Overview of Course. Practical Robot Implementation Details. August 28, 2008

Transcription

1 Overview of Course + Practical Robot Implementation Details August 28, 2008

2 Announcements/Questions Course mailing list set up: If you haven t received a welcome message from this mailing list, see Yifan Tang (TA) right away. Any questions about Assignment #1? Use submit script 594mr_submit to submit your assignment

3 Helpful tool: robot-playerv Client GUI that visualizes sensor data from player server, from robot s perspective. Can also use it to teleoperate robot. Is used instead of a player client program (such as laserobstacleavoid) Run something like robot-player everything.cfg, then start up robotplayerv in another window Use pull-down Devices menu to subscribe to various devices (such as position2d, sonar, laser, etc.) Shows position and sonar Shows position, laser, blobfinder, and ptz data

4 Explore!! Try out different programs already provided for you Make some changes and see what happens Pick out some example programs, look up the function definitions, and be sure you understand what they re doing.

5 <see separate pdf file> Course Overview Slides

6 Practical Robot Implementation Issues

7 E f f e c t o r s Typical mobile robot implementation architecture Essentially: PC on wheels/legs/tracks/... S e n s o r s Special- Purpose Processor for sensor processing M e m o r y Standard PC running Linux M e m o r y Special- Purpose Processor for motor control

8 Implication for Robot Control Code Two options: Separate threads with appropriate interfaces, interrupts, etc. Single process with operating-system -like time-slicing of procedures Usually: combination of both For now: let s examine single process with operating-system -like timeslicing of procedures

9 Simple program: Follow walls and obey human operator commands Assume we have the following functions needed: Communications to operator interface -- commands such as stop, go, etc. Sonar: used to follow wall and avoid obstacles S o n a r Wall follower Obstacle avoider Controller & Arbitrator W h e e l s Human operator commands Communicator Want all of this to happen in parallel on single processor

10 Typical single process control approach to achieve functional parallelism int wall_follower(){/* one time slice of work */} int obstacle_avoider(){/* one time slice of work */} int com municator(){/* one time slice of work */} int controller_arbitrator(){/* decides what action to take */} main() { for(;;) { wall_follower(); obstacle_avoider(); com municator(); controller_arbitrator(); } } Note of caution: dependent upon programmer to ensure individual functions return after time slice completed

11 Example Control Command in Player/Stage PlayerClient robot( localhost ); Position2dProxy pp(&robot, 0); for (;;) { pp.setspeed(speed, turnrate); } Robot continues to execute the given velocity command until another command issued. Thus, duration of time slice is important.

12 Sources and Effect of Uncertainty/Noise Sources of sensor noise: Limited resolution sensors Sensor reflection, multi-pathing, absorption Poor quality sensor conditions (e.g., low lighting for cameras) Sources of effector noise: Friction: constant or varying (e.g., carpet vs. vinyl vs. tile; clean vs. dirty floor) Slippage (e.g., when turning or on dusty surface) Varying battery level (drainage during mission) Impact: Sensors difficult to interpret Same action has different effects when repeated Incomplete information for decision making

13 Example of Effect of Noise on Robot Control Code: Exact Motions vs. Servo Loops Current robot position & orientation d θ Goal Two possible control strategies: (1) Exact motions: Turn right by amount θ Go forward by amount d (2) Servo loop: If to the left of desired trajectory, turn right. If to the right of desired trajectory, turn left. If online with desired trajectory, go straight. If error to desired trajectory is large, go slow. If error to desired trajectory is small, go fast.

14 Consider effect of noise: Exact control method Exact method: θ θ 1 d 1 d 2 θ 2 Current robot position & orientation d θ 1 Goal, d 1 : actual angle, distance traveled; Noise overshoot goal; have to turn back to goal Doesn t give good performance

15 Consider effect of noise: Servo method Servo method: Current robot position & orientation Goal Much better performance in presence of noise

16 Keep these points in mind when designing robot solutions Autonomous: robot makes majority of decisions on its own; no human-inthe-loop control (as opposed to teleoperated) Mobile: robot does not have fixed based (e.g., wheeled, as opposed to manipulator arm) Unstructured: environment has not been specially designed to make robot s job easier Dynamic: environment may change unexpectedly Partially observable: robot cannot sense entire state of the world (i.e., hidden states) Uncertain: sensor readings are noisy; effector output is noisy