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.

Multiple unmanned aerial vehicles (UAVs) are able to efficiently accomplish a variety of tasks in complex scenarios. However, developing a collision-avoiding flocking policy for multiple fixed-wing UAVs is still challenging, especially in obstacle-cluttered environments. In this article, we propose a novel curriculum-based multiagent deep reinforcement learning (MADRL) approach called task-specific curriculum-based MADRL (TSCAL) to learn the decentralized flocking with obstacle avoidance policy for multiple fixed-wing UAVs. The core idea is to decompose the collision-avoiding flocking task into multiple subtasks and progressively increase the number of subtasks to be solved in a staged manner. Meanwhile, TSCAL iteratively alternates between the procedures of online learning and offline transfer. For online learning, we propose a hierarchical recurrent attention multiagent actor–critic (HRAMA) algorithm to learn the policies for the corresponding subtask(s) in each learning stage. For offline transfer, we develop two transfer mechanisms, i.e., model reload and buffer reuse, to transfer knowledge between two neighboring stages. A series of numerical simulations demonstrate the significant advantages of TSCAL in terms of policy optimality, sample efficiency, and learning stability. Finally, the high-fidelity hardware-in-the-loop (HITL) simulation is conducted to verify the adaptability of TSCAL. A video about the numerical and HITL simulations is available at https://youtu.be/R9yLJNYRIqY.

Flocking with multiple unmanned aerial vehicles (UAVs) offers significant potential for diverse applications due to its enhanced maneuverability, improved efficiency, and increased robustness. Collision avoidance is a critical and challenging issue for distributed flocking control with a UAV fleet, especially in dynamic environments with varying numbers of non-cooperative intruders. However, existing reinforcement learning based methods mainly focus on flocking with collision avoidance tasks with static obstacles and a fixed number of UAVs. In this article, we propose a scalable multi-agent reinforcement learning based method to solve the distributed flocking with collision avoidance problem for a scalable fleet of fixed-wing UAVs in dynamic environments. Specifically, we cast this problem in a decentralized partially observable Markov decision process framework and propose a scalable multi-agent reinforcement learning algorithm called spatial-temporal attention multi-agent actor-critic (STAAC). In this algorithm, we design a spatial-temporal attention based population-invariant network architecture to facilitate the representation learning of dynamic dimensional observations. By integrating the local spatial attention and global temporal attention mechanisms, STAAC is able to adapt to the changes in the scale of UAV fleets and the number of intruders. Finally, we empirically demonstrate the effectiveness, scalability, and adaptability of the proposed approach in numerical simulations and hardware-in-the-loop experiments.

Centralized multi-robot path planning is a prevalent approach involving a global planner computing feasible paths for each robot using shared information. Nonetheless, this approach encounters limitations due to communication constraints and computational complexity. To address these challenges, we introduce a novel decentralized multi-robot path planning approach that eliminates the need for sharing the states and intentions of robots. Our approach harnesses deep reinforcement learning and features an asynchronous multi-critic twin delayed deep deterministic policy gradient (AMC-TD3) algorithm, which enhances the original GRU-Attention based TD3 algorithm by incorporating a multi-critic network and employing an asynchronous training mechanism. By training each critic with a unique reward function, our learned policy enables each robot to navigate towards its long-term objective without colliding with other robots in complex environments. Furthermore, our reward function, grounded in social norms, allows the robots to naturally avoid each other in congested situations. Specifically, we train three critics to encourage each robot to achieve its long-term navigation goal, maintain its moving direction, and prevent collisions with other robots. Our model can learn an end-to-end navigation policy without relying on an accurate map or any localization information, rendering it highly adaptable to various environments. Simulation results reveal that our proposed approach surpasses baselines in several environments with different levels of complexity and robot populations.

To enable unmanned aerial vehicle (UAV) operators to efficiently and intuitively convey their commands to a swarm of UAVs, we propose the use of natural and human-centric input modalities, such as voices and gestures. This paper addresses the fusion of input modalities such as voice and gesture data, which are captured through a microphone and a Leap Motion controller, respectively, to control UAV swarms. The obtained experimental results are presented, and the achieved performance (accuracy) is analyzed. Finally, combined human factor ergonomics test with a questionnaire to verify the method’s validity.