Sharing Experience for Behavior Generation of Real Swarm Robot Systems Using Deep Reinforcement Learning

2019 ◽  
Vol 31 (4) ◽  
pp. 520-525 ◽  
Author(s):  
Toshiyuki Yasuda ◽  
Kazuhiro Ohkura ◽  
◽  
Keyword(s):  
Reinforcement Learning ◽  
Collective Behavior ◽  
Design Methodology ◽  
Learning Performance ◽  
Centralized Control ◽  
Robot System ◽  
Swarm Robots ◽  
Robot Systems ◽  
Swarm Robot ◽  
Typical Design

Swarm robotic systems (SRSs) are a type of multi-robot system in which robots operate without any form of centralized control. The typical design methodology for SRSs comprises a behavior-based approach, where the desired collective behavior is obtained manually by designing the behavior of individual robots in advance. In contrast, in an automatic design approach, a certain general methodology is adopted. This paper presents a deep reinforcement learning approach for collective behavior acquisition of SRSs. The swarm robots are expected to collect information in parallel and share their experience for accelerating their learning. We conducted real swarm robot experiments and evaluated the learning performance of the swarm in a scenario where the robots consecutively traveled between two landmarks.

scholarly journals Behavior-Based Formation Control of Swarm Robots

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Dongdong Xu ◽  
Xingnan Zhang ◽  
Zhangqing Zhu ◽  
Chunlin Chen ◽  
Pei Yang
Keyword(s):  
Formation Control ◽  
Large Scale ◽  
Research Field ◽  
Initial Formation ◽  
Swarm Robots ◽  
Robot Systems ◽  
Potential Applications ◽  
Behavior Based ◽  
Swarm Robot ◽  
Searching Method

Swarm robotics is a specific research field of multirobotics where a large number of mobile robots are controlled in a coordinated way. Formation control is one of the most challenging goals for the coordination control of swarm robots. In this paper, a behavior-based control design approach is proposed for two kinds of important formation control problems: efficient initial formation and formation control while avoiding obstacles. In this approach, a classification-based searching method for generating large-scale robot formation is presented to reduce the computational complexity and speed up the initial formation process for any desired formation. The behavior-based method is applied for the formation control of swarm robot systems while navigating in an unknown environment with obstacles. Several groups of experimental results demonstrate the success of the proposed approach. These methods have potential applications for various swarm robot systems in both the simulation and the practical environments.

scholarly journals A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 680
Author(s):  
Qiuzhen Wang ◽  
Hai Zhang
Keyword(s):  
Completion Time ◽  
Robotic System ◽  
Area Coverage ◽  
Experimental Results ◽  
Experimental Simulation ◽  
Robot System ◽  
Swarm Robots ◽  
Swarm Robot ◽  
Self Organizing

The openness of the environment brings great challenges to the swarm robotic system to cover the task area quickly and effectively. In this paper, a coverage method based on gradient and grouping (GGC) is proposed. What is novel about our proposed solution is that it is suitable for extremely simple robots that lack computing or storage power. Through the change of the robot gradient, the swarm robot system with very simple functions can effectively self-organize to cover the unknown task area. By grouping the swarm robots, each group can cover the task area in parallel, which greatly improves the coverage speed. We verified our proposed method through experimental simulation and found that the gradient and grouping-based method in this paper was superior to other methods in terms of coverage, coverage completion time, and other aspects. Simultaneously, the robustness of the proposed method is analyzed and admirable experimental results are obtained. Because the applicable robot is very simple, the method in this paper can be applied to the submillimeter swarm robot system, which will lay the foundation for micro medicine.

scholarly journals Generating Legible and Glanceable Swarm Robot Motion through Trajectory, Collective Behavior, and Pre-attentive Processing Features

2021 ◽  
Vol 10 (3) ◽  
pp. 1-25
Author(s):  
Lawrence H. Kim ◽  
Sean Follmer
Keyword(s):  
Swarm Intelligence ◽  
Collective Behavior ◽  
Optimal Solution ◽  
Robot Motion ◽  
Vision Science ◽  
Swarm Robots ◽  
Different Types ◽  
Behavior Based ◽  
Swarm Robot

As swarm robots begin to share the same space with people, it is critical to design legible swarm robot motion that clearly and rapidly communicates the intent of the robots to nearby users. To address this, we apply concepts from intent-expressive robotics, swarm intelligence, and vision science. Specifically, we leverage the trajectory, collective behavior, and density of swarm robots to generate motion that implicitly guides people’s attention toward the goal of the robots. Through online evaluations, we compared different types of intent-expressive motions both in terms of legibility as well as glanceability, a measure we introduce to gauge an observer’s ability to predict robots’ intent pre-attentively. The results show that the collective behavior-based motion has the best legibility performance overall, whereas, for glanceability, trajectory-based legible motion is most effective. These results suggest that the optimal solution may involve a combination of these legibility cues based on the scenario and the desired properties of the motion.

Swarm Robot System for Underwater Communication Network

2014 ◽  
Vol 18 (5) ◽  
pp. 769-775 ◽  
Author(s):  
Ryan Rhay P. Vicerra ◽  
◽  
Elmer P. Dadios ◽  
Argel A. Bandala ◽  
Laurence A. Gan Lim
Keyword(s):  
Communication Network ◽  
Wireless Communication ◽  
Social Insects ◽  
Collective Behavior ◽  
Underwater Communication ◽  
Robot System ◽  
Wireless Communication Network ◽  
Underwater Environment ◽  
Swarm Robot ◽  
Underwater Wireless Communication

This paper presents a swarm robot simulator for implementing underwater wireless communication network. Swarm intelligence is based on the collective behavior of social insects and animals such as ants, bees and others. In this paper, swarm was applied to overcome the challenges of transmitting data in a large underwater environment. A robot considered to be a member of the swarm acts as a simple “physical” carrier of the data, it moves until they converge and manage to form a link connecting the data transmitter and receiver. The system is developed, simulated and tested using a coded simulator.

Advanced swarm robots addressing innovative tasks such as assembly, search, rescue, mapping, communication, aerial and other original applications

2014 ◽  
Vol 41 (5) ◽  
pp. 408-412
Author(s):  
Richard Bloss
Keyword(s):  
Design Methodology ◽  
Content Type ◽  
Swarm Robots ◽  
Wide Range ◽  
Robot Technology ◽  
Swarm Robot ◽  
Practical Implications

Purpose – The purpose of this paper is to review some of the various worldwide projects to develop and apply innovative swarm-type robots to many challenging applications. Design/methodology/approach – An in-depth review of published information and interviews with researchers and developers of swarm robot technology were conducted. Findings – Swarm robots continue to be developed to match an ever-increasing number of interesting and innovative applications. Practical implications – Readers may be very surprised at the tasks that autonomous swarm robots can address and the developments that are underway to further extend the abilities of swarm robots. Originality/value – This paper is a review of a wide range of the latest swarm robot developments, innovations and applications.

scholarly journals Simulation and Analysis of Self-Replicating Robot Decision-Making Systems

Computers ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Andrew Jones ◽  
Jeremy Straub
Keyword(s):  
Lower Risk ◽  
System Construction ◽  
System Configuration ◽  
Operating Environment ◽  
Robot System ◽  
Robotic Exploration ◽  
Mission Success ◽  
Robot Systems ◽  
System Configurations

Self-replicating robot systems (SRRSs) are a new prospective paradigm for robotic exploration. They can potentially facilitate lower mission costs and enhance mission capabilities by allowing some materials, which are needed for robotic system construction, to be collected in situ and used for robot fabrication. The use of a self-replicating robot system can potentially lower risk aversion, due to the ability to potentially replenish lost or damaged robots, and may increase the likelihood of mission success. This paper proposes and compares system configurations of an SRRS. A simulation system was designed and is used to model how an SRRS performs based on its system configuration, attributes, and operating environment. Experiments were conducted using this simulation and the results are presented.

scholarly journals Reinforcement-Learning-Based Asynchronous Formation Control Scheme for Multiple Unmanned Surface Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 546
Author(s):  
Jiajia Xie ◽  
Rui Zhou ◽  
Yuan Liu ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Formation Control ◽  
Rapid Development ◽  
Gradient Algorithm ◽  
Robot System ◽  
Physical Relationship ◽  
Unmanned Surface Vehicles ◽  
Main Challenge ◽  
Control Scheme ◽  
Multi Robot

The high performance and efficiency of multiple unmanned surface vehicles (multi-USV) promote the further civilian and military applications of coordinated USV. As the basis of multiple USVs’ cooperative work, considerable attention has been spent on developing the decentralized formation control of the USV swarm. Formation control of multiple USV belongs to the geometric problems of a multi-robot system. The main challenge is the way to generate and maintain the formation of a multi-robot system. The rapid development of reinforcement learning provides us with a new solution to deal with these problems. In this paper, we introduce a decentralized structure of the multi-USV system and employ reinforcement learning to deal with the formation control of a multi-USV system in a leader–follower topology. Therefore, we propose an asynchronous decentralized formation control scheme based on reinforcement learning for multiple USVs. First, a simplified USV model is established. Simultaneously, the formation shape model is built to provide formation parameters and to describe the physical relationship between USVs. Second, the advantage deep deterministic policy gradient algorithm (ADDPG) is proposed. Third, formation generation policies and formation maintenance policies based on the ADDPG are proposed to form and maintain the given geometry structure of the team of USVs during movement. Moreover, three new reward functions are designed and utilized to promote policy learning. Finally, various experiments are conducted to validate the performance of the proposed formation control scheme. Simulation results and contrast experiments demonstrate the efficiency and stability of the formation control scheme.

scholarly journals Research Progress on Synergistic Technologies of Agricultural Multi-Robots

2021 ◽  
Vol 11 (4) ◽  
pp. 1448
Author(s):  
Wenju Mao ◽  
Zhijie Liu ◽  
Heng Liu ◽  
Fuzeng Yang ◽  
Meirong Wang
Keyword(s):  
Path Planning ◽  
Formation Control ◽  
Research Progress ◽  
Hybrid Architecture ◽  
Labor Costs ◽  
Robot System ◽  
System Architectures ◽  
Research Results ◽  
Robot Systems ◽  
Multi Robot

Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.

A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments

2021 ◽  
Author(s):  
Ching-Wei Chuang ◽  
Harry H. Cheng
Keyword(s):  
Bayesian Networks ◽  
Task Allocation ◽  
Low Cost ◽  
Dynamic Environments ◽  
Modern World ◽  
Robot System ◽  
Novel Approach ◽  
Robot Systems ◽  
Robot Task ◽  
Multi Robot

Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed various algorithms or approaches to solve MRTA problems in different multi-robot systems, it is still difficult to overcome certain challenges, such as dynamic environments, changeable task information, miscellaneous robot abilities, the dynamic condition of a robot, or uncertainties from sensors or actuators. In this paper, we propose a novel approach to handle MRTA problems with Bayesian Networks (BNs) under these challenging circumstances. Our experiments exhibit that the proposed approach may effectively solve real problems in a search-and-rescue mission in centralized, decentralized, and distributed multi-robot systems with real, low-cost robots in dynamic environments. In the future, we will demonstrate that our approach is trainable and can be utilized in a large-scale, complicated environment. Researchers might be able to apply our approach to other applications to explore its extensibility.

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