scholarly journals An emotional model for swarm robotics

2019 ◽  
Vol 22 (3) ◽  
Author(s):  
Angel Gil ◽  
Eduardo Puerto ◽  
Jose Aguilar ◽  
Eladio Dapena
Keyword(s):  
Swarm Robotics ◽  
Recognition Algorithm ◽  
Emergent Behavior ◽  
Robot System ◽  
Level Of Satisfaction ◽  
Emotional Model ◽  
Object Transportation ◽  
The Individual ◽  
Emergent Behaviors ◽  
Multi Robot

Swarm robotics is a system of multiple robots where a desired collective behavior emerges from the interactions between the robots and with the environment. This paper proposes an emotional model for the robots, to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These emotions lie on a spectrum where depending where the emotion of the robot lies, can affect its behavior and of its neighboring robots. The more negative the emotion is, the more individualistic it becomes in its decisions. The more positive the robot is in its emotion, the more it will consider the group and global goals. Each robot is able to recognize another robot's emotion in the system based on their current state, using the AR2P recognition algorithm. Specifically, the paper addresses emotions’ influence on the behavior of the system, at the individual and collective levels, and the emotions’ effects on the emergent behaviors of the multi-robot system. The paper analyses two emergent scenarios: nectar harvesting and object transportation, and shows the importance of the emotions into the emergent behavior in a multi-robot system

scholarly journals Emotional model for a multi-robot system with emergent behavior

2020 ◽  
Vol 9 (3) ◽  
pp. 220
Author(s):  
Angel Gil ◽  
Jose Aguilar ◽  
Eladio Dapena ◽  
Rafael Rivas
Keyword(s):  
Emotional State ◽  
General Purpose ◽  
Emergent Behavior ◽  
The Other ◽  
Collective Actions ◽  
Robot System ◽  
Basic Emotions ◽  
Emotional Process ◽  
Emotional Model ◽  
Multi Robot

<p>This article describes an emotional model for a general-purpose robot operating in a multi-robot system with emergent behavior. The model considers four basic emotions: anger, rejection, sadness and joy, plus  a neutral emotional state, which affect the behavior of the robot,  both individually and collectively. The emotional state of each robot in  the system is constructed through the conjunction of a series of factors related to their individual and collective actions, which are: safety, load, acting and interaction, which serve as input to an emotional process that results in an index of satisfaction of the robot that establishes the emotional state in which it is in a certain moment. The emotional state of a robot influences its interactions with the other robots and with the environment, that is, it determines its emergent behavior in the system. This paper  presents the design of this model, and establishes some considerations for its implementation.</p>

scholarly journals TWO-LAYERS WORKSPACE: A NEW APPROACH TO COOPERATIVE OBJECT TRANSPORTATION WITH OBSTACLE AVOIDANCE FOR MULTI-ROBOT SYSTEM

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Stephanie Kamarry ◽  
Raimundo Carlos S. Freire ◽  
Elyson A. N. Carvalho ◽  
Lucas Molina ◽  
Phillipe Cardoso Santos ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Robot System ◽  
New Approach ◽  
Object Transportation ◽  
Multi Robot

scholarly journals Confronting Functional Complexity Failures: The Case of a Runway Excursion in Munich

2019 ◽  
Vol 63 (1) ◽  
pp. 116-120
Author(s):  
Robert Mauro ◽  
Lance Sherry
Keyword(s):  
Complex Systems ◽  
Emergent Behavior ◽  
Operating Region ◽  
Geographically Distributed ◽  
Functional Complexity ◽  
The Individual ◽  
Emergent Behaviors ◽  
Normal Accidents

Complex systems often produce unanticipated emergent behavior as a result of the interactions between behaviorally complex sub-systems or agents. The sub-systems may be human or artificial. They may be co-located or geographically distributed and operate autonomously. Although the individual sub-systems may be tested and certified for high levels of reliability (e.g. 10-7), interactions between the sub-systems may occur so that emergent behaviors allow the system to migrate into an unsafe operating region. This may occur even when all of the sub-systems are behaving nominally and no equipment has failed. This phenomenon is called a “functional complexity failure.” In this paper, we present an analysis of a functional complexity failure that resulted in a runway excursion and discuss the options for detecting and mitigating the conditions for these “normal accidents” before the accident occurs.

Multi-Robot Cooperative Transportation of Objects Using Modified Q-Learning

2010 ◽  
Author(s):  
Pallege Gamini Dilupa Siriwardana ◽  
Clarence de Silva
Keyword(s):  
Autonomous Robots ◽  
Dynamic Environment ◽  
Learning Approach ◽  
Robot System ◽  
Q Learning ◽  
Time Performance ◽  
Object Pose Estimation ◽  
Object Transportation ◽  
Blob Tracking ◽  
Multi Robot

In cooperative multi-robot object transportation, several autonomous robots navigate cooperatively in either a static or a dynamic environment to transport an object to a goal location and orientation. The environment may consist of both fixed and removable obstacles and it will be subject to uncertainty and unforeseen changes within the environment. More than one robot may be required for handling heavy and large objects. This paper presents a modified Q-learning approach for object transportation utilizing cooperative and autonomous multiple mobile robots. A modified version of Q-learning is presented, which employs for effective robot coordination. A solution to the action selection conflicts of the robots is presented, which helps to improve the real time performance and robustness of the system. As required in the task, the paper presents an algorithm for object pose estimation, by utilizing the laser range finder and color blob tracking. The developed techniques are implemented in a multi-robot system in laboratory. Experimental results are presented to demonstrate the effectiveness of the developed multi-robot system and its underlying methodologies.

Fuzzy coordination through measure information sharing of multi-robot system: A case study

2021 ◽  
pp. 1-8
Author(s):  
Xuefeng Dai ◽  
Jiazhi Wang ◽  
Dahui Li ◽  
Yanchun Wang
Keyword(s):  
Robot System ◽  
Fuzzy Membership Functions ◽  
System A ◽  
Parameter Update Law ◽  
Robot Systems ◽  
Potential Applications ◽  
The Individual ◽  
Logic Reasoning ◽  
Multi Robot

Multi-robot systems have many potential applications; however, the available results for coordination were based on qualitative information. Fuzzy logic reasoning has a feature of human being thinking, so a novel coordinated algorithm is proposed. The algorithm utilizes sharing sensing information of rooms and semantic robots to coordinating robots in a structured environment exploration. The approach divides all teammate robots into two classes according to robot exploration performance, and divides rooms into large, medium and small ones according to estimations of the individual areas. On the purpose of minimizing exploration time of the system, the reasoning coordination assigns large room to good performance robot, and vice versa. A parameter update law is introduced for fuzzy membership functions. Finally, the results are validated by computer simulations for a structured environment.

Mobile Multi-Robot Control in Target Search and Retrieval

2008 ◽  
Author(s):  
Guoxian Zhang ◽  
Devendra P. Garg
Keyword(s):  
Task Performance ◽  
Robot Control ◽  
Swarm Robotics ◽  
Retrieval System ◽  
Robot System ◽  
Target Search ◽  
Transition Mechanism ◽  
Environment Exploration ◽  
Search And Retrieval ◽  
Multi Robot

In this paper, the design of a controller is proposed for a multi-robot target search and retrieval system. Inspired by research in insect foraging and swarm robotics, we developed a transition mechanism for the multi-robot system. Environmental information and task performance obtained by the robot system are used to adjust individual robot’s parameters and guide environment exploration. The proposed control system is applicable in the solution of multi-target problem also where several robots may be needed to cooperate together to retrieve a large target. Simulations show that the task performance improves significantly with the proposed method by sharing information in parameter learning and environment exploration.

Object Transportation Using a Cooperative Mobile Multi-Robot System

2016 ◽  
Vol 14 (3) ◽  
pp. 1184-1191 ◽  
Author(s):  
A.G. Barrientos ◽  
J.L. Lopez ◽  
E.S. Espinoza ◽  
J. Hoyo ◽  
G. Valencia
Keyword(s):  
Robot System ◽  
Object Transportation ◽  
Multi Robot

scholarly journals Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1499
Author(s):  
Daegyun Choi ◽  
Donghoon Kim
Keyword(s):  
Fuzzy Inference ◽  
Target Position ◽  
Inequality Constraints ◽  
Robot System ◽  
Inference System ◽  
Genetic Fuzzy System ◽  
Object Transportation ◽  
The Stability ◽  
Inference Systems ◽  
Multi Robot

Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots. A multi-robot system would be a good option for such a transportation task because it can carry massive objects and provide better system reliability and redundancy when compared to a single robot system. This paper proposes an intelligent and decentralized approach for the multi-robot system using a genetic fuzzy system to perform an object transportation mission that not only minimizes the total travel distance of the multi-robot system but also guarantees the stability of the whole system in a rough terrain environment. The proposed fuzzy inference system determines the multi-robot system’s input for transporting an object to a target position and is tuned in the training process by a genetic algorithm with an artificially generated structured environment employing multiple scenarios. It validates the optimality of the proposed approach by comparing the training results with the results obtained by solving the formulated optimal control problem subject to path inequality constraints. It highlights the performance of the proposed approach by applying the trained fuzzy inference systems to operate the multi-robot system in unstructured environments.

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.

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