Robotic Control with Situation-Aware Mobile Computing

1. Robotic ControlWith Situation Aware Mobile Computing and Distributed Robot Agents Brent Dingle Marco A. Morales Texas A&M University, Spring 2002

2. Outline • Definition of the problem • A robot agent

3. Problem • A robot is an Intelligent Connection of Perception to Action (Jones, Flynn 1993) • The major problem in Robotic Motion Planning is stated thus:To plan an obstacle free path for a robot from a specified initial configuration (position, orientation, etc) to a specified final configuration. • Multiple sensors and actuators • Two main approaches: • Sequential based • Behavior based

4. Control of Robots • Two main approaches: • Sequential based • Behavior based • Little integration with the environment • It is important to create autonomous robots, but technology could help to build robots competent enough for specific tasks.

5. Sequential Based Approach • Sensors gather data • Data are translated into a intermediate language • A model of the world is built • Motion planning is performed • Motion commands are translated into low level orders for actuators

6. Behavior based approach • Modules generate behaviors • Each has perception and planning • Each receives input and give commands • A mediator scheme assigns control to modules • Basic behaviors lead to complex behaviors • No central model of the world • No central control

7. Smaller Problems • Currently robot control is done for SPECIFIC environments. • Sequential approach • It takes a great amount of time to find the solution to complex environments. • The environment is often assumed static. • Behavior based approach • There is little knowledge to share about the environment • The solution usually applies to a SPECIFIC robot. • The solutions only deal with one robot. • If the path doesn’t work the robot gets “stuck” (until a human helps it out).

8. Small to One: Situation Aware Mobile Computing (SAMC) • We would propose that these small problems can all be solved through the usage of techniques employed in Situation Aware Mobile Computing. • SAMC obviously is related to Robotic Motion Planning. • So we are going to assume some things (reasonably) so that we may incorporate the advantages of Situation Aware Mobile Computing into Robotic Motion Planning.

9. Assumptions • Rooms exist with transensors – devices that can send and receive wireless communications from mobile devices and relay them to a computer for processing. • Robots are mobile devices. • are equipped with a minimal set of functions defining how to move themselves about. • are capable of translating a “general” command set into their hardware specific command set. • Computers exist which are aware of • Robots and have access to “extra” information on various types of robots and robot IDs. • Various room ‘states.’ Where the state of the room is derived from information relayed by transensors. • General solutions to moving around the room, items in the room, and actions that may be taken on items in the room (and how to do so). • The general command set and can relay directions for motion and action using this command set.

10. Picture (Proposed Idea) • The solution usually applies to a SPECIFIC robot. • No longer a problem as the solutions (paths and actions) are stored in the room in a generic language. • Each robot becomes responsible for translating the general solution into specific commands.

11. Problems become solutions. • The solutions only deal with one robot. • Obviously since each robot is operating autonomously there is no loss of processing power to implement the solutions. • And the problem is mostly pre-solved by the room’s distant computer. • All the computer need do is send each robot a path in such a fashion as it will not collide or interfere with the path of another. • The environment is often assumed static. • As the transensors can track objects in the room the computer will know the location of all objects in the room – even those moving. • So if necessary small (and quick) adjustments can be sent by the room’s computer to correct for the dynamic environment (e.g. telling a given robot to delay 10 seconds so another may pass OR sending another robot to assist).

12. Problems become solutions. • If the path doesn’t work the robot gets “stuck” (until a human helps it out). • Also no longer a problem. • If a solution for whatever reason fails, the robot may send a request for another solution or request aid in performing its current task.

13. Extra Beauty • As the room’s computer may be in contact with multiple robots at the same time (and objects in the room). • It may direct robot B to help robot A, or to synchronize them together to achieve a task they cannot perform alone. • This may involve sending robot A to find robot B – known to be elsewhere in the building. • Further the rooms’ computer could control access to the rooms or direct things such things as lifts or non-mobile robots (robot arms) to assist in accomplishing tasks. • Robots can help each other to accomplish a task by sharing information only accessible to one of them at a time.

14. Robot Architecture

15. Components of the Robot Agent • Planner • Finds a path between two points or reports no such a path • Navigator • Creates a list of high level commands for the robot • Pilot • Gives low level commands to the controller and it’s aware of the sensors • Controller • Controls actuators in closed chains

16. Problems: Environment modeling • To model the environment each robot either is able to: • Identify main features by itself, or • Uses a set of preloaded features. • It seems reasonable to make robots use both.

17. More Problems • Distribution of tasks • Environments are far too complex for a robot to handle efficiently in detail • A robot shouldn’t care for parts of the environment that are far away, unless it really needs them. • Nearby robots can help by providing info on the environment. • Rooms, buildings, sites can help by having planning abilities.

18. Solution • Build a distributed Motion Planning Agent • Use nearby robots as sources of info for the environment • Use precompiled info about the environment • The local planner gathers info from all the robots in it and makes plans for them while they are nearby • The robots can ask a local planner for a plan to follow • The robots have the navigator, pilot and controller. • A global planner coordinates the missions of the robots.

19. Distributed Planner • Global planner • Defines general goals based on main tasks • Go to room 124, go to room 302 • Local planner • Activated when the robot arrives to the area known to a given local planner • Coordinates robot information with room information • Stores a local map of the room and info gathered by all robots in the room • Gives a plan to each robot in its “influence” area.

20. Robot agent • Basic planner • Takes control when no info is given by a room planner • Navigator • Pilot • Controller

21. Advantage summary • We have turned problems into solutions. • We have introduced generality into the solutions. • We have added the extra functionality of coordinating robots. • We have decreased the functionality requirements of the robots (and likely the cost) • We have increased the potential functionality of a given robot (no longer constrained to just a specific one or two tasks).

22. The “newness” • Almost no robotic motion planning algorithms consider the possibility of another computer assisting in the path. • Much of the research currently is on finding paths – not accomplishing tasks. • Coordinating actions through wireless communications would be an obvious direction to go in research. • Building a generic command language to describe robotic motion needs to be done. (Aside: Building a generic representation of rooms also needs to be done.)

23. Potential Future • Incorporating humans into these concepts would be nice. • This is already being done in medical operations. • Could it be possible to design automatic assistants that coordinate in real time with the surgeon, via preplanned paths and a similar network. • Could equipment auto relocate itself as needed from one surgery room to another based on calls from room computers and scheduled operations?

24. End of Talk • Questions? • Contact: Brent Dingle dingle@cs.tamu.eduor Marco Morales marcom@cs.tamu.edu

25. Misc. thoughts • Knowing the needs of the future is planning. =) • Preplanning paths is anticipating needs based on situation. • Representing and recognizing the room state is not as important as representing and recognizing what just changed in the room.