Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

UAV group formation collision avoidance method based on second-order consensus algorithm and improved artificial potential field

The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the "leader-follower" strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method

Collision avoidance control for Unmanned Autonomous Vehicles (UAV): Recent advancements and future prospects

873-883The recent advances in collision avoidance technologies for unmanned vehicles such as UAVs, AUVs, AGVs, and USVs have greatly advanced the industry. Their lower cost and acceptability of high-risk missions have enabled the development of collision avoidance controllers for autonomous vehicles. These low-maintenance gadgets are also portable, need low maintenance, and enable continuous monitoring to occur near real-time. This may be said; however it would be incorrect, because collision avoidance controllers have been related with compromises that affect data dependability. Research on collision avoidance controls is quickly developing; therefore it is distributed throughout multiple papers, projects, and grey literature. This report critically reviews the recent relevant research on creating collision avoidance systems for autonomous vehicles. Typically, the assessment measures are dependent on the algorithm's use case and the platform's capabilities. The full evaluation of the benefits and drawbacks of the most prevalent approaches in the present state of the art is provided based on 7 metrics which are complexity, communication dependence, pre-mission planning, robustness, 3D compatibility, real-time performance and escape trajectories

Collision avoidance control for Unmanned Autonomous Vehicles (UAV): Recent advancements and future prospects

The recent advances in collision avoidance technologies for unmanned vehicles such as UAVs, AUVs, AGVs, and USVs have greatly advanced the industry. Their lower cost and acceptability of high-risk missions have enabled the development of collision avoidance controllers for autonomous vehicles. These low-maintenance gadgets are also portable, need low maintenance, and enable continuous monitoring to occur near real-time. This may be said; however it would be incorrect, because collision avoidance controllers have been related with compromises that affect data dependability. Research on collision avoidance controls is quickly developing; therefore it is distributed throughout multiple papers, projects, and grey literature. This report critically reviews the recent relevant research on creating collision avoidance systems for autonomous vehicles. Typically, the assessment measures are dependent on the algorithm's use case and the platform's capabilities. The full evaluation of the benefits and drawbacks of the most prevalent approaches in the present state of the art is provided based on 7 metrics which are complexity, communication dependence, pre-mission planning, robustness, 3D compatibility, real-time performance and escape trajectories

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade