This article studies the formation and trajectory tracking control of multiagent systems. We present a novel multilayer graph for the multiagent system to enable extensibility of the interaction network. Based on the multilayer graph, a formation control law by using the potential function approach is developed for autonomous formation, formation maintenance, collision, and obstacle avoidance. When the desired formation is achieved, the barycentric of the formation shape is viewed as a virtual leader, and a model predictive control (MPC) scheme is applied to the virtual leader for tracking a reference trajectory; meanwhile, the agents will maintain the desired angles and distances via the formation control law. By applying the proposed schemes, the tasks of formation maintenance and trajectory tracking in a constrained space are fulfilled. Comprehensive simulation studies under different environmental constraints and trajectories confirm the effectiveness of the proposed approaches in addressing the formation and trajectory tracking problems.

Object detectors often struggle with accuracy and generalization when applied to aerial imagery, primarily due to the following challenges: 1) great scale variation of objects in aerial images: both extremely small and large objects are visible in the same image; and 2) an extreme imbalance of the training sample between positive and negative anchors: there are several positive ground truth (GT) anchors and an abundance of negative anchors. In this article, we propose a dynamic training sample selection network (DTSSNet) to solve the above-mentioned problems in two dimensions. An attention-enhanced feature module (AEFM) is proposed to enhance the basic features by focusing on both channel and semantic information related to targets. This module provides more valuable information for accurately classifying objects of different scales. To tackle the imbalance in training samples, this article implements a dynamic training sample selection (DTSS) module that divides the training samples based on GT information. This module dynamically selects samples, ensuring a more balanced representation of positive and negative anchors, leading to improved learning. Importantly, the combination of AEFM and DTSS does not introduce any additional computational costs. Experimental evaluations on the VisDrone2019-DET dataset demonstrate that DTSSNet outperforms base detectors and generic approaches. Furthermore, the effectiveness of DTSSNet is validated on the UAVDT benchmark dataset, where it achieves state-of-the-art performance.

Summary: As for the obstacle avoidance and formation control for the multi-robot systems, this paper presents an obstacle-avoidance method based on the improved artificial potential field (IAPF) and PID adaptive tracking control algorithm. In order to analyze the dynamics and kinematics of the robot, the mathematical model of the robot is built. Then we construct the motion situational awareness map (MSAM), which can map the environment information around the robot on the MSAM. Based on the MSAM, the IAPF functions are established. We employ the rotating potential field to solve the local minima and oscillations. As for collisions between robots, we build the repulsive potential function and priority model among the robots. Afterwards, the PID adaptive tracking algorithm is utilized to multi-robot formation control. To demonstrate the validity of the proposed method, a series of simulation results confirm that the approaches proposed in this paper can successfully address the obstacle- and collision-avoidance problem while reaching formation.

In this article, an adaptive path following the controller of a multijoint snake robot (MSR) based on the improved Serpenoid curve is proposed. The proposed controller can make the MSR follow the desired path. Compared with the traditional controller, this controller can make the position error possess fast convergence speed and high stability. The swing, the controller can estimate unknown friction coefficients, which improves the adaptive path following the ability of the MSR in an environment with unknown friction coefficients. First, the dynamic model without lateral force is established. Then, the control objectives of the controller are formulated. Third, the Serpenoid gait equation is improved, and the state-dependent time-varying amplitude is obtained. Fourth, the input–output control function of the system and the tracking function of the swing amplitude compensation are designed by the adaptive control method. The stability of the motion attitude angle variable errors and uniformly ultimately bounded stability of the tracking position are verified, respectively. Finally, the effectiveness and superiority of the proposed controller are verified by experiments.

In this paper, we propose a C51-Duel-IP (C51 Dueling DQN with Independent Policy) dynamic destination path-planning algorithm to solve the problem of autonomous navigation and avoidance of multiple Unmanned Aerial Vehicles (UAVs) in the communication denial environment. Our proposed algorithm expresses the Q function output by the Dueling network as a Q distribution, which improves the fitting ability of the Q value. We also extend the single-step temporal differential (TD) to the N-step timing differential, which solves the problem of inflexible updates of the single-step temporal differential. More importantly, we use an independent policy to achieve autonomous avoidance and navigation of multiple UAVs without any communication with each other. In the case of communication rejection, the independent policy can achieve the consistency of multiple UAVs and avoid the greedy behavior of UAVs. In multiple-UAV dynamic destination scenarios, our work includes path planning, taking off from different initial positions, and dynamic path planning, taking off from the same initial position. The hardware-in-the-loop (HITL) experiment results show that our C51-Duel-IP algorithm is much more robust and effective than the original Dueling-IP and DQN-IP algorithms in an urban simulation environment. Our independent policy algorithm has similar effects as the shared policy but with the significant advantage of running in a communication denial environment.