Multi-Robot Task Allocation (MRTA)

Allocates tasks with temporal constraints and uncertain durations to a multi-robot system.

Includes three allocation algorithms:

• Temporal-sequential single item auctions (TeSSI)[1].
• Temporal-sequential single item auctions with degree of strong controllability (TeSSI-DSC) (based on [1] and [3]).
• Temporal-sequential single item auctions with static robust execution (TeSSI-SREA) (based on [1] and [2])

Each robot maintains a temporal network with its tasks. The temporal network is either a:

• Simple Temporal Network (STN)
• Simple Temporal Network with Uncertainties (STNU)
• Probabilistic Simple Temporal Network (PSTN)

The temporal network represents a Simple Temporal Problem (STP).

The mrta_stn repository includes the temporal network models and solvers for the STP.

The system consists of a fms (fleet managements system), a robot proxy and a robot instance per physical robot in the fleet.

Brief description of the components:

FMS:

• Gets tasks' plan from pickup to delivery and adds it to the task.
• Requests the auctioneer to allocate tasks

Auctioneer

• Announces unallocated tasks to the robot proxies in the local network, opening an allocation round.
• Receives bids from the robot bidders.
• Elects a winner per allocation round or throws an exception indicating that no allocation was possible in the current round.

Dispatcher

• Gets earliest task and checks schedulability condition (a task is schedulable x time before its start time).
• Adds action between current robot's position and the task's pickup location.
• Dispatches a task queue to the schedule execution monitor.

Timetable Monitor

• Receives task-status messages
• Updates the corresponding robot's timetable accordingly and triggers recovery measures if necessary.

Fleet Monitor

• Update robot's positions based on robot-pose messages.

PerformanceTracker

• Updates performance metrics during allocation, scheduling and execution

Simulator

• Controls simulation time using simpy

RobotProxy

Acts on behalf of the robot

Bidder

• Receives task announcements.
• Computes a bid per task received in the task announcement. Bid calculation is dependant of the allocation method.
• Sends its best bid to the auctioneer.

Timetable Monitor

• Same as the timetable monitor, but only updates the robot's proxy timetable.

Robot

Physical robot (in this case, just a mockup)

Schedule Monitor

• Receives a task queue and schedules the first task in the queue.
• Sends the task to the executor.
• Receives task-status messages from the executor and monitors the execution of the task.
• Triggers recovery measures in case the current task violates the temporal constraints and the next task is at risk.

Executor

• Determines the duration of actions based on a duration graph (travel time based on historical information) and sends task-status msgs.

API:

• Provides middleware functionality

ccu_store

• interface to interact with the ccu db

robot_store

• interface to interact with the robot db

robot_proxy_store

• interface to interact with the robot proxy db

Documentation

Create documentation using sphinx

Go to /docs and run

make html

Go to /docs/_build/html and open the documentation in a web browser.

Installation

Create directory for logger

sudo mkdir -p /var/log/mrta
sudo chown -R $USER:$USER /var/log/mrta

Available approaches are specified in mrs/config/default/approaches.yaml

Using Docker

Install docker

Install docker-compose

Go to mrs/tests and run

python3 run.py approach_name

Example:

python3 run.py tessi-dsc-corrective-preempt

Without Docker

Install the repositories

• mrta_stn

• ropod_common

Get the requirements:

pip3 install -r requirements.txt

Add the task_allocation to your PYTHONPATH by running:

pip3 install --user -e .

Open a terminal per robot proxy and run

python3 robot_proxy.py ropod_001 --approach approach_name

Open a terminal per robot and run

python3 robot.py ropod_001 --approach approach_name

Run in another terminal

python3 ccu.py  --approach approach_name

Go to /tests and run test in another terminal

python3 test.py --approach approach_name

References

[1] E. Nunes, M. Gini. Multi-Robot Auctions for Allocation of Tasks with Temporal Constraints. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015

[2] Lund et al. 2017. Robust Execution of Probabilistic Temporal Plans. In Proc. of the 31th Lund et al. 2017. Robust Execution of Probabilistic Temporal Plans. In Proc. of the 31th Conference on Artificial Intelligence (AAAI. 2017)

[3] Akmal et al. 2019. Quantifying Degrees of Controllability for Temporal Networks with Uncertainty. In Proc of the 29th International Conference on Automated Planning and Scheduling (ICAPS-2019).