Multi Robot Swarm Systems
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The document discusses swarm robots, including different types defined by size, communication range and topology. It describes how swarm robotics uses large numbers of simple robots that can collectively solve problems through local interactions. Examples of tasks include transportation, search and rescue. Challenges include scalability and performing physical tasks. Modeling approaches include microscopic and macroscopic, with the latter being more computationally efficient.
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detecting and correspondences. The viability of the methodology has been checked with simulation, for a
set of swarm robots.
Introduction of Swarm Robotics and basics.pptx
Introduction of Swarm Robotics and basics.pptxnishantkumar919235 General Definition and introduction of course
A010430107
A010430107IOSR Journals This document describes a new approach for behavioral control of swarm robots using implicit communication. The key aspects of the proposed algorithm are:
1) Robots can operate in either a centralized or decentralized mode. In centralized mode, one robot acts as the leader and others follow. In decentralized mode, each robot plans its path individually.
2) Robots can switch between these modes depending on obstacles. If an obstacle disrupts communication between the leader and follower, they switch to decentralized mode.
3) Robots are equipped with sensors like IR sensors for obstacle avoidance and an IR seeker to navigate toward a goal. RF transceivers enable implicit communication between robots.
4) The algorithm was implemented using
drones-07-00269.pdf
drones-07-00269.pdfFateehaFatimaTurk A review article summarizes swarm robotics, including its key characteristics, behaviors inspired by nature, and applications. The article discusses simulating swarm behaviors and deploying them using robot hardware. It reviews the evolution of swarm robotics from early research to current applications and outlines future directions, with the goal of bridging theory and industrial practice.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOT
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
trouble solving for the real time environments. By observing the performance of groups of bees and ants
are working together has given to a rise to the ‘swarm intelligence concepts’. One of the most interesting
and new explore area of recent decades towards the impressive challenge of robotics is the design of
swarm robots that are self-independent and self intelligence one. This concept can be essential for robots
exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots.
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOT
BIO-INSPIRATIONS AND PHYSICAL CONFIGURATIONS OF SWARM-BOTJaresJournal Scientists and engineers in the whole time history have turned to nature for encouragement and dreams for
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exposed to environment that are shapeless or not easily available for an individual operator, such as a
distorted construction, the deep sea, or the surface of another planet. In this paper, we present a study on
the basic bio-inspirations of swarm and its physical configurations, such as reconfigurability, replication
and self-assembly. By introducing the swarm concepts through swarm-bot, which offers mainly
miniaturization with robustness, flexibility and scalability. This paper discusses about the various swarmbot intelligence, self-assembly and self-reconfigurability among the most important and capabilities as well
as functionality to swarm robots
A NOVEL PROTOTYPE MODEL FOR SWARM MOBILE ROBOT NAVIGATION BASED FUZZY LOGIC C...
A NOVEL PROTOTYPE MODEL FOR SWARM MOBILE ROBOT NAVIGATION BASED FUZZY LOGIC C...ijcsit Autonomous mobile robots have been used to carry out different tasks without continuous human guidance.
To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them.
Navigation means that the robot can move through the environment to reach a destination. Obstacles
avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient
technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured
environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles
position around them and store their positions in shared memory. By accessing the shared memory, the
other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea,
the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).
A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic C...
A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic C...AIRCC Publishing Corporation Autonomous mobile robots have been used to carry out different tasks without continuous human guidance. To achieve the tasks, they must be able to navigate and avoid different kinds of obstacles that faced them. Navigation means that the robot can move through the environment to reach a destination. Obstacles avoidance considers a challenge which robot must overcome. In this work, the authors propose an efficient technique for obstacles avoidance through navigation of swarm mobile robot in an unstructured environment. All robots cooperate with each other to avoid obstacles. The robots detect the obstacles position around them and store their positions in shared memory. By accessing the shared memory, the other robots of the swarm can avoid the detected obstacles when they face them. To implement this idea, the Authors used a MATLAB® and V-REP® (Virtual Robot Experimentation Platform).
Swarm Robotics An Overview
Swarm Robotics An Overviewijtsrd A robot swarm is essentially a decentralized multi robotics system that can collectively accomplish missions that a single robot could not achieve by itself. It has some unique characteristics that differentiate it from centralized multi robot systems. Swarm robotics is inspired by the swarming nature of insects and birds. It employs a large number of simple robots which can perform complex tasks in a more efficient way than a single robot. It consists of multi robotics in which large numbers of robots are coordinated in a distributed and decentralized way. The goal is to control a large number of simple robots to solve complex tasks. This paper presents an overview of swarm robotics and its applications, benefits, and challenges. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Swarm Robotics: An Overview" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-4 , June 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50035.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/50035/swarm-robotics-an-overview/matthew-n-o-sadiku
Adaptive Collective Systems - Herding black sheep
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The authors are academics working in various areas of a new rising field: adaptive collective systems.
Stuart Anderson (The University of Edinburgh, United Kingdom)
Nicolas Bredeche (Université Pierre et Marie Curie, France)
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Swarms Robots and their applications
Swarms Robots and their applicationsIOSRjournaljce Swarm robotics is a new approach to the coordination of multirobot systems which consist of large numbers of mostly simple physical robots In this paper, we will discuss this emerging filed, Swarm Robots. This filed has many applications. We will also discuss these applications in detail.
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Solution
Behavior-based control employs a set of distributed, in-teracting modules, called behaviors that
collectively achieve the desired system-level behavior. To an ex-ternal observer, behaviors are
patterns of the robot’s activity emerging from interactions between the robot and its
environment. To a programmer, behaviors are control modules that cluster sets of constraints in
order to achieve and maintain a goal. Each behavior receives inputs from sensors and/or other
behaviors in the system, and provides outputs to the robot’s actuators or to other behaviors. Thus,
a behavior-based controller is a structured network of interacting behaviors, with no centralized
world representation or focus of control. In-stead, individual behaviors and networks of
behaviors maintain any state information and models.
The basic principles of behavior-based control can be summarized briefly as follows:
• Behaviors are implemented as control laws (some-times similar to those used in control
theory), either in software or hardware, as a processing element or as a procedure.
• Each behavior can take inputs from the robot’s sen-sors (e.g., proximity sensors, range
detectors, contact sensors, camera) and/or from other modules in the
system, and send outputs to the robot’s effectors (e.g., wheels, grippers, arm, speech) and/or to
other modules.
• Many different behaviors may independently re- ceive input from the same sensors and output
action commands to the same actuators.
• Behaviors are encoded to be relatively simple, and are added to the system incrementally.
• Behaviors (or subsets thereof) are executed con- currently, not sequentially, in order to exploit
parallelism and speed of computation, as well as the interaction dynamics among behaviors and
between behaviors and the environment.
The ability to improve performance over time and to reason about the world, in the context of a
chang-ing and dynamic environment, is an important area of research in situated robotics. Unlike
in classical ma-chine learning, where the goal is typically to optimize performance over a long
period of time, in situated learning the aim is to adapt relatively quickly, toward
attaining efficiency in the light of uncertainty. Models from biology are often considered, given
its proper- ties of learning directly from environmental feedback. Variations and adaptations of
machine learning, and in particular reinforcement learning, have been effectively applied to
behavior-based robots, which have demon- strated learning to walk [38.
, navigate and create topological maps, di-vide tasks, behave socially , and even identify
opponents and score goals in robot soc-
cer. Methods from artificial life, evolutionary computation/genetic algorithms, fuzzy logic,
vision and learning, multi-agent systems, and many other research areas continue to be actively
explored and applied to behavior-based robots as their role in ani.
A Corpus Based Analysis Of The Application Of Concluding Transition Signals ...
A Corpus Based Analysis Of The Application Of Concluding Transition Signals ...Darian Pruitt This document summarizes the past, present, and future of swarm robotics. It discusses how swarm robotics was inspired by natural swarms like ants and birds. Early work in the late 1980s first introduced the concepts of cellular robotics and swarm intelligence. Research has focused on emulating behaviors like foraging, flocking, and cooperation through simple robot rules and local interactions. Current work involves various simulation and real-world applications. The future of swarm robotics may include more complex collective behaviors and larger swarm sizes for tasks like search and rescue missions.
IRJET- Swarm Robotics and their Potential to be Applied in Real Life Problems
IRJET- Swarm Robotics and their Potential to be Applied in Real Life ProblemsIRJET Journal This document discusses swarm robotics and its potential applications to real-life problems. It provides an overview of existing research on swarm robotics, which has successfully demonstrated complex collective behaviors like aggregation, pattern formation, and transportation in controlled laboratory environments. However, the document notes that more research is still needed to apply swarm robotics to solve real-world problems. It analyzes the tasks that have been studied in the context of swarm robotics, like aggregation, mapping and localization, and discusses how combining these tasks could help achieve practical applications of swarm robotics.
afin_2016_1_10_40014
afin_2016_1_10_40014Hanin Almutairi This document discusses using swarm intelligence algorithms to control node movement in mobile medium ad hoc networks (M2ANETs). It proposes applying an ant colony optimization (ACO) approach where nodes move based on pheromone trails left by recent data forwarding activity. Simulation results showed this approach significantly improved delivery rates in sparse networks, increasing them by up to 50% by directing nodes to locations with recent activity. The document provides background on M2ANETs, self-organizing systems, swarm intelligence, and existing algorithms like particle swarm optimization and ant colony optimization that were the inspiration for the new approach.
Distributed Robotics A Primer
Distributed Robotics A Primerijtsrd Distributed robotics has been the focus of attention in recent years. The idea of using a group of robots instead of a single one to execute a task came from the necessity of accomplishing a task that is too complex for a single robot. The use of multiple processing units lead to a distributed system within a single robot. Modern robotics systems are increasingly distributed, heterogeneous, and collaborative and can involve challenging computational tasks. A distributed approach can offer desirable advantages such as improved performance. This paper presents an introduction to distributed robots. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Distributed Robotics: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-4 , June 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50026.pdf Paper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/50026/distributed-robotics-a-primer/matthew-n-o-sadiku
Mobile robot1
Mobile robot1Anbarasu Athimoolam Autonomous robots can perform tasks without continuous human guidance by gaining information about their environment, working without intervention, moving throughout the environment, and avoiding harm. They have different types of locomotion and use sensory perception to navigate and identify goals. Knowledge representation allows them to model the world using prior and sensed information for planning navigation and completing missions. Evaluation considers their autonomy, interaction with humans, and potential for collaboration between robot teams.
MODEL PREDICTIVE CONTROL BASED JUMPINGOF ROBOTIC LEG ON A PARTICULAR HEIGHT U...
MODEL PREDICTIVE CONTROL BASED JUMPINGOF ROBOTIC LEG ON A PARTICULAR HEIGHT U...IRJET Journal 1. The document discusses using model predictive control and reinforcement learning to teach a robotic leg to jump a certain height. Model predictive control is used to regulate the leg's dynamics like torque and angles, while reinforcement learning helps the leg adapt through trial and error.
2. Reinforcement learning algorithms like PPO and A2C are applied to give feedback based on successful or unsuccessful jumps. This helps the robotic leg learn over time to precisely jump the target height.
3. Legged robots have an advantage over wheeled robots in navigating uneven terrain. Quadruped robots like Boston University's Mini Cheetah can move quickly over varied surfaces using model predictive control of its leg actuators and sensors.
Design & Development of Vision Controlled Snake Robot
Design & Development of Vision Controlled Snake Robotvivatechijri A snake is the only reptile which has the ability to conquer harsh terrains like rock and sand with apparent ease. This project highlights the design, development and testing of a snake-like robot prototype. The snake robot offers high stability than any other wheeled devices. It simulates the serpentine motion of a snake and is controlled by the keyfob transmitter and receiver. The motion commands to snake robot are delivered by four button remote control. The brain of the snake robot is an Arduino microcontroller board which has a wireless camera connected with the board which is placed in the front head portion of the snake and therefore it possesses the ability to map and navigate in its surroundings and also to find the possibility of human life. The applications of these kinds of robots are mainly in space exploration, disaster management, surveillance, etc.
A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic C...
A Novel Prototype Model for Swarm Mobile Robot Navigation Based Fuzzy Logic C...AIRCC Publishing Corporation
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Multi Robot Swarm Systems
- 1. Prepared By: Reza Madankan Ting-Chiun Chung
- 2. Introduction to Swarm Robots Different types of swarm robots Swarm Specifications Used methods in Swarm modeling Future Challenges Examples References
- 4. Self-organizing natural systems Social insect systems: ants, termites, wasps, bees, cockroaches, locusts… Animals with social behaviors: penguins, birds, fish, sheep... Artificial self-organizing systems Amorphous computing
- 5. Definition: a Swarm consists of (i) a large number of (ii) homogenous (iii) autonomous (iv) relatively incapable or inefficient robots with (v) local sensing and communication capabilities. In other words, Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between the agents and the environment.
- 6. Transportation Search & Rescue Mine Detection Surveillance & Monitoring Medical Service using small-size robots Military . . .
- 7. A larger range of task domain Greater efficiency Improved system performance Fault tolerance Robustness Lower economic cost
- 9. Swarm Robots are classified to different types according to the following criteria: Swarm Size Pair 2 Robots Limited Group multiple Robots Infinite Group n>>1 Robots Communication Range None-Communicative Robots Near-Communicative Robots Infinite communicative Robots Communication Topology Top-Broad Every robot can communicate with all of the other robots, but it is not able to send a message to a particular robot Top-Address Every robot can communicate with any arbitrary robot by name or address Top-Tree Robots are linked in a tree & may only communicate through this hierarchy Top-Graph Robots are linked in a general graph
- 11. What is Reconfigurability? Reconfigurability is the rate at which the swarm can spatially reorganize itself. Reconfigurability is equivalent to the rate at which members can move with respect to one another .
- 12. Ability to operate under a wide range of group sizes. In other words, coordination mechanisms are rather independent of the number of individuals in the group.
- 13. Swarm Robots can continue to operate despite large disturbances because of: Redundancy Simplicity of the individuals
- 15. Microscopic Treats the robot as the fundamental unit of the model. Describes the robot’s interactions with other robots & the environment. Macroscopic Directly describes the collective behavior of the robotic swarm. Computationally efficient because of fewer variables
- 16. - Technical challenges to scalability - Performing physical tasks in the real world
- 17. Chain Formation Aggregation Obstacle Avoidance & Beacon Navigation Dispersing Self Assembly
- 19. The communication and robot location system iRobot ISIS allow each robot to commute with its neighbors and determined their range, bearing, and orientation
- 20. Primitive Behaviors These low-level behaviors do not interact with other robots at all. They provide low-level motion control and obstacle avoidance for an individual robot. EX: move or stop a robot Pair Behaviors Pair behaviors also direct the actions of a single active robot, but they use the position and current state of only one neighbor that is the reference robot. EX: Orient to another robot, fellow a robot Group Behaviors These behaviors form the bulk of the behavior library. They are responsible for guiding the actions of a single active robot based on the positions and current state of all of its neighbors. The entire set of neighbors are the reference robots. EX: Follow a leader, clustering, grouping.
- 21. In Chain Formation problem, the aim is to move the robots so that they form a chain pattern. The follow The Leader behavior dynamically constructs an ordered line of robots. This line is suitable for leading a group of robots into an area. Another behavior is required to control the leader.
- 22. + The goal of the clustering behavior is to move the swarm to a centralized location in as small an area as possible. + The clusterIntoGroups behavior implements a primitive form of division of labor. It operates in two `steps; first, each robot selects a group to join, then the behavior moves robots in the same groups together, while moving entire groups away from each other.
- 27. “ A Taxonomy for Swarm Robots”, G. Dudek! M. Jenkinj E. Milios! and D. Wilkest “ A Review of Studies in Swarm Robotics”, L. Bayindir, E. Sahin. “ A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems”, K. Lerman, A. Martinoli, A. Galstyan. - “Stupid Robot Tricks: A Behavior-Based Distributed Algorithm Library for Programming Swarms of Robots”, J. McLurkin - “Swarm Robotics: From sources of inspiration to domains of application”, E. Sahin.