Jill Goryca, Richard Hill American Control Conference June 17, 2013

 Background  System Models  Control Software Implementation  Task Planning Algorithm  Simulation Results  Conclusion

 Goal: ◦ To control two or more robots that work together  Challenges: ◦ Coordinate robot actions ◦ Respond to changing goals and conditions ◦ Scale small examples to larger ones  Practical applications: ◦ Surveillance, search & rescue, and firefighting

 Develop tools necessary to address complex scenarios  Tools include: MATLAB Control Software Task-Planning Optimization

 Apply supervisory control framework ◦ Allow controllable and uncontrollable events ◦ Assume all events are observable ◦ Formally ensure safety and nonblocking  Use a relatively simple, but illustrative scenario involving control of two robots

Components:  2 robots  4 tasks  4 regions Rules:  Complete all tasks ◦ 1 before 2, same robot ◦ 3 before 4, same robot  Different regions

 Finite State Machine (FSM) represents region boundaries and task locations  One FSM for each robot  Initial location of robot is marked with an arrow (Robot A starts in region 5) Geographical Constraints

 Represents the rule that the robot must finish a task before it can start another one Task Contraints

 Represents the order of task completion  Task 1 before task 2 by the same robot  Task 3 before task 4 by the same robot Task Completion

 Represents the rule that robots may not be in the same region at the same time Avoidance

 Use UMDES/DESUMA software  Combine multiple FSMs to synthesize controller that meets goals  Contains 638 states and 1666 transitions  Text file format (*.fsm) is input to MATLAB control software

Interface High-Level Control Low-Level Control (D*Lite, VFH, Mapping) Simulation (Player/Stage) Planner (Main Control File) User InputFinite State Machine MATLAB Tool Intermediary (Goal Plan, Detect Region Events) Optimization (Calculate Costs, Dijkstra) Offline Online User Data

Interface High-Level Control Low-Level Control (D*Lite, VFH, Mapping) Simulation (Player/Stage) Planner (Main Control File) User InputFinite State Machine MATLAB Tool Intermediary (Goal Plan, Detect Region Events) Optimization (Calculate Costs, Dijkstra) Offline Online User Data

 Script file generated from MATLAB Tool  Name of Initial and Final State  User Data matrices: ◦ States—from FSM file ◦ Events—from MATLAB Tool ◦ Tasks—from MATLAB Tool ◦ Regions—from MATLAB Tool

Interface High-Level Control Low-Level Control (D*Lite, VFH, Mapping) Simulation (Player/Stage) Planner (Main Control File) User InputFinite State Machine MATLAB Tool Intermediary (Goal Plan, Detect Region Events) Optimization (Calculate Costs, Dijkstra) Offline Online User Data

 MATLAB high-level control code  Written off-line  References User Data file generated by MATLAB Tool  Controls 1 robot  “While” loop completes “mission” of FSM  Executes optimal path through FSM  Receives events from both robots (when crossing borders and finishing tasks)

Interface High-Level Control Low-Level Control (D*Lite, VFH, Mapping) Simulation (Player/Stage) Planner (Main Control File) User InputFinite State Machine MATLAB Tool Intermediary (Goal Plan, Detect Region Events) Optimization (Calculate Costs, Dijkstra) Offline Online User Data

 Detects “uncontrollable” events (border crossing)  Compares robot current position to all user- defined regions  Converts list of region names to event names for calling function (Goal Plan)

 Commands controllable events, sending robot to goal until event is detected.  Uses low-level algorithms ◦ D*Lite ◦ VFH ◦ Mapping  When uncontrollable event is detected, returns event names to calling function (Main Control File)

Interface High-Level Control Low-Level Control (D*Lite, VFH, Mapping) Simulation (Player/Stage) Planner (Main Control File) User InputFinite State Machine MATLAB Tool Intermediary (Goal Plan, Detect Region Events) Optimization (Calculate Costs, Dijkstra) Offline Online User Data

 Plan the best path through FSM  “Best” is defined as shortest time ◦ (shortest time = shortest distance)  Path is used to: ◦ Control actions of robots ◦ Determine when mission is complete  Path can be re-optimized if costs change

 Determines cost matrix for Dijkstra’s algorithm.  Cost is defined as straight-line distance between robot position and task locations.  New robot position after completion of task is taken into account.

 Plans minimum-cost path through FSM  Sums total cost of all edges (events)  Does not distinguish between robots

 MATLAB high-level control algorithm  Utilizes both robots working simultaneously  Sums cost for each robot individually

 Player/Stage simulation software  Robot A completed tasks 3 and 4  Robot B completed tasks 1 and

 Developed MATLAB control software and optimization algorithms ◦ MATLAB Tool  Generates a data structure from FSM and user input that maps events to low-level functions ◦ Planner  Commands controllable events based on optimal path  Detects uncontrollable events ◦ Optimization  Calculate costs as distance to task  Choose optimal path through FSM for two robots

 Expand MATLAB tool for additional DES applications with controllable and uncontrollable events.  Further test control software ◦ More complex models, actual hardware  Improve optimization algorithms ◦ Save cost information that has not changed, only choose from controllable events