Robot Walking with Genetic Algorithms

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1 Robot Walking with Genetic Algorithms Bente Reichardt 14. December 2015 Bente Reichardt 1/52

2 Outline Introduction Genetic algorithms Quadruped Robot Hexapod Robot Biped Robot Evaluation Bente Reichardt 2/52

3 Why walking? Mobile robots Similar to humans and animals Can move forward on different surfaces Can climb Bente Reichardt 3/52

4 Why walking? Mobile robots Similar to humans and animals Can move forward on different surfaces Can climb Problem: Walking is complex Bente Reichardt 4/52

5 Evolutionary Algorithms Biological inspired Survival of the fittest Reproduction and Mutation Fishapod-Tetrapod-Tree Bente Reichardt 5/52

6 Genetic Algorithm Population based Bente Reichardt 6/52

7 Genetic Algorithm Population based Solution as chromosomes Bente Reichardt 7/52

8 Genetic Algorithm Population based Solution as chromosomes Fitness function Bente Reichardt 8/52

9 Genetic Algorithm Population based Solution as chromosomes Fitness function Generating a new generation Bente Reichardt 9/52

10 Example: Finding Orange Bente Reichardt 10/52

11 Example: Finding Orange Bente Reichardt 11/52

12 Example: Finding Orange Bente Reichardt 12/52

13 Example: Finding Orange Bente Reichardt 13/52

14 Quadruped Robot 4 legs 2 DOF per leg Bente Reichardt 14/52

15 Setup Code for the walking exists Bente Reichardt 15/52

16 Setup Code for the walking exists 235 mm/s with human testing Bente Reichardt 16/52

17 Setup Code for the walking exists 235 mm/s with human testing 4 Robots to test parallel and autonomous Bente Reichardt 17/52

18 Setup Code for the walking exists 235 mm/s with human testing 4 Robots to test parallel and autonomous Some hours to learn Bente Reichardt 18/52

19 Genetic Algorithm Population: 30 Chromosomes: Parameters of the walking algorithm Fitness function: Distance moved forward Bente Reichardt 19/52

20 Result Distance measurement sometimes failed Bente Reichardt 20/52

21 Result Distance measurement sometimes failed Time was enough Bente Reichardt 21/52

22 Result Distance measurement sometimes failed Time was enough 290 mm/s with GA optimization Bente Reichardt 22/52

23 Result Distance measurement sometimes failed Time was enough 290 mm/s with GA optimization The robot got faster Bente Reichardt 23/52

24 Hexapod Robot 6 legs 2 DOF per leg Bente Reichardt 24/52

25 Setup Evolution of a neural network for walking control Bente Reichardt 25/52

26 Setup Evolution of a neural network for walking control Separated in two steps Bente Reichardt 26/52

27 Setup Evolution of a neural network for walking control Separated in two steps Step one: move a leg Bente Reichardt 27/52

28 Setup Evolution of a neural network for walking control Separated in two steps Step one: move a leg Step two: walking with 6 legs Bente Reichardt 28/52

29 Genetic Algorithm 1 Chromosomes: Neural Network for one leg Bente Reichardt 29/52

30 Genetic Algorithm 1 Chromosomes: Neural Network for one leg Fitness function: Range of the oscillation Bente Reichardt 30/52

31 Genetic Algorithm 1 Chromosomes: Neural Network for one leg Fitness function: Range of the oscillation Stops when oscillation reaches the full length Bente Reichardt 31/52

32 Genetic Algorithm 2 Population: 10; 2 stay Bente Reichardt 32/52

33 Genetic Algorithm 2 Population: 10; 2 stay Chromosomes: Neural network for all legs Bente Reichardt 33/52

34 Genetic Algorithm 2 Population: 10; 2 stay Chromosomes: Neural network for all legs Fitness function: distance from starting point in the body axis Bente Reichardt 34/52

35 Genetic Algorithm 2 Population: 10; 2 stay Chromosomes: Neural network for all legs Fitness function: distance from starting point in the body axis Crossover rate 0.1 Bente Reichardt 35/52

36 Genetic Algorithm 2 Population: 10; 2 stay Chromosomes: Neural network for all legs Fitness function: distance from starting point in the body axis Crossover rate 0.1 Mutation rate: 0.04 Bente Reichardt 36/52

37 Result Learned to move their legs Bente Reichardt 37/52

38 Result Learned to move their legs Robots walked backwards Bente Reichardt 38/52

39 Result Learned to move their legs Robots walked backwards Similar to living insects Bente Reichardt 39/52

40 Result Learned to move their legs Robots walked backwards Similar to living insects Wave-gait vs tripod gait Bente Reichardt 40/52

41 Biped Robot 2 legs 6 DOF per leg Bente Reichardt 41/52

42 Experiment Truncated Fourier Series and polynomial equations used for motor control Evaluating GA and PSO Evolutionary Algorithms for Humanoid Walk Pattern Planning Bente Reichardt 42/52

43 Experiment Truncated Fourier Series and polynomial equations used for motor control Sinusoid equation the swing leg Evaluating GA and PSO Evolutionary Algorithms for Humanoid Walk Pattern Planning Bente Reichardt 43/52

44 Experiment Truncated Fourier Series and polynomial equations used for motor control Sinusoid equation the swing leg partly polynomial equations for the support leg Evaluating GA and PSO Evolutionary Algorithms for Humanoid Walk Pattern Planning Bente Reichardt 44/52

45 Experiment Truncated Fourier Series and polynomial equations used for motor control Sinusoid equation the swing leg partly polynomial equations for the support leg Learning of the equations for every motor Evaluating GA and PSO Evolutionary Algorithms for Humanoid Walk Pattern Planning Bente Reichardt 45/52

46 Genetic Algorithm Population: 20 Chromosomes: Coefficients of the equations Fitness function: Walking without falling Evaluating GA and PSO Evolutionary Algorithms for Humanoid Walk Pattern Planning Bente Reichardt 46/52

47 Result Coefficients are learned Each motor has specific equation robots learned to walk Evaluating GA and PSO Evolutionary Algorithms for Humanoid Walk Pattern Planning Bente Reichardt 47/52

48 Evaluation Optimization algorithm Bente Reichardt 48/52

49 Evaluation Optimization algorithm Finds a good solution Bente Reichardt 49/52

50 Evaluation Optimization algorithm Finds a good solution PSO (particle swarm optimization) usually faster Bente Reichardt 50/52

51 Thank you for your attention! Bente Reichardt 51/52

52 References Chernova, Sonia, and Manuela Veloso. An evolutionary approach to gait learning for four-legged robots. Intelligent Robots and Systems, 2004.(IROS 2004). Proceedings IEEE/RSJ International Conference on. Vol. 3. IEEE, Lewis, M. Anthony, Andrew H. Fagg, and Alan Solidum. Genetic programming approach to the construction of a neural network for control of a walking robot. Robotics and Automation, Proceedings., 1992 IEEE International Conference on. IEEE, Azarkaman, Mostafa, Mohammad Aghaabbasloo, and Mostafa E. Salehi. Evaluating GA and PSO evolutionary algorithms for humanoid walk pattern planning. Electrical Engineering (ICEE), nd Iranian Conference on. IEEE, Bente Reichardt 52/52

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