Fast Walking and Modeling Kicks Purpose: Team Robotics Spring 2005 By: Forest Marie

Walk Learning CMU developed an autonomous approach for optimizing fast forward gaits based on genetic algorithms This approach allowed 20% improvement from CMPack’02 Using the technique, the ERS-7 reached speeds of up to 40 cm/sec

Walk Learning Sample several different movement positions of an individual leg executing a movement For each walk parameter motion, check with the kinematics system to assure you want stress out the motor or harm the robot –E.G. Stop a movement that is not physically capable by AIBO Every walk parameter is represented by a vector Cross-over uses two walk parameters to form two individuals from two parents Not the most trivial algorithm unless you are familiar with genetic algorithms in detail. More details can be found at: – 491/lectures/iros2004.pdf

Modeling the Effects of Kicking Motion Goal : Analyze a variety of kicks and select the best kick for a given state of the world Algorithms used: –Trajectory Angle ( TRACKANGLE() ) –Modeling Kick Distance ( TRACKDISTANCE() ) –List of kicking motions

Trajectory Angle Purpose: –To obtain the angle of trajectory after a kick Background: –The robot’s vision module provides info about the ball’s location –This module stores the location for every vision frame assuming the ball is in range of the camera

TRACKANGLE() Code Algorithm: –Set time of kick = 0 TRACKBALLWITHHEAD() if Ball is in kicking range KICK() Set time of kick to current time if current time – time of kick > delay angle = CALCANGLEUSINGBALLHISTORY()

TRACKANGLE (continued) Notes: –Vision ball history is continuously updating –We want to store the trajectory accurately, so: We must store the trajectory before it goes too far But after enough time has passed to record at least one second of the ball’s trajectory –The value of delay is represented as : time of the kick + 1 second – Larger delays are used for kicks with lots of head motions that cause delay in tracking

TRACKANGLE (continued) More notes –Linear regression algorithm is used in the history list to calculate trajectory – M is used to approximate the angle of the trajectory – AIBO records 25 vision frames per second –CMU requires that at least 20/25 frames contain info

Modeling Kick Distance Purpose: –Calculate the distance the ball will travel after a given kick Background: –AIBO is unable to track the entire trajectory of the ball because the ball will travel out of the robot’s field of view –Instead we can calculate where the ball will stop relative to its position before the kick –The next slide shows the algorithm on how to achieve this

TRACKDISTANCE() code While ( true ) APPROACHBALL() KICKBALL() STANDANDLOCALIZE() Set initial ball position = current AIBO position FINDBALL() APPROACHBALL() if ball distance < 50 centimeters STANDANDLOCALIZE() Set final ball position = current ball position Set ball display vector = final ball position – initial ball position

Kick Selection Algorithm Purpose: –To choose an appropriate kicking motion out of a list based on a given state of the world Background –Data gathered in the modeling stages can be organized into a motion library –Each kicking motion is classified by its effects on the ball, in terms of the mean angle and distance

Kick Selection Algorithm explained The localization system gives info about AIBO’s position on the field relative to the location of the goal The allows our code to calculate the trajectory of the ball After, the motion library is referenced to select the best kick whose effects match the desired trajectory If no kick motion provides a close match, then a series of behaviors can be put together to achieve the desired outcome –E.G. AIBO may turn the ball to achieve a better scoring position

Conclusion Using modeling,13 second for scoring versus CMPack’02. Their research is ongoing and they are looking to expand upon the modeling techniques we discussed CMU is interested in constructing algorithms to improve kick selection in convoluted multi-AIBO scenarios Questions?