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Jonne Zutt

Delft University of Technology

Information Technology and Systems

Collective Agent Based Systems Group

Fault detection and recovery in multi-modaltransportation networks with autonomous mobile actors

TRAIL/TNO Project 16

Supervisors

Dr. C. Witteveen

Dr. ir. Z. Papp

Dr. ir. A.J.C. van Gemund

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Content

1. Outline of the project

2. Problem setting:Transport Planning Problem

3. Set-up of experiments

4. Preliminary results of experiments

5. Achievements / Future plans

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Project Characteristics

“Fault detection and recovery in multi-modal transportation networks with autonomous mobile actors”

• Planning, fault detection and recovery• Multi-agent approach• Multi-layered approach for distributed planning• Operational aspect of multi-modal transportation

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Applications• Autonomous Guided Vehicle (AGV) terminal,

– FTAM-5/6 (Davinci)– Simple infrastructures with capacity restrictions and many

conflicts• Taxi-cab companies,

– SMM-6 (M.M. de Weerdt)– Medium infrastructure sizes, few capacity restrictions

• Freight transportation, distribution centers– FTAM-1 (L.D. Aronson)– Large infrastructures without capacity restrictions

• Multi-Agent diagnosis– STW: Distributed Model-Based Diagnosis and Repair– Fault detection and recovery

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Transport Planning Problem• Transportation orders

• Infrastructure resources

• Transport resources

• Agents

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TPP – Orders

O = (f, v, s, Ts, d, Td, l, u, p)

f, v freight identifier / volume,s, d source / destination location,Ts, Td source / delivery time-window,l, u loading / unloading costs,p penalty.

Transport Planning Problem:

• Transportation orders

• Infrastructure resources

• Transport resources

• Agents

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TPP – Infrastructure• Transportation orders

• Infrastructure resources

• Transport resources

• Agents

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TPP – Infrastructure model• Transportation orders

• Infrastructure resources

• Transport resources

• Agents

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TPP – Transport resources• Transportation orders

• Infrastructure resources

• Transport resources

• Agents

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TPP – Agent architecture

Infrastructureresources

Transportresources

Transportationorders

CUS

TAC

OPR CRA

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Incident Management

What are incidents? Any event from outside the planning system that

cannot be anticipated with certainty.• new orders, changes in orders• road blocks, traffic jams• malfunctional vehicles

What is incident management?• Ensuring the correct operation of a system under

the events of incidents• Detection, repair and notification of problems

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Contents

1. Outline of the project

2. Problem setting:Transport Planning Problem

3. Set-up of experiments

4. Preliminary results of experiments

5. Achievements / Future plans

www.rsTRAIL.nl

Generating infrastructures

• Locations:Retrieve a list of related postal codes, convert to latitude / longitude,then to (x, y) coordinates,

• Arcs:Paul Bourke’s efficient triangulation algorithm (for terrain modeling)– As equilateral as possible (avoiding wedge shaped

triangles),– Fast O(n·log(n)).

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Generating transportation orders

1. Generate a (possibly infeasible) set of transportation orders using several statistical functions,

2. Generate a feasible set • Create random plans for the transport agents by

just letting them drive around,• Extract a set of orders they could have been

executing (using a density parameter),

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Contents

1. Outline of the project

2. Problem setting:Transport Planning Problem

3. Set-up of experiments

4. Preliminary results of experiments

5. Achievements / Future plans

www.rsTRAIL.nl

Distributed operational planning

• Job-shop Scheduling with BlockingHatzack & Nebel (ECP 2001)

• JS scheduling: find an optimal allocation of a set R of scarce resources to a set of activities (jobs) J over time

• Blocking means that a resource is claimed by a job until it claims the next resource

• Agent plan: ((IR1, 0-2), (IR2, 5-7), (IR3, 8-9) …)

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Experiments

• Used 3 different infrastructures,

• 20 transport agents each execute one order,

• Randomly chosen source-, destination location and fixed time-window,

• H&N algorithm with rerouting.

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Results (averaged over 100 problem instances)

Number of alternatives

Ave

rage

% o

f de

lay

Number of alternatives

Tar

dine

ss

Tardiness aA Ca - a if Ca< aDelay { aA (Ca – Ma) / Ca } / |A|

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Achievements

• AGV Terminal Demonstrator (delayed, Mar’03)

• D**: dynamic (re)routing algorithm for AGVs in a terminal [FTAM02 / TRAIL02]

• Distributed operational planning using Hatzack & Nebel’s approach [BNAIC02]

• Development Uptime tool for multi-agent based diagnosis [SPIE02]

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Future Plans

• Complete problem instances for the experiments,

• Survey on routing and conflict resolution algorithms,

• Build Incident Generator,• Redo experiment, this time influenced by

incidents, • Delivering an efficient and robust

demonstrator.

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The End

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Example infrastructure (1)

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Example infrastructure (2)

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Need for complex experiments

• AGV terminals usually have very simple infrastructures.– This is to keep things easy, not efficient,

– As terminals become larger, the problem will return.

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Properties of these infrastructures

• Many routes from a source location to a destination location,

• The arcs (and their cost values) are reasonably realistic.

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Tactical Level (1)

• Responsible for finding plans and keeping those in line with reality

• Customers may add and remove orders, as well as change them

• Problems that cannot be handled by the operational planners must be dealt with at the tactical level

• Output is a list of order assignments foreach operational agent

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Tactical Level (2)

• Planning is done by means of a heuristic function, which tells which agent should execute an order. If no agent can be found, a reordering is necessary

• Replanning is done by removing the affected orders and offer them to the planner again

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Operational Level (1)

• Responsible for performing the tasks they have been assigned

• Tactical layer may add and remove orders as well as change them

• In the event of incidents, the operational planner should detect these, and try to fix

within the bounds set by the tacticallayer. Incidents that cannot be dealt with,

are reported to the higher level

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Operational Level (2)

• Adaptive route planning, adapting to usage levels of the roads. Agents will take another route if they see a road is congested

• Traffic control at crossroads to ensure that only one agent can make use of a crossroad. Traffic flow is being maximized

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TPP – Transport resources• Transportation orders

• Infrastructure resources

• Transport resources

• Agents

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TPP - Agents

• Transportation orders

• Infrastructure resources

• Transport resources

• Agents

Customers

Planners

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customer agents

interface,auction mechanism

tactical agents

operational agents /cross road agents

infrastructure/ transport resources

orders

TPP – Agent architecture