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Mobile Robotic Navigation Control in Moving Obstacle Environment

Mobile Robotic Navigation Control in Moving Obstacle Environment. Rahul Kala, Department of Information Technology Indian Institute of Information Technology and Management Gwalior http://students.iiitm.ac.in/~ipg_200545/ rahulkalaiiitm@yahoo.co.in, rkala@students.iiitm.ac.in.

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Mobile Robotic Navigation Control in Moving Obstacle Environment

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  1. Mobile Robotic Navigation Control in Moving Obstacle Environment Rahul Kala, Department of Information Technology Indian Institute of Information Technology and Management Gwalior http://students.iiitm.ac.in/~ipg_200545/ rahulkalaiiitm@yahoo.co.in, rkala@students.iiitm.ac.in Paper: Kala, Rahul, et al (2009), Mobile Robot Navigation Control in Moving Obstacle Environment using Genetic Algorithm, Artificial Neural Networks and A* Algorithm, Proceedings of the IEEE World Congress on Computer Science and Information Engineering (CSIE 2009), ieeexplore, DOI 10.1109/CSIE.2009.854, pp 705-713, April 2009, Los Angeles/Anaheim, USA (Conference) (Download Paper)

  2. Algorithms • A* Algorithm • Genetic Algorithm • Artificial Neural Networks

  3. The Problem • There is a region of static and dynamic obstacles • Guide the robot from source o destination

  4. Assumptions • The entire region divided in grid on size M X N • Each dynamic element can move not more than a unit distance in threshold time

  5. Robotic Assumptions Various directions the robot can move in The representation of various directions

  6. Robotic Assumptions The robotic movements • Move forward (unit step) • Move at an angle of 45 degrees forward (unit step) from the current direction • Move clockwise/anti-clockwise (45 degrees) • Move clockwise/anti-clockwise (90 degrees)

  7. Robotic Movements Various Movements Possible

  8. Algorithm I: A*

  9. Costs • g(n) = depth from the initial node, increases by 1 in every step • h(n) = square of the distance of the current position and the final position + R(n) • R(n) = minimum time required for the robot to rotate in its entire journey assuming no obstacles • f(n) = g(n)+h(n)

  10. 3 2 Goal Position 1 4 R(n) The values of R(n) at various places Region 1: 0 Region 2: 2 Region 3: 1 Region 4: 1

  11. NavigationPlan() Step 1: while(CurrentPosition <> FinalPosition) Begin Step 2: m ← getNextRobotMove(CurrentPosition) Step 3: moveRobot(m)

  12. getNextMove(CurrentPosition) Step 1: closed ← empty list Step 2: add a node n in open such that position(n) = CurrentPosition Step 3: while open is not empty Begin Step 4: extract the node n from open with the least priority Step 5: if n = final position then break Step 6: else Step 7: moves ← all possible moves from the position n Step 8: for each move m in moves Begin Step 9: if m leads us to a point which can be the point of any obstacle in the next unit time then discard move m and continue Step 10 if m is already in open list and is equally good or better then discard this move Step 11: if m is already in closed list and is equally good or better then discard this move Step 12 delete m from open and closed lists Step 13: make m as new node with parent n Step 14: Add node m to open Step 15: Add n to closed Step 16: Remove n from open

  13. 0(N) 2(E) 3(SE) 1(NE) 4(S) 5(SW) 6(W) 7(NW) Testing The representation of obstacles in various directions

  14. Results

  15. Results

  16. Simulation Snapshot

  17. Various Phases

  18. Algorithm IIGenetic Algorithm

  19. Genetic Algorithm • We consider a graphical representation of chromosome • Each chromosome a collection of graphical nodes • Crossover operation by graph mixing

  20. Types Of solutions • Full: They are complete solution and fully connect source to destination • Left: The start from the source but are unable to reach to the destination • Right: They do not start from the source but reach the destination

  21. Types Of solutions Full Left Left Right Right

  22. Fitness Function • Full Solution: f(S)=No of steps needed to traverse from source to destination • Left Solution: Number of steps needed to traverse from source to the last point traversed + (Square of the distance of this point to the final destination + R(n)) • Right Solution: (Square of the distance of source point to the first point traversed + R(n)) + Number of steps needed to traverse from this point to the final solution

  23. Initial Solution • Try to find a straight path solution between source and destination • Try to find random left solution between source and destination • Try to find random right solution between source and destination. • Find Full Solutions from fusion of left and right

  24. Initial Solution Step1: for i ← 1 to noIterationsInitial Begin Step2: for j ← 1 to (3/2)*perimeter of board (Maximum moves per solution being tried) Begin Step3: if p ← destinationPoint Step4: add p to current solution set and break Step5: find the move m in moves which makes the robot closest to destination Step6: if point p we reach after m is already in the current solution set then delete earlier points and add this point. Step7: if no move takes the robot closer to target Step 8: make randomMoves number of random moves in any direction Step9: for each move m in these moves, if point p we reach after m is already in the current solution set then delete earlier points and add this point. Step10: if destinationPoint is reached then add this solution set to fullSolution Step11: else add this solution set to leftSolution

  25. Crossover • The crossover can occur between any of the following pairs of solutions • Full Solution and Full Solution • Left Solution and Full Solution • Full Solution and Right Solution • Left Solution and Right solution

  26. Crossover

  27. CrossOver(parent1,parent2) Step 1: points ← find common points in parent1 and parent2 that have same x and y coordinate Step 2: if points <> null Step 3: for each common point p in points Begin Step4: sol1 ← all points in parent1 till p + rotations necessary to transform direction of p to the one of point p of parent2 + all points in parent2 after p Step5: Add sol1 to fullSolution set

  28. Mutation

  29. Mutate Step 1: if no of solutions in full solution set > 0 then Step 2: r1,r2 ← any two random points on a random solution from fullSolution set set with r1 on the left of r2 Step 3: find solutions as found in initial solution with start point as r1 and goal point as r2 Step 4: if full results generated > 0 Step 5: newSolution ← most fit solution in this generated solution set Step 7: sol1 ← all points in solution till r1 + rotations necessary to transform direction of r1 to the one of first point of newSolution + all points in newSolution + rotations necessary to transform direction of last point in newSolution to r2 + all points in solution after r2 Step 8: add sol1 to solutionFull

  30. Selection • With a probability of 2/3, select the most fit chromosome from the full solution set and any one randomly from the left/right/full solution set • With a probability 1/3 select any random chromosome from full solution set and any one randomly from left/right/full solution set

  31. Results

  32. Results

  33. Algorithm IIIArtificial Neural Networks

  34. Inputs There are total 26 inputs to the neural network (I0-I25) • The I0 and I1 denote the rotations • (I0,I1)=(0 0) represents +2 or -2 • (I0,I1)=(0 1) represents +1 • (I0,I1)=(1 1) represents 0 • (I0,I1)=(1 0) represents -1 These are numbered according to gray codes

  35. Inputs

  36. Outputs • 3 Outputs (O0,O1,O2)=(0,0,0) Rotate left 90 degrees. (O0,O1,O2)=(0,0,1) Rotate left 45 degrees. (O0,O1,O2)=(0,1,0) Move forward. (O0,O1,O2)=(0,1,1) Rotate left 45 degrees and move forward. (O0,O1,O2)=(1,0,0) Do not move. (O0,O1,O2)=(1,0,1) Rotate right 90 degrees. (O0,O1,O2)=(1,1,0) Rotate right 45 degrees and move forward. (O0,O1,O2)=(1,1,1) Move right 45 degrees.

  37. GenerateTestCases() Step 1: initialize map and generate N obstacles at positions (xi,yi,di) Step 2: while(noTestCaseGenerated <> noTestCasesDesired) Begin Step 3: Generate the robot at random position (xi,yi,di) Step 4: I ← Input sequence Step 5: m ← getNextRobotMoveUsingAStar(CurrentPosition) Step 6: moveRobot(m) Step7: O ← Output Sequence Step 8: Add (I,O) to training data

  38. NavigationPlan() Step 1: n ← Generate Neural Network Step 2: n ← TrainNeuralNetwork() Step 3: while(CurrentPosition <> FinalPosition) Begin Step 4: I ← generateInputSequence() Step 5: O ← generateOutputSequence(I) Step6: if O gives valid move Step 7: moveRobot(O) Step 8: if robot not changed position in last 5 steps Step 9: makeRandomMove() Step10: if distance of robot decreasing in last 5 steps (excluding rotations) Step11: move next move only if the next move decreases distance or is of equal distance

  39. Results

  40. Results

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