Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path planning, and collision avoidance), but also need to cooperate in order to pursue and catch the evaders efficiently. In this paper, a novel approach based on a bioinspired neural network is proposed for the real-time cooperative hunting by multirobots, where the locations of evaders and the environment are unknown and changing. The bioinspired neural network is used for cooperative pursuing by the multirobot team. Some other algorithms are used to enable the robots to catch the evaders efficiently, such as the dynamic alliance and formation construction algorithm. In the proposed approach, the pursuing alliances can dynamically change and the robot motion can be adjusted in real-time to pursue the evader cooperatively, to guarantee that all the evaders can be caught efficiently. The proposed approach can deal with various situations such as when some robots break down, the environment has different boundary shapes, or the obstacles are linked with different shapes. The simulation results show that the proposed approach is capable of guiding the robots to achieve the hunting of multiple evaders in real-time efficiently.

Bioinspired Neural Network for Real-Time Cooperative Hunting by Multirobots in Unknown Environments

A multi-AUV cooperative hunting method in 3-D underwater environment with obstacle

Robots coordinate among themselves to select one of them to respond to an event reported to one of robots. The goal is to minimize the communication cost of selecting best robot, response time, and cost of performing the task. Existing solutions are either centralized, neglecting communication cost, assuming complete graph, or based on flooding with individual responses to robot decision maker (simple auction protocol), ignoring communication cost and response time bound. This article proposes auction aggregation protocols for task assignment in multi-hop wireless robot networks. Robot collector leads an auction and initiates response tree construction by transmitting search message. Each robot, after receiving the message, makes decision on whether to retransmit search message, based on the estimated response cost of its robots up to k -hops away. Robots wait to receive the bids from its children in the search tree, aggregates responses by selecting the best bid, and forward it back toward the robot collector (auctioning robot). When distance is used as sole cost metrics, traversal aggregation algorithm (RFT --- routing toward the event with traversal of face containing the event) can be applied and is an optimal solution. Advantage of our auction aggregation over simple auction protocol is shown by simulation results.

Auction Aggregation Protocols for Wireless Robot-Robot Coordination

Based on task allocation of multi-AUV (Autonomous Underwater Vehicle) system, the innovative auction algorithm is applied to hunting task assignments of multi-AUV. The two most decisive factor in the auction algorithm: the election of the auctioneer and the calculation of bidding value. In the traditional auction algorithm, the auctioneer's choice follows the principle of "Who finds the target, who is the auctioneer", but this principle seriously threatens the security and robustness of auction process of the multi-AUV. We propose a new method to determine auctioneer and the method of determining the bidding value of multiple AUV in the auction algorithm, and we have made an essential optimization of the auction algorithm. The final experimental results show that the optimized auction algorithm is compared with the traditional auction algorithm and other hunting tasks allocation algorithms, the security and robustness of the innovative auction has a better effect.

Task Allocation of Multi-AUVs Based on Innovative Auction Algorithm

We study the leader—following consensus stability and stabilization of networked multi-teleoperator systems with interval time-varying communication delays. With the construction of a suitable Lyapunov—Krasovskii functional and the utilization of the reciprocally convex approach, novel delay-dependent consensus stability and stabilization conditions for the systems are established in terms of linear matrix inequalities, which can easily be solved by various effective optimization algorithms. One illustrative example is given to illustrate the effectiveness of the proposed methods.

Leader—following consensus control for networked multi-teleoperator systems with interval time-varying communication delays

Within the frameworks of the well-known contract net protocol, a task allocation method for multi-robot cooperative hunting behavior is proposed based on an improved auction algorithm. Previous allocation methods using ordinary auction algorithms might turn out to be unsuitable when targets keep moving. Furthermore, the ordinary auctions might lead to unfavorable releases of the captured targets, because the hunters which were formerly assigned to relative tasks might be designated to some other tasks in a new round of bidding. In this paper an improved auction algorithm is proposed which may establish an ordered task list and holds biddings for each task in the list repeatedly. Therefore the allocation methods can be modified which allow dealing with a considerable volume of tasks in a dynamic environments. In addition, this approach holds no biddings for the accomplished tasks, and the relative hunters will keep guarding on their previously assigned target. The improvements also save the computational cost. Simulations demonstrate the flexibility and the efficiency of the method and the hunting task can be achieved in nearly half the time of the original methods.

Task allocation for multi-robot cooperative hunting behavior based on improved auction algorithm

Scale-free networks and consensus behaviour among multiple agents have both attracted much attention. To investigate the consensus speed over scale-free networks is the major topic of the present work. A novel method is developed to construct scale-free networks due to their remarkable power-law degree distributions, while preserving the diversity of network topologies. The time cost or iterations for networks to reach a certain level of consensus is discussed, considering the in∞uence from power-law parameters. They are both demonstrated to be reversed power-law functions of the algebraic connectivity, which is viewed as a measurement on convergence speed of the consensus behaviour. The attempts of tuning power-law parameters may speed up the consensus procedure, but it could also make the network less robust over time delay at the same time. Large scale of simulations are supportive to the conclusions.

/pdf/convergence-speed-of-consensus-problems-over-undirected-21gg4ex2wz.pdf

Convergence speed of consensus problems over undirected scale-free networks

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Sun Wei

Author Tools

Chat about Author

Papers

Task allocation for multi-robot cooperative hunting behavior based on improved auction algorithm

Convergence speed of consensus problems over undirected scale-free networks