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Robotic ControlWith Situation Aware Mobile Computing and Distributed
Robot Agents
Brent Dingle
Marco A. Morales
Texas A&M University, Spring 2002
Outline
• Definition of the problem
• A robot agent
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
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.
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
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
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).
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.
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.
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.
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).
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.
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.
Robot Architecture
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
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.
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.
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.
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.
Robot agent
• Basic planner
– Takes control when no info is given by a room planner
• Navigator
• Pilot
• Controller
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).
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.)
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?
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.
