optimal flights. The synthesis of a UAV autonomous control system with a brain emotional learning based intelligent controller (BELBIC) is presented. The BELBIC, based on information from the feedback loop of the reference signal tracking system, shows a high learning ability to develop an appropriate control action with low computational complexity. This extends the capabilities of commonly used fixed-value proportional–integral–derivative controllers in a simple but efficient manner. The problem of controller tuning is treated here as a problem of optimization of the cost function expressing control signal effort and maximum precision flight. The article introduces several techniques (bio-inspired metaheuristics) that allow for quick self-tuning of the controller parameters. The performance of the system is comprehensively analyzed based on results of the experiments conducted for the quadrotor model.

A Study on Path Planning Algorithms of UAV Collision Avoidance

The unmanned aerial vehicle (UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms, the artificial potential field, fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile, an improved algorithm of artificial potential field is proposed, and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable, which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms.

Ellipse proposal and convolutional neural network discriminant for autonomous landing marker detection

AbstractAutonomous landing in complex environments is a critical problem for unmanned aerial vehicle autonomous control, and efficiently detecting landing identification mark in real‐world scenario is still challenging. Due to the limited computational power of airborne computing equipment, current target detection algorithms cannot meet the demand efficiently. In this paper, we proposed a new landing marker detection algorithm for autonomous landing systems in a real environment. We used an ellipse detection algorithm to detect the ellipse landmark or other elliptical objects. Furthermore, convolution neural networks were utilized to obtain the correct landmarks, which is robust, fast and can achieve a speed of 25 fps with 720p resolution video on an Intel NUC onboard computer. Unlike the other methods, the accuracy and speed of our algorithm is verified in a real‐time application with more harsh conditions. During system testing, the flight system can detect the object above 20 m, track it, and automatically land on it with this vision algorithm. The algorithm has helped us achieve the first position at Mohamed Bin Zayed International Robotics Challenge in Abu Dhabi this year.

Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm

The unmanned aerial vehicle (UAV) has been a research focus in recent years. The path planner is a key element of the unmanned aerial vehicle autonomous control module. In this paper, the modified wolf pack search (WPS) algorithm is applied to compute the quasi-optimal trajectories for the rotor wing UAVs in the complex three-dimensional (3D) spaces including the real and fake 3D spaces. Moreover, it adopts the multi-objective cost function. In the path planning process, some concepts in the genetic algorithm (GA) are applied to realize the WPS algorithm. Then, the crossover and mutation operators in the GA method are introduced to improve the original WPS algorithm. Considering the dynamic properties of the vehicle, the path smoothing process based on the cubic B-spline curve is used to make the planning path suitable for the fixed wing UAVs. Simulation results show that this approach is efficient for the rotor wing UAVs and the fixed wing UAVs when taking into account of all kinds of constraints and the path generated is flyable. Moreover, the comparisons of the four algorithms show that the trajectories produced by the modified WPS algorithm are far superior to the original WPS algorithm, the GA and the random search way under the same conditions.

Modified central force optimization (MCFO) algorithm for 3D UAV path planning

Path planning for the three-dimensional (3D) unmanned aerial vehicles (UAV) is a very important element of the whole UAV autonomous control system. In this paper, a modified central force optimization (MCFO) method is introduced to solve this complicated path-optimization problem for the rotary wing vertical take-off and landing (VTOL) aircraft. In the path planning process, the idea from the particle swarm optimization (PSO) algorithm and the mutation operator of the genetic algorithm (GA) are applied to improve the original CFO method. Furthermore, the convergence analysis of the whole MCFO method is established by the linear difference equation method. Then, in order to verify the effectiveness and practicality of this new path planning method, the path following process is put forward based on the six-degree-of-freedom quadrotor helicopter control system. At last, the comparison simulations among the six algorithms show that the trajectories produced by the whole MCFO method are more superior than the original CFO algorithm, the GA, the Firefly algorithm (FA), the PSO algorithm, the random search (RS) way and the other MCFO algorithm under the same conditions. What is more, the path following process results show that the path planning results are practical for the real dynamic model of the quadrotor helicopter.

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Autonomous mission control, unlike automatic mission control which is generally pre-programmed to execute an intended mission, is guided by the philosophy of carrying out a complete mission on its own through online sensing, information processing, and control reconfiguration. A crucial cornerstone of this philosophy is the capability of intelligence and of information sharing between unmanned aerial vehicles (UAVs) or with a central controller through secured communication links. Though several mission control algorithms, for single and multiple UAVs, have been discussed in the literature, they lack a clear definition of the various autonomous mission control levels. In the conventional system, the ground pilot issues the flight and mission control command to a UAV through a command data link and the UAV transmits intelligence information, back to the ground pilot through a communication link. Thus, the success of the mission depends entirely on the information flow through a secured communication link between ground pilot and the UAV. In the past, mission success depended on the continuous interaction of ground pilot with a single UAV, while present day applications are attempting to define mission success through efficient interaction of ground pilot with multiple UAVs. However, the current trend in UAV applications is expected to lead to a futuristic scenario where mission success would depend only on interaction among UAV groups with no interaction with any ground entity. However, to reach this capability level, it is necessary to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. This article presents a detailed framework of UAV autonomous mission control levels in the context of information flow and communication between UAVs and UAV groups for each level of autonomy.