Neural-network-based formation control with collision, obstacle avoidance and connectivity maintenance for a class of second-order nonlinear multi-agent systems

Distributed Adaptive Formation Tracking for a Class of Uncertain Nonlinear Multiagent Systems: Guaranteed Connectivity Under Moving Obstacles

This article explores a guaranteed network connectivity problem during moving obstacle avoidance within a distributed formation tracking framework for uncertain nonlinear multiagent systems with range constraints. We investigate this problem based on a new adaptive distributed design using nonlinear errors and auxiliary signals. Within the detection range, each agent regards other agents and static or dynamic objects as obstacles. The nonlinear error variables for formation tracking and collision avoidance are presented, and the auxiliary signals in formation tracking errors are introduced to maintain network connectivity under the avoidance mechanism. The adaptive formation controllers using command-filtered backstepping are constructed to ensure closed-loop stability with collision avoidance and preserved connectivity. Compared with the previous formation results, the resulting features are as follows: 1) the nonlinear error function for the avoidance mechanism is considered an error variable, and an adaptive tuning mechanism for estimating the dynamic obstacle velocity is derived in a Lyapunov-based control design procedure; 2) network connectivity during dynamic obstacle avoidance is preserved by constructing the auxiliary signals; and 3) owing to neural networks-based compensating variables, the bounding conditions of time derivatives of virtual controllers are not required in the stability analysis.

Prescribed-time containment control of multi-agent systems subject to collision avoidance and connectivity maintenance

In this paper, the problem of prescribed-time containment control for a second-order multiple leader-follower systems (MLFSs) is studied, in where both collision avoidance and connectivity maintenance of the agents are considered. Firstly, an effective exponential potential field function (EAPF) with constraints based on the estimated distance is designed to achieve collision resistance and connectivity preservation of the agents at a prescribed-time. Secondly, an estimator-based distributed control protocol is proposed, which drives the agents to achieve containment control in a cooperative manner at a prescribed-time. Furthermore, a novel distributed control protocol containing a collision avoidance term and a containment control term is addressed as well, which enables all followers to complete collision avoidance and connectivity maintenance in any prescribed-time and enter the leaders' convex packet. Finally, the stability of the system is technically analyzed by using Lyapunov theory, and the effectiveness of the presented strategies is verified by several simulations.

Formation with Non-Collision Control Strategies for Second-Order Multi-Agent Systems

This article tackles formation control with non-collision for a multi-agent system with second-order dynamics. The nested saturation approach is proposed to solve the well-known formation control problem, allowing us to delimit the acceleration and velocity of each agent. On the other hand, repulsive vector fields (RVFs) are developed to avoid collisions among the agents. For this purpose, a parameter depending on the distances and velocities among the agents is designed to scale the RVFs adequately. It is shown that when the agents are at risk of collision, the distances among them are always greater than the safety distance. Numerical simulations and a comparison with a repulsive potential function (RPF) illustrate the agents' performance.

Obstacle Boundary Point and Expected Velocity-Based Flocking of Multiagents with Obstacle Avoidance

Obstacle avoidance is a key technology of multiagents flocking control. However, most existing research studies assume that the agent can obtain global information about obstacles and have specific constraints on the shape and boundary of obstacles, which easily limit their practical applications. To relax these constraints, we assume that the agent can only perceive the position of obstacle boundary points within its sensing radius and propose an obstacle boundary point and an expected velocity-based flocking algorithm of multiagents with obstacle avoidance. In this algorithm, the attraction/repulsion potential is designed to avoid collisions between agents and between the agent and obstacle boundary points, the expected velocity is designed to tow the agent to move along the boundary of obstacles, and the virtual leader is designed to lead all agents to realize the group objective. Finally, the sufficient conditions that the agent does not collide are demonstrated, and the performance of the proposed algorithm is further verified through simulations.

Review of wheeled mobile robot collision avoidance under unknown environment

Recently, the working scenes of the robot have been emerging as diversity and complexity with gradually mature of robotic control technology. The challenge of robot adaptability emerges, especially in complicated and unknown environments. Among the numerous researches on improving the adaptability of robots, aiming at avoiding collision between robot and external environment, obstacle avoidance has drawn much attention. Compared to the global circumvention requiring the environmental information that is known, the local obstacle avoidance is a promising method due to the environment is possibly dynamic and unknown. This study is aimed at making a review of research progress about local obstacle avoidance methods for wheeled mobile robots (WMRs) under complex unknown environment in the last 20 years. Sensor-based obstacle perception and identification is first introduced. Then, obstacle avoidance methods related to WMRs' motion control are reviewed, mainly including artificial potential field (APF)-based, population-involved meta heuristic-based, artificial neural network (ANN)-based, fuzzy logic (FL)-based and quadratic optimization-based, etc. Next, the relevant research on Unmanned Ground Vehicles (UGVs) is surveyed. Finally, conclusion and prospection are given. Appropriate obstacle avoidance methods should be chosen based on the specific requirements or criterion. For the moment, effective fusion of multiple obstacle avoidance methods is becoming a promising method.