Energy-efficient Delivery by Heterogeneous Mobile Agents Andreas B - PowerPoint PPT Presentation

Energy-efficient Delivery by Heterogeneous Mobile Agents Andreas B¨ artschi J´ er´ emie Chalopin, Shantanu Das, Yann Disser, Daniel Graf, Jan Hackfeld, Paolo Penna Department of Computer Science

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 100 km 100 km 100 km 100 km 100 km 10 ℓ /100 km 100 km 100 km 100 km 100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 2 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 2 · 6 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 2 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 2 · 6 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 2 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 2 · 6 ℓ + 1 · 6 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 2 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 2 · 6 ℓ + 4 · 6 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 2 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 2 · 6 ℓ + 4 · 6 ℓ = 36 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 2 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 100 km 100 km 100 km 100 km 100 km 10 ℓ /100 km 100 km 100 km 100 km 100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ + 7 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ + 7 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ + 7 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ + 7 ℓ + 2 · 6 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ + 7 ℓ + 2 · 6 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ + 7 ℓ + 2 · 6 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ + 7 ℓ + 2 · 6 ℓ + 5 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ + 7 ℓ + 2 · 6 ℓ + 5 ℓ = 34 ℓ 10 ℓ /100 km s t Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Motivation / Toy model 7 ℓ /100 km 6 ℓ /100 km 5 ℓ /100 km 10 ℓ + 7 ℓ + 2 · 6 ℓ + 5 ℓ = 34 ℓ 10 ℓ /100 km s t We extend this with: multiple items to be delivered ( messages ) varying road lengths ( edge lengths ) many vehicles ( mobile agents ) handovers at cities ( nodes ) Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 3 / 10

Model Setting Assumptions global coordination undirected graph G = ( V , E ) handovers possible at nodes V with edges E having lengths m messages, given by Task source-target node pairs ( s i , t i ) Find a delivery schedule which minimizes anyone can use any edge overall energy cost, given by the weighted sum of each agent’s travel distance d i : Agents k agents each with capacity κ and k � w i · d i starting position p i ∈ V rate of energy consumption w i i =1 also called weights Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 4 / 10

1 Introduction Motivation Model 2 Collaboration, Planning and Coordination Collaboration Planning Coordination 3 Conclusion Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 5 / 10

Collaboration, Planning and Coordination How should the agents work together on each message? Collaboration Which route should each agent take? Planning How should the agents be assigned to the messages? Coordination Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 6 / 10

Collaboration, Planning and Coordination How should the agents work together on each message? Collaboration Defines all handover points of a message and their order. An agent then carries it between consecutive handover points. Which route should each agent take? Planning How should the agents be assigned to the messages? Coordination Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 6 / 10

Collaboration, Planning and Coordination How should the agents work together on each message? Collaboration Defines all handover points of a message and their order. An agent then carries it between consecutive handover points. Which route should each agent take? Planning Gives an order of all pick-ups and all drop-offs of each agent. How should the agents be assigned to the messages? Coordination Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 6 / 10

Collaboration, Planning and Coordination How should the agents work together on each message? Collaboration Defines all handover points of a message and their order. An agent then carries it between consecutive handover points. Which route should each agent take? Planning Gives an order of all pick-ups and all drop-offs of each agent. How should the agents be assigned to the messages? Coordination Assigns a subset of the messages to each agent. Depends on the starting position of an agent, and on its weight. Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 6 / 10

Collaboration, Planning and Coordination  How should the agents work together on each message? Collaboration     Defines all handover points of a message and their order.      An agent then carries it between consecutive handover points.      + more details!  simultaneously     Which route should each agent take?  Planning    Gives an order of all pick-ups and all drop-offs of each agent.       How should the agents be assigned to the messages? Coordination       Assigns a subset of the messages to each agent.      Depends on the starting position of an agent, and on its weight.       Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 6 / 10

Collaboration, Planning and Coordination  How should the agents work together on each message? Collaboration     Defines all handover points of a message and their order.      An agent then carries it between consecutive handover points.      + more details!  simultaneously → This includes the case of a single message .     Which route should each agent take?  Planning    Gives an order of all pick-ups and all drop-offs of each agent.  → This includes the case of a single agent .      How should the agents be assigned to the messages? Coordination       Assigns a subset of the messages to each agent.      Depends on the starting position of an agent, and on its weight.       Department of Computer Science Andreas B¨ artschi Maastricht Visit Nederland June 15, 2017 6 / 10