Multi-robot Cooperative Control Research Articles

Finite-Time Neuroadaptive Cooperative Control for Nonlinear Multiagent Systems Under Nonaffine Faults and Partially Unknown Control Directions.

This article investigates the cooperative control of complex nonlinear multiagent systems (CNMASs), in which the agents suffer from nonaffine faults and the control directions of some agents are unknown. A finite-time adaptive control scheme is presented for the CNMASs. A finite-time command filter is designed to solve the "explosion of complexity" issues, overcome chattering issues, and relax the limitations of the filter input. The impact of filter errors is alleviated by an improved error compensation mechanism. Based on piecewise Nussbaum functions, the partially unknown control direction is addressed. The proposed finite-time cooperative control strategy on the basis of local information can ensure that all signals in the closed-loop system are finite-time bounded, and the absolute value of the cooperative control errors can converge to a given upper bound in a finite time. The rapidity and robustness of the proposed method are verified by two comparative simulation examples. A real multirobot cooperative control experiment is used to verify the effectiveness of the presented method.

End-to-end decentralized formation control using a graph neural network-based learning method.

Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline design, and the separation of perception and controls may cause processing latencies and compounding errors that affect control performance. End-to-end learning overcomes this limitation by implementing direct learning from onboard sensing data, with control commands output to the robots. Challenges exist in end-to-end learning for multi-robot cooperative control, and previous results are not scalable. We propose in this article a novel decentralized cooperative control method for multi-robot formations using deep neural networks, in which inter-robot communication is modeled by a graph neural network (GNN). Our method takes LiDAR sensor data as input, and the control policy is learned from demonstrations that are provided by an expert controller for decentralized formation control. Although it is trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates the triangular formation behavior of multi-robot teams of different sizes under the learned control policy.

Multirobot searching method of natural gas leakage sources on offshore platform using ant colony optimization

Natural gas leakage on offshore platforms has a great impact on safety production, and effective and reasonable leakage detection methods can prevent the harm caused by natural gas leakage. This article proposes a method based on ant colony optimization (ACO) for multirobots to collaboratively search for leaking natural gas sources on offshore platforms. First, analyze the structure and environment of the offshore platform, use Fluent software to simulate the diffusion process of natural gas leaked from the platform, and establish a diffusion model of natural gas leaked from various aspects, such as the layout of different platforms, the number of leaked gas sources, and the concentration of leaked gas sources. In terms of multirobot cooperative control, we analyzed and improved the ant colony algorithm and proposed a multirobot cooperative search strategy for gas search, gas tracking, and gas source positioning. The multirobot search process was simulated using MATLAB software, and the robot on the detection effect of multirobots was analyzed in many aspects, such as quantity, location of leak source, and a number of leak sources, which verified the feasibility and effectiveness of the multirobot control strategy based on optimized ACO. Finally, we analyze and compare the two control algorithms based on ACO and cuckoo search algorithm (CSA). The results show that the ACO-based multirobot air source positioning effect is significantly better than CSA.

Multi-robot Cooperation Strategy Based on Wireless Sensor Network

To realize the design of multi-robot cooperative control based on wireless sensor networks, a large number of local control research experiments are carried out on a specific model of mobile robots. Combined with the kinematic model of mobile robot, the motion characteristics of this kind of mobile robot are grasped. On the basis of realizing the multi-target tracking and positioning of wireless sensor network, a multi mobile robot cooperative control system based on the feedback of wireless sensor network is developed, and the synchronous motion between the master and the robot is maintained through the feedback control of the network. Finally, a cooperative trajectory tracking control algorithm for mobile robot is introduced, and the principle of the controller design is mastered. A multi mobile robot cooperative trajectory tracking LQG controller is designed for mobile robot in wireless sensor network. The simulation results show that the algorithm can reach the cooperative control target.

Distributed estimation and control of multi-agent systems in uncertain environment

Due to the wide applications in wireless sensor networks, multi-robot cooperative control and satellite formation flying, etc., distributed control and estimation of multi-agent systems (MASs) are now a hot topic in the system control community. In practice, when carrying out distributed tasks, MASs are often subject to various uncertainties, which can make significant macro-influence to the whole systems via affecting each agents microscopic decisions. The interaction among uncertainties and distributed information pattern may bring essential difficulty to the design and analysis of MASs. This paper is aimed at seeking more robust and more effective distributed estimation and control protocols to achieve global estimation and control tasks under various uncertainties including stochastic noises, signal loses, quantization errors and unknown system structures, etc. Specifically, distributed estimation, distributed control and distributed adaptive control are investigated.

Experimental Study of Concensus Algorithm on a Group of Humanoid Robots

Synchronized motion of robot arms can be achieved by consensus algorithm. This paper presents the design of hardware experimental platfomr to implement consensus algorithm on a group of humanoid robot. Some experiments have been conducted under various interaction topologies to explore issues and challenges in the field of distributed multi-robot cooperative control. Distributed consensus algorithm are applied to synchronize the arm's movement of some robots. In this synchronization task, a group of robot will perform some identical movements in unison. Consensus algorithm mainly used in this paper has been proposed by Wei Ren et al. The hardware experimental platform is inspired by the works of Benedetty et al. Some experiment results using this platform show that the consensus algorithm is effective and robust even in a condition in which the robots have various different initial position values.

A FAST AND ROBUST ALGORITHM FOR COLOR-BLOB TRACKING IN MULTI-ROBOT COORDINATED TASKS

In this paper, a fast and robust algorithm is presented for color-blob tracking, which is applicable in a multi-robot cooperative control system. The algorithm, which is immune to uneven illumination, identifies the current poses (positions and orientations) of a robot and the manipulated object (a rectangle box) from a color image, in real time. Two main challenges are faced in the multi-robot task considered in the paper. The first one concerns the response speed of the vision subsystem. The second challenge comes from uneven lighting, which makes it very difficult for the vision subsystem to trace a specific color blob in different positions. A fast computer vision algorithm is presented to cope with these challenges. First, an image in the RGB (Red-Green-Blue) color space is converted into the HSI (Hue-Saturation-Intensity) color space. Then the Saturation and the Intensity components of the image are removed and only the Hue component is retained. Second, filtering and template matching technologies are employed to remove the disturbances from the background and other objects in the image. Finally, coordinate transformations are used to reconstruct the poses of the robot and the object when they are moving. A multi-robot route planning approach is presented, which uses the information acquired by the color-blob tracking algorithm. The experimental results are presented to show the feasibility and the effectiveness of the algorithm.