Consensus-based cooperative control of parallel fixed-wing UAV formations via adaptive backstepping

Formation flight of fixed-wing UAV swarms: A group-based hierarchical approach

The optimal consensus problem of asynchronous sampling single-integrator and double-integrator multiagent systems is solved by distributed model predictive control (MPC) algorithms proposed in this article. In each predictive horizon, the finite-time linear–quadratic performance is minimized distributively by the control input with consensus state optimization. The MPC technique is then utilized to extend the optimal control sequence to the case of an infinite horizon. Conditions depending only on each agent’s weighting scalar and sampling step are derived to guarantee the stability of the closed-loop system. Numerical examples of rendezvous control of multirobot systems illustrate the efficiency of the proposed algorithm.

Distributed Model Predictive Control for Linear–Quadratic Performance and Consensus State Optimization of Multiagent Systems

A review on fault-tolerant cooperative control of multiple unmanned aerial vehicles

The evolution of artificial intelligence and Internet of Things (IoT) envision a highly integrated artificial IoT (AIoT) network. Flocking and cooperation with multiple unmanned aerial vehicles (UAVs) are expected to play a vital role in industrial AIoT networks. In this article, we formulate the collision-free flocking problem of fixed-wing UAVs as a Markov decision process and solve it in the deep reinforcement learning (DRL) framework. Our method can deal with a variable number of followers by encoding the dynamic environmental state into a fixed-length embedding tensor. Specifically, each follower constructs a fixed-size local situation map that describes the collision risks with other followers nearby. The local situation maps are used by a proposed DRL algorithm to learn the collision-free flocking behavior. To further improve the learning efficiency, we design a reference-point-based action selection strategy and an adaptive mechanism. We compare the proposed MA2D3QN algorithm with several benchmark DRL algorithms through numerical simulation, and we verify its advantages in learning efficiency and performance. Finally, we demonstrate the scalability and adaptability of MA2D3QN in a semiphysical simulation experiment.

Deep Reinforcement Learning of Collision-Free Flocking Policies for Multiple Fixed-Wing UAVs Using Local Situation Maps

In this paper, we present our recent advances in both theoretical methods and field experiments for the coordinated control of miniature fixed-wing unmanned aerial vehicle (UAV) swarms. We propose a multi-layered group-based architecture, which is modularized, mission-oriented, and can implement large-scale swarms. To accomplish the desired coordinated formation flight, we present a novel distributed coordinated-control scheme comprising a consensus-based circling rendezvous, a coordinated path-following control for the leader UAVs, and a leader-follower coordinated control for the follower UAVs. The current framework embeds a formation pattern reconfiguration technique. Moreover, we discuss two security solutions (inter-UAV collision avoidance and obstacle avoidance) in the swarm flight problem. The effectiveness of the proposed coordinated control methods was demonstrated in field experiments by deploying up to 21 fixed-wing UAVs.

Coordinated flight control of miniature fixed-wing UAV swarms: methods and experiments

How to work effectively under occlusion-rich environments remains a challenge for airborne vision-based ground target tracking, due to the natural limitation of monocular vision. Given this, a novel cross-drone binocular coordination approach, inspired by the efficient coordination of human eyes, is proposed and developed. The idea, derived from neural models of the human visual system, is to utilize distributed target measurements to overcome occlusion effects. Eventually, a binocular coordination controller is developed. It enables two distributed pan-tilt cameras to execute synergistic movements similar to human eyes. The proposed approach is able to work based on binocular or monocular vision, and hence it is practically appropriate for various environments. Both testbed experiments and field experiments are conducted for performance evaluation. Testbed experiments highlight its advantages over independent tracking in terms of accuracy while being robust to a partial perception ratio of up to 43%. Field experiments with a pair of drones further demonstrate its effectiveness in the real-world scenarios.

Cross-Drone Binocular Coordination for Ground Moving Target Tracking in Occlusion-Rich Scenarios

Flocking navigation and obstacle avoidance in complex environments remain challenging for multiple unmanned aerial vehicle (multi-UAV) systems, especially when only one UAV (termed as information UAV) knows the predetermined path and the communication range is limited. To this end, we propose a hierarchical weighting Vicsek model (HWVEM). In this model, a hierarchical weighting mechanism and an obstacle avoidance mechanism are designed. Based on the hierarchical weighting mechanism, all the UAVs are divided into different layers, and assigned with different weights according to the layer to which they belong. The purpose is to align the rest of UAVs with the information UAV more efficiently. Subsequently, the obstacle avoidance mechanism that utilizes only the local information is developed to ensure the system safety in an environment filled with obstacles differing in size and shape. A series of simulations have been conducted to demonstrate the high performance of HWVEM in terms of convergence time, success rate, and safety.

Towards Flocking Navigation and Obstacle Avoidance for Multi-UAV Systems through Hierarchical Weighting Vicsek Model

This paper investigates how to keep tracking of a ground target with a camera-equipped unmanned aerial vehicle (UAV) by autonomously steering the pan-tilt movements of the gimbal. In particular, we design and compare two different approaches, the position-based visual servoing (PBVS) and the image-based visual-servoing (IBVS). They mainly differ in whether the UAV attitudes are explicitly reflected in the control law. More specifically, PBVS utilizes the attitude measurements for target localization and compensates for the attitude changes. In contrast, IBVS only uses image tracking errors as feedbacks inputs. A detailed analysis is provided in the main text to reveal as well as interpret the underlying differences of their accuracy and robustness. To put a deeper sight into the tracking performance of the proposed controllers, a high-fidelity semiphysical simulation system has been constructed and a batch of numerical examples have been conducted. As expect, the results demonstrate that the two approaches outperform each other subjecting to different experimental settings. IBVS has a higher tracking accuracy for a moving target, while PBVS is more robust against wind disturbance. Therefore, our work provides potential guidance for practical applications.

Visual-based Online Control of Gimbal on the UAV for Target Tracking

Binocular intersection is practically indispensable for stereo visual systems that extract 3D information from images of two distinct cameras. This paper proposes a multi-UAV binocular intersection approach where the two distinct cameras are fixed separately on multiple flying unmanned aerial vehicles (UAVs), rather than physically connected with each other as in the pre-existing works. Eventually, both modeling and algorithms are developed for the multi-UAV binocular intersection with extremely limited communication. As the formulated model states, once one flying vehicle detects the target and shares related information only in a one-shot manner, the other vehicles steer their pan-tilt units (PTUs) to reconstruct a spatial binocular intersection automatically. The binocular intersection algorithm is further improved from two aspects to maximize the probability of target capture. On one hand, the search scope is narrowed by developing an iterative algorithm with priori terrain height and variance. On the other hand, an approaching navigation law is designed to reduce view occlusion caused by terrain. Extensive simulations with different target speeds and flight altitudes are carried out to evaluate the effectiveness of the proposed method. The results show that the probability of binocular intersection is over 90% and is subject to little fluctuation against episodic parameter changes. Furthermore, the developed algorithms are verified localization-free and time-saving as well, and hereafter are potentially practicable for micro aerial swarm applications with extremely limited onboard computation and communication.

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Yuan Chang

Papers

Coordinated flight control of miniature fixed-wing UAV swarms: methods and experiments

Cross-Drone Binocular Coordination for Ground Moving Target Tracking in Occlusion-Rich Scenarios

Towards Flocking Navigation and Obstacle Avoidance for Multi-UAV Systems through Hierarchical Weighting Vicsek Model

Visual-based Online Control of Gimbal on the UAV for Target Tracking

Multi-UAV Binocular Intersection With One-Shot Communication: Modeling and Algorithms