Programmers: I Have A Challenge For You

[kamocat]

15 seconds isn't enough to take advantage of full-field awareness.
It's only enough time to do something and hope it works. (For example, if you fire a ball into the goals from mid- or far- field, you don't have enough time to go over there and make sure it actually went in.)

[Kevin Watson]

Originally Posted by kamocat

15 seconds isn't enough to take advantage of full-field awareness.
It's only enough time to do something and hope it works. (For example, if you fire a ball into the goals from mid- or far- field, you don't have enough time to go over there and make sure it actually went in.)

Back in 2004 we had an infrared beacon system that could be used to determine your position on the field and perform a fully autonomous action. It's demonstrated at about 1:40:40 of the kick-off video:

http://robotics.nasa.gov/first/2004/kickoff.htm

It used $10-$15 dollars worth of parts plus some custom code that's still available here:

http://kevin.org/frc/2004/

-Kevin

[ideasrule]

Originally Posted by Chris27

Good thing we have teams like 1114 to prove you dead wrong

1114 didn't prove him dead wrong this year. 1114's ranking at any regional would definitely not have been different if they stayed still during autonomous. The robot was averaging 1-2 balls scored per autonomous mode. Our robot averaged 1 ball per autonomous mode, and despite being a much weaker team, the one match where that one score mattered was better known as the match where we got disqualified.

[Chris27]

Originally Posted by ideasrule

1114 didn't prove him dead wrong this year. 1114's ranking at any regional would definitely not have been different if they stayed still during autonomous. The robot was averaging 1-2 balls scored per autonomous mode. Our robot averaged 1 ball per autonomous mode, and despite being a much weaker team, the one match where that one score mattered was better known as the match where we got disqualified.

Not as much this year but in Overdrive their auton alone could have won them 9/10 matches. I'm just saying its absurd to dismiss a 15s autonomous period as useless. I know there are not any bonus points this year for scoring in auton like in past years, but that was also the case last year. Before the season started many people on CD were also saying that auton would be mostly useless and the only really useful thing to do was to not get scored on. Not really meaning to toot my own horn but last year one thing that gave my team a substantial early advantage was that we were able to load up during auton. I think we set a good example of what could be done. I think a lot of the effort spent complaining about how FIRST views the role of autonomy would be better spent developing an autonomous mode that consistently scores 3-5 balls. That's nothing to sneeze at considering most alliances struggle to break 10 points.

[Chris is me]

Originally Posted by theprgramerdude

Rather than the 5-second rule, I think a far better, and more adjustable, step for FIRST teams would simply be to expand the autonomous period past 15 seconds. Because, seriously, 15 seconds isn't enough to do anything worthwhile. It would make the autonomous a bit more important, and give some motivation to teams that decide "our drivers can make up for anything we dont do in autonomous."

I'm sure teams that decided to do that in 2006, 2008, and 2010 were playing the final match on Einstein.

[Andrew Schreiber]

Originally Posted by theprgramerdude

Rather than the 5-second rule, I think a far better, and more adjustable, step for FIRST teams would simply be to expand the autonomous period past 15 seconds. Because, seriously, 15 seconds isn't enough to do anything worthwhile. It would make the autonomous a bit more important, and give some motivation to teams that decide "our drivers can make up for anything we dont do in autonomous."

2003, taking the entire stack on the top of the bump and then controlling the bump could win. (I wasn't around this year perhaps someone could figure out how much of a swing it could be)

2004, several teams hung in autonomous and then controlled the bar, they swung the match score up to 150 points (+50 for their hang -100 for their opponents not being able to hang). I guess that isn't serious.

2006, winning auton was a huge benefit. I saw many matches decided by auton alone.

2008, 1114, do I need to say more? Ok, 217. There you go. Auton could decide the match.

2009, on Einstein the final match autons consisted of loading up the bots to go dump a load into their opponents, not as important but it added many balls to the arsenal of the dumpers (or 217's shooter).

2010, 469 shows how useful a good auton can be. If they get set in auton you are pretty much down 4 points at the start of the match (They score 2 balls and then recycle them as soon as people start moving).

I would say that 15 seconds is plenty of time to do something important.

[davidthefat]

All who are trying this: I have a book for you. http://www.amazon.com/Introduction-A.../dp/026219502X
The TOC:

Contents
Acknowledgments                                                   xi
Preface                                                          xiii
1    Introduction                                                  1
     1.1 Introduction                                              1
     1.2 An Overview of the Book                                  10
2    Locomotion                                                   13
     2.1 Introduction                                             13
          2.1.1 Key issues for locomotion                         16
     2.2 Legged Mobile Robots                                     17
          2.2.1 Leg configurations and stability                  18
          2.2.2 Examples of legged robot locomotion               21
     2.3 Wheeled Mobile Robots                                    30
          2.3.1 Wheeled locomotion: the design space              31
          2.3.2 Wheeled locomotion: case studies                  38
3    Mobile Robot Kinematics                                      47
     3.1 Introduction                                             47
     3.2 Kinematic Models and Constraints                         48
          3.2.1 Representing robot position                       48
          3.2.2 Forward kinematic models                          51
          3.2.3 Wheel kinematic constraints                       53
          3.2.4 Robot kinematic constraints                       61
          3.2.5 Examples: robot kinematic models and constraints  63
     3.3 Mobile Robot Maneuverability                             67
          3.3.1 Degree of mobility                                67
          3.3.2 Degree of steerability                            71
          3.3.3 Robot maneuverability                             72
viii                                                                             Contents
     3.4  Mobile Robot Workspace                                                       74
          3.4.1 Degrees of freedom                                                     74
          3.4.2 Holonomic robots                                                       75
          3.4.3 Path and trajectory considerations                                     77
     3.5  Beyond Basic Kinematics                                                      80
     3.6  Motion Control (Kinematic Control)                                           81
          3.6.1 Open loop control (trajectory-following)                               81
          3.6.2 Feedback control                                                       82
 4   Perception                                                                        89
     4.1 Sensors for Mobile Robots                                                     89
          4.1.1 Sensor classification                                                  89
          4.1.2 Characterizing sensor performance                                      92
          4.1.3 Wheel/motor sensors                                                    97
          4.1.4 Heading sensors                                                        98
          4.1.5 Ground-based beacons                                                  101
          4.1.6 Active ranging                                                        104
          4.1.7 Motion/speed sensors                                                  115
          4.1.8 Vision-based sensors                                                  117
     4.2 Representing Uncertainty                                                     145
          4.2.1 Statistical representation                                            145
          4.2.2 Error propagation: combining uncertain measurements                   149
     4.3 Feature Extraction                                                           151
          4.3.1 Feature extraction based on range data (laser, ultrasonic, vision-based
                ranging)                                                              154
          4.3.2 Visual appearance based feature extraction                            163
 5   Mobile Robot Localization                                                        181
     5.1 Introduction                                                                 181
     5.2 The Challenge of Localization: Noise and Aliasing                            182
          5.2.1 Sensor noise                                                          183
          5.2.2 Sensor aliasing                                                       184
          5.2.3 Effector noise                                                        185
          5.2.4 An error model for odometric position estimation                      186
     5.3 To Localize or Not to Localize: Localization-Based Navigation versus
          Programmed Solutions                                                        191
     5.4 Belief Representation                                                        194
          5.4.1 Single-hypothesis belief                                              194
          5.4.2 Multiple-hypothesis belief                                            196
Contents                                                                       ix
      5.5   Map Representation                                               200
            5.5.1 Continuous representations                                 200
            5.5.2 Decomposition strategies                                   203
            5.5.3 State of the art: current challenges in map representation 210
      5.6   Probabilistic Map-Based Localization                             212
            5.6.1 Introduction                                               212
            5.6.2 Markov localization                                        214
            5.6.3 Kalman filter localization                                 227
      5.7   Other Examples of Localization Systems                           244
            5.7.1 Landmark-based navigation                                  245
            5.7.2 Globally unique localization                               246
            5.7.3 Positioning beacon systems                                 248
            5.7.4 Route-based localization                                   249
      5.8   Autonomous Map Building                                          250
            5.8.1 The stochastic map technique                               250
            5.8.2 Other mapping techniques                                   253
 6    Planning and Navigation                                                257
      6.1 Introduction                                                       257
      6.2 Competences for Navigation: Planning and Reacting                  258
            6.2.1 Path planning                                              259
            6.2.2 Obstacle avoidance                                         272
      6.3 Navigation Architectures                                           291
            6.3.1 Modularity for code reuse and sharing                      291
            6.3.2 Control localization                                       291
            6.3.3 Techniques for decomposition                               292
            6.3.4 Case studies: tiered robot architectures                   298
Bibliography                                                                 305
      Books                                                                  305
      Papers                                                                 306
      Referenced Webpages                                                    314
      Interesting Internet Links to Mobile Robots                            314
Index

As you can see, it covers everything from the Perception to Logic and even drive systems

[gvarndell]

Originally Posted by davidthefat

All who are trying this: I have a book for you.

[Doug Leppard]

Originally Posted by Andrew Schreiber

2003, taking the entire stack on the top of the bump and then controlling the bump could win. (I wasn't around this year perhaps someone could figure out how much of a swing it could be)

2004, several teams hung in autonomous and then controlled the bar, they swung the match score up to 150 points (+50 for their hang -100 for their opponents not being able to hang). I guess that isn't serious.

2006, winning auton was a huge benefit. I saw many matches decided by auton alone.

2008, 1114, do I need to say more? Ok, 217. There you go. Auton could decide the match.

2009, on Einstein the final match autons consisted of loading up the bots to go dump a load into their opponents, not as important but it added many balls to the arsenal of the dumpers (or 217's shooter).

2010, 469 shows how useful a good auton can be. If they get set in auton you are pretty much down 4 points at the start of the match (They score 2 balls and then recycle them as soon as people start moving).

I would say that 15 seconds is plenty of time to do something important.

I would agree. As a mentor I have been there for all auto years. If the game is designed right you can do a lot in 15 seconds.

2003 purpose, to knock your boxes to your side of field. Auto gave you a good position and start against your opponents.

2004 Purpose to knock down the balls early in game. Gave you a small advantage. But mostly if you moved during auto you go noticed. It was a fun one to do and watch.

2005 It was the tetra year and idea was to put the tetra in place. It was a bust. Almost no one could do much with it, too hard.

2006 Aim high place balls in corner or middle target. It was 1902's rookie year and we had a simple auto mode that consistently put 10 balls in corner and gave bonus points. Because of that we were 9-0 in Houston.

2007 Rack and Roll, place tube on rack. Many said it did not help. I calculated it made the difference in winning or losing several matches.

2008 Race around track and knock down balls. This was the most fun and challenging auto mode. Could make huge difference.

2009 Auto was hard and mostly in my opinion did not make a big difference unless you didn't move and you got nailed for not moving.

Bottom line auto modes are fun and most years make a difference. Longer than 15 seconds and it becomes boring because most teams do not even move during that time.

I think auto mode is important for a team because you stand out during that the 15 seconds awhile so many others just sit there.

[kamocat]

The thing I notice about all of those is that autonomous is always playing an assistive role to teleop. I wonder if that could be reversed?

[toastnbacon]

I love the sound of this, but I can almost guarantee my team won't.

[gblake]

Originally Posted by toastnbacon

I love the sound of this, but I can almost guarantee my team won't.

See post #231 Link_to_231

[gblake]

Originally Posted by kamocat

The thing I notice about all of those is that autonomous is always playing an assistive role to teleop. I wonder if that could be reversed?

Another mentor wrote something like this during the Rack-N-Roll season - I'm paraphrasing:

Once you create a machine that can score a ring (or some other useful function) during the autonomous period, you use that capability like a macro to automate scoring during the entire match.

A good autonomous scorer, becomes a predictable/reliable tool for the drive team to use throughout teleop. It multiplies their effectiveness and frees them to think about higher level concerns, instead of the minutiae of the actions a machine can carry out on their behalf.

How about starting with this general mindset and then pushing it as far as we are able?

Blake

[WJF2011]

Understand this. This is a beatable robot. All robots can be defeated with strategy. - Alexander McGee

Please see team 71 in 2002:
http://www.youtube.com/watch?v=h4slvnvPHW8

[Tom Line]

Originally Posted by WJF2011

Understand this. This is a beatable robot. All robots can be defeated with strategy. - Alexander McGee

Please see team 71 in 2002:
http://www.youtube.com/watch?v=h4slvnvPHW8

That WAS a beatable robot. If your robot was quick enough to get to them and knock them off center before they locked on, they were defeatable.

Many people said the same thing about 469 on einstein this year - that they were a lock with 1114.

It requires a clear understanding of the game and a good grasp of strategy, but any robot is beatable.