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A* ALGORITHM BASICS FOR PATH FINDING & HEURISTICS METHODS : ARTIFICIAL INTELLIGENCE

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 A* ALGORITHM BASICS FOR PATH FINDING A* , widely used  known form of best-first search & path planning algorithm nowadays in mobile robots,games. this is the function for A*,                                     f(n) = g(n) + h(n) g ( n ) is the cost of the path from the start node to n , and h ( n ) is a heuristic function that estimates the cost of the cheapest path from n to the goal This will find cheapest f(n) value in neighbor nodes to archive goal node. check below image  A to B path finding with g(n),h(n),f(n) value In the final level check below image Now we will check the Algorithm // A* Search Algorithm 1. Initialize the open list 2. Initialize the closed list put the starting node on the open list (you can leave its f at zero) 3. while the open list is not empty a) find the node with the least f on the open list, call it "q" b) pop q off the open list c) generate q's 8 successors
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INTRODUCTION TO SWARM ROBOTICS

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Happy to write about swarm robots in my blog, keep on checking my blog guys more interesting stuffs are waiting for you , This is a turning point for robotics lovers & my first post about swarm robots
A short & quick Introduction about Swarm Robotics have a look

Swarm robotics is defined as,
"the study of how a 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"

Have a look below video to get an quick understanding about swarm robots,its from Big Hero 6  movie

 what are the special things in Swarm Robotics
  • scalable
  • system’s efficiency will not affect 
  • addition or removal of robots do not change the functioning or efficiency of a swarm robotic system
  • purely based on local sensing the system is adaptable and flexible
  • applicable to dynamic problem scenarios
  • single robot faults won’t affect the total system’s performance
  • decentralized manner purely based on local sensing
  • very low level computational power
  • most of the swarm robots adopt bio-inspired animal behavior 
  • local interaction with other robots
  • group task completing concept
  • very low level localization capability 
 what are the applications of Swarm Robotics
  • Search and Rescue : robots are used in search and rescue operations especially in disaster management
  • Area Mapping : Terrain Mapping refers to building a map of the unknown terrain using robots
  • Waste Management : some collecting mechanism are being used for removing wastes on the water bodies
let see what are interesting research area in Swarm Robotics
  • Cooperative Object Transportation : One of the scintillating skills of natural swarm system is its capability of transporting objects/prey to its nest, especially the ant colonies
  • Cooperative/single Forging : scalable, flexible, and efficient algorithm for robot swarms to collect objects in un-mapped environments
so next we will see what are the Collective behaviors in Swarm Robotics

  • Aggregation:Aggregation refers to the grouping of all robots at a common point



  • Collective Navigation and Exploration:Collective navigation refers to, a robot reaching a destination by moving through an unknown environment with the help of other robots



  • Flocking : In nature, different species of animals show this behavior. A flock of birds, fish schools, the formation of herds in ungulates etc. are some examples of natural flocks. Flocking of swarm robots, inspired by these natural scenarios, is the coordinated movement of robots towards a common goal or simple coordinated movement


major general methodologies for  Cooperative Object Transportation
  • Gripping
  • Caging
  • Pushing
 check below videos

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A* ALGORITHM BASICS FOR PATH FINDING & HEURISTICS METHODS : ARTIFICIAL INTELLIGENCE

By
 A* ALGORITHM BASICS FOR PATH FINDING A* , widely used  known form of best-first search & path planning algorithm nowadays in mobile robots,games. this is the function for A*,                                     f(n) = g(n) + h(n) g ( n ) is the cost of the path from the start node to n , and h ( n ) is a heuristic function that estimates the cost of the cheapest path from n to the goal This will find cheapest f(n) value in neighbor nodes to archive goal node. check below image  A to B path finding with g(n),h(n),f(n) value In the final level check below image Now we will check the Algorithm // A* Search Algorithm 1. Initialize the open list 2. Initialize the closed list put the starting node on the open list (you can leave its f at zero) 3. while the open list is not empty a) find the node with the least f on the open list, call it "q" b) pop q off the open list c) generate q's 8 successors
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