Path Finding And Control In Mobile Autonomous Robotic Systems

1. Path Finding And Control In Mobile Autonomous Robotic Systems Dan Hand COT 4810 February 19, 2002

2. Presentation Overview • A robot is an computer-controlled electro-mechanical device • Robotic systems can be grouped by movement capability: • Constrained systems • Free moving systems, we will call these mobile systems • Mobile robotic systems have some unique issues. This presentation will focus on two: • Path finding • Related control issues

3. Path Finding • Applies to autonomous systems • Path Finding can consist of: • Trial and Error • Pre Planning + Adjustment (if needed) • Several approaches have been used to implement the intelligence in Path-Finding • Subsumption approach • Knowledge-based approach • Selection is dependent on information, sensors, and computer hardware available

4. Path Finding Example Consider the following situation: Straight Path Trial & Error Path Target Pre-Planned Path Lake or Other Hazard Robot

5. Subsumption-Based Path-Finding • Corresponds roughly to a trial and error approach • Approach to “intelligence” in robotic systems • Proposed by R.A. Brooks in 1986 • Advocates building agents by combining simple reflexive reactions • Characteristics • No explicit knowledge representation • Reflexive response to environment • Purely reactive • Behavior based systems extend subsumption architecture to add state storage

6. Situational Behaviors – these will only be executed if the correct conditions exist Avoid hazards The default behavior usually takes no action but is the parent of all other behaviors in the system Increasing Precedence Move to target Default behavior Subsumption Architecture Example • Behaviors can be viewed as being placed in a stack • Behaviors with a lower precedence in the stack are said to be suppressed • Avoidance behavior has “highest” precedence here

7. Subsumption Architecture Path Finding Example Tree Target 6 4 3 5 Initial Straight Line Course From Move To Target Behavior Path With Obstacle Avoidance Behavior Robot 2 1 Building • Active Behaviors At Each Point In The Diagram • ) Default, Move To Target • ) Default, Move To Target, Move Away From Hazards • ) Default, Move To Target • ) Default, Move To Target, Move Away From Hazards • ) Default, Move To Target • ) Default

8. Knowledge-Based Path-Finding • Several knowledge-based mobile robots and techniques exist • Basic idea: • If information about the environment is well-known • Pre-planning a reasonable path based on known data • Adjustments can be made (if needed) along the way • If no or little information is available about the environment • Possible to collect information and store along the way • Problems arise with collecting accurate information • Disadvantage: • System highly reliant on accurate and detailed data on surroundings

9. Control Overview • Control involves both sensing a condition and taking a action based on it • Control difficulty dependent on complexity of system hardware • Locomotion method • Wheels / Tracks • Legs • Number & type of sensors • Vision? • Operating environment issues • Sensing environment, converting into useful information • Localization

10. Feedback From Output Device Output Device Logic Control System Command Output Based on Feedback General Control Overview • Feedback from output devices is critical in mobile robotic systems to ensure operation in varying environments

11. Feedback Example • Consider the example below • It is important to insure that each wheel spins at the correct rate for the vehicle to remain on course Wheel Sensor Varying force applied to wheel Top View Motor Controller Motor Wheel Rotation

12. Localization • Important control and sensing issue • Consists of precisely locating position on map • Needed to identify position relative to obstacles • Especially important to knowledge-based systems since error in positioning can accumulate • Numerous solutions exist • High-accuracy GPS • Landmark recognition • Odometers • Beacons

13. Localization Continued • Sample situation with a standard GPS-Based system Error in GPS Positioning Distance to object Object Detected By Sensors

14. Sample Knowledge-Based Robot TJ - Designed by Mark Torrance for his master’s thesis Where are you? I don't know where I am right now. Which way are you facing? I don't know which way I am facing. Please tell me. You are facing north I am facing NORTH. Thanks. You are at Mark's office I'm learning about MARK'S OFFICE. Turn around I'm turning around. Go until you can turn right I'm going until I see no obstacle on the right. You are at the northeast entrance to the elevator lobby I'm learning about THE NORTHEAST ENTRANCE TO THE ELEVATOR LOBBY. Go I'm going. . . . Designed to navigate around MIT offices Uses an odometer for localization

15. For Additional Information • Mobile Robots: http://www.ai.mit.edu/projects/mobile-robots/http://www.cc.gatech.edu/ai/robot-lab/ • Subsumption Architecture: http://ai.eecs.umich.edu/cogarch0/subsump/http://www-formal.stanford.edu/eyal/lsa/ • Motor Control / Feedback:http://e-www.motorola.com/webapp/sps/site/homepage.jsp?nodeId=03nQXG