Victor - PowerPoint Presentation

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Victor

Marmol vmarmol@andrew.cmu.eduAdvisor: M. Bernardine Dias, Ph.D. Mentor: Balajee Kannan, Ph.D.

Market-Based Coordination of

Recharging

Robots

Autonomous recharging is becoming increasingly important to mobile robotics as it has the potential to greatly enhance the operational time and capability of robots. Current research in autonomous recharging favors a mobile robot that can recharge a group of worker robots. Existing approaches however have little to no coordination between a recharging agent and its workers. This leads to less efficient interactions which adversely affect the performance of the team of robots, especially as the ratio of worker robots to recharging robots increases. Therefore, improved coordination can greatly enhance the performance of such a team. Furthermore, recharging robots could have capabilities beyond recharging and can thus further contribute to team performance. The current literature does not

address these additional roles that can be played by recharging robots. The proposed senior thesis will advance the state of the art in autonomous coordination by developing, implementing, testing, and evaluating a market-based distributed algorithm for effectively coordinating recharging robots. The developed solution will be evaluated in a long-duration exploration task where the environment being explored is unknown and dynamic, the team of robots is heterogeneous, and some of these robots are capable of both exploring and recharging other robots.

Abstract

Necessary for any group of mobile robots that are to be effective beyond a short amount of time

Mobile and static recharging units allow a group of robots to recharge when necessaryRobots must be charge aware and know what to do when recharging is triggered

Autonomous Recharging

Utilize a simulated economy for task allocation, where robots buy and sell tasks via auctions according to their estimated costs for completion

Market-based systems are dynamic and distributed which provides flexibility and fault-tolerance for many domains

Market-Based Coordination

Acknowledgements: I would like to thank my advisor Dr. M. Bernardine Dias and my mentor Dr. Balajee Kannan for their help and support. I would also like to thank Jimmy Bourne, M. Freddy Dias, Nisarg Kothari, and Sairam Yamanoor for their work on the robot hardware and software. Finally, I want to thank everyone in the rCommerce Group for developing and maintaining the current system and robots.

Tasks are auctioned by agents

An agent’s bid is determined by their objective cost functionLowest costing bid wins the taskTasks are placed on an agent’s tour

TraderBots

Runtime = distance in meters traversable by robot before battery depletion.

Battery discharge patterns are consistent with a significant linear region.Battery resolution: tenths of a volt from 12.5 – 11.5 volts.Resolution too coarse, need to create more.Two models of discharge: in motion and while idle.Robot updates distance to empty estimation at 2HzRuntime is recalculated at new sighting of coarse resolution reading.

Estimating Distance to Empty

Pairwise

cost functions calculate the cost between two tasts.Following cost functions reflect inserting a recharging task into the schedule if necessary

Cost Functions

TraderBots

scheduler can’t add tasks while scheduling.Implemented custom TraderBots scheduler which recognizes recharge tasks during and after auctionsSchedule is optimized by trimming duplicate recharging tasks and moving the recharge task where its cost is minimized in the tour.

Inserting Into Tours

Recharging aware system with robots going home to recharge

Incorporate mobile rechargersAllow rechargers to take on multiple roles

Thesis Roadmap

Mobile recharging agent docking with a worker . Real hardware and CAD model.

Task

Task

Robot

Robot

Robot

An auction for two tasks with three bidding robots. Arrows are bids,

green arrows are winning bids. Cost metric is distance.

3 city tour in

TraderBots

S : Literature Survey, F : Familiarization, P : Presentation, L : Long-term Test, T : Thesis Writing

Graph

Cost

TaskToTask

(

Task

task1,

Task

task2)

// We can assume that we have at least enough charge to recharge If ((estimatedDistanceToEmpty – distance(task1Pos, task2Pos) – distance(task2Pos, homePos)) > 0) // Can reach task2 and recharge from there estimatedDistanceToEmpty -= distance(task1Pos, task2Pos) Return distance(task1Pos, task2Pos) Else // Can’t reachtask2 and recharge from there, must // insert the cost of going home to recharge estimatedDistanceToEmpty = FullBatteryDistanceToEmpty Return distance(task1Pos, homePos) + distance(homePos, task2Pos)

Cost NullToTask(Null nullTask, Task task2) If (estimatedDistanceToEmpty < distance(currentPos, homePos)) // We can’t go home and recharge now estimatedDistanceToEmpty = 0 Return Infinity Else // Treat as a task from the current position to task1 Return TaskToTask(currentPosAsTask, task1);

Task to Task Cost Function: Calculates the cost of performing the second task given the completion of the first task.

Null to Task Cost Function: Calculates the cost of performing the first task given the robot’s current location and state.

r

untime -= distanceTraveledInTimestep + idleToMeters(timeIdleInTimestep)

Tasks are auctioned by agents

An agent’s bid is determined by their objective cost functionLowest costing bid wins the taskTasks are placed on an agent’s tour

TraderBots

3 city tour in

TraderBots