Smart Teams • University of Freiburg – Christian Ortolf – Christian Schindelhauer • University of Paderborn – Bastian Degener – Barbara Kempkes – Friedhelm Meyer auf der Heide 11 th Organic Computing Colloquium
What is a Smart Team? • A set of robots that is deployed in an unknown terrain • E.g. an outer planet or in an ocean • No remote control: The robots have to organize themselves • The robots are widely distributed • Each robot can only contact few robots nearby 08. October 2010 DFG SPP 1183 Organic Computing 2
The Challenge There is no global control guiding the Smart Team, so we need simple local rules for the robots that lead to globally good behavior • Design of local algorithms • Theoretical analysis: Worst-case analysis, competitive analysis of local distributed online algorithms • Experimental analysis using simulators 08. October 2010 DFG SPP 1183 Organic Computing 3
Smart Teams Smart Teams Communication Assignment Exploration Energy Organic Methods 08. October 2010 DFG SPP 1183 Organic Computing 4
The problem • Goal: Collectively explore a terrain modeled as a graph – With k robots – Restricted communication • Offline algorithm: Knows the graph, can subdivide robot groups optimally • Online algorithm: Graph unknown to the robots. How to subdivide? • Competitive analysis: Compare online algorithm to optimal offline algorithm (competitive factor) 08. October 2010 DFG SPP 1183 Organic Computing 5
Overview • Previous work � – Upper bound competitiveness for tree: �� ��� � � Fraigniaud et al, Networks (06) �� �� – Upper bound sparse trees: Dynia et al, MFCS 06 ��� � Ω� ��� ��� � � – Lower bound competitiveness: (shown with Jellyfish tree) Dynia et al, SIROCCO 07 08. October 2010 DFG SPP 1183 Organic Computing 6
Outlook • Simpler environments equally hard? – City-block graphs – Only convex obstacles – Only quadratic obstacles – “Nicer” placement of obstacles 08. October 2010 DFG SPP 1183 Organic Computing 7
Smart Teams Smart Teams Communication Assignment Exploration Energy Organic Methods 08. October 2010 DFG SPP 1183 Organic Computing 8
Communication: Overview • Goal: Set up and maintain short communication infrastructure within the robot team • Each robot has restricted communication range → Relay robots to forward communication • Challenge: Relays have restricted capabilities and information • Restricted viewing range • Restricted communication • Main restriction: Locality. Leads to self-organization of the relays 08. October 2010 DFG SPP 1183 Organic Computing 9
Smart Teams and Robot Formation Problems Given: � robots distributed in the Euclidean plane • Gathering problem: Gather all robots in a not predetermined point • Relay chain problem: Minimize the length of a chain of relays between two stations • Communication network problem: Minimize the length of a communication network between several stations 08. October 2010 DFG SPP 1183 Organic Computing 10
08. October 2010 DFG SPP 1183 Organic Computing 11
Previous work • Go-To-The-Middle: relay i+1 ��� � log �� time steps Kutylowski et al, BICC (06) relay i+2 relay i • Hopper: Kutylowski et al, TCS (09) ���� time steps Remove-Operation Hop-Operation Shorten-Operation 08. October 2010 DFG SPP 1183 Organic Computing 12
Time models We have looked at discrete time models: In a time step, robots can sense their neighborhood, compute, and move a distance of at most 1 (or 2). But: The closer the final configuration is approached, the smaller the movements become. Alternative cost measures incorporate the travelled distance. - Restrict a movement to distance δ per step → � -bounded model → → → - Assume continuous sensing, and continuous adaptation of movement direction to positions of neighbors (assume speed limit 1) → → → → continuous model 08. October 2010 DFG SPP 1183 Organic Computing 13
Continuous time • Assumption: Robots • continuously observe neighboring positions • react immediately (at the same time!) • Determine direction in which to move based on current position of neighbors Relay � Robots move in curves • Maximum speed: 1 08. October 2010 DFG SPP 1183 Organic Computing 14
The Move-On-Bisector strategy Each relay at each point of time: • If not positioned on line between neighbors • Move in direction of angle bisector with speed 1 • Otherwise • Stay on the line α α 08. October 2010 DFG SPP 1183 Organic Computing 15
A simulation 08. October 2010 DFG SPP 1183 Organic Computing 16
Approach • Which robot abilities are crucial to reach the formation • as fast as possible • with a given robot model • Local view • No memory ( oblivious ) • Etc. • in the continuous time model 08. October 2010 DFG SPP 1183 Organic Computing 17
Analysis Relay � � Station 1 � � � Height box � ≔ � � is lower bound for optimal global algorithm • Goal: strategy for relays with runtime dependent on � � • • Local algorithm can be compared to optimal global algorithm for a specific instance 08. October 2010 DFG SPP 1183 Organic Computing 18
Results • Upper bounds for runtime of Move-On-Bisector: � : sum of distances between neighbors (length) • � � • � � � � log � • � � � � � – � � � ���� tight bound – Local algorithm only by a � � constant slower than optimal global algorithm 08. October 2010 DFG SPP 1183 Organic Computing 19
Results � � Ω � , � � � 1 – Bad bound: � � � ���� – Better bound: �� � � � log �� = � � log � – Optimal global algorithm: Ω�1� � Open question: Is �� � � � log �� tight � for some configurations? Relay Station 08. October 2010 DFG SPP 1183 Organic Computing 20
Outlook So far: our strategies are oblivious Beyond the priority program: • Let the robots learn which algorithm to use in which situation • Use formal methods to prove that runtimes of the learned algorithms are good with high probability (models inspired by PAC learning) 08. October 2010 DFG SPP 1183 Organic Computing 21
Conclusion: Smart Teams in numbers • 4 PhDs (Miroslaw Dynia, Jaroslaw Kutylowski, Chia Ching Ooi, Bastian Degener) • 23 papers • 14 student theses • 2 project groups (12 + 11 undergraduate students) 08. October 2010 DFG SPP 1183 Organic Computing 22
Publications of Smart Teams 2010 • Degener, Bastian; Gehweiler, Joachim; Lammersen, Christiane: Kinetic Facility Location. In: Algorithmica, 2010 Degener, Bastian; Kempkes, Barbara; Meyer auf der Heide, Friedhelm: A local O( � � ) • gathering algorithm. In: SPAA 2010 • Degener, Bastian; Kempkes, Barbara; Kling, Peter; Meyer auf der Heide, Friedhelm: A continuous, local strategy for constructing a short chain of mobile robots. In: SIROCCO 2010 • Degener, Bastian; Kempkes, Barbara; Pietrzyk, Peter: A local, distributed constant-factor approximation algorithm for the dynamic facility location problem. In: IPDPS 2010 • Ooi, Chia Ching; Schindelhauer, Christian: Utilising coverage holes and wireless relays for mobile target tracking . In: IJAHUC 2010 08. October 2010 DFG SPP 1183 Organic Computing 23
Publications of Smart Teams 2009 • Bonorden, Olaf; Degener, Bastian; Kempkes Barbara; Pietrzyk, Peter: Complexity and Approximation of Geometric Local Assignment Problem. In: Proceedings of ALGOSENSORS, 2009 • Ooi, Chia Ching; Schindelhauer, Christian: Minimal Energy Path Planning for Wireless Robots. In: ACM/Springer Journal of Mobile Networks and Applications (MONET) 2009 • Jaroslaw Kutylowski, Friedhelm Meyer auf der Heide: Optimal Strategies for Maintaining a Chain of Relays between an Explorer and a Base Camp. In: Journal of Theoretical Computer Science 2009. • Ooi, Chia Ching; Schindelhauer, Christian: Smart Ring: Utilizing Coverage Holes for Mobile Target Tracking , accepted for publication in International ACM Conference on Management of Emergent Digital EcoSystems (MEDES'09), October, 2009. 08. October 2010 DFG SPP 1183 Organic Computing 24
Publications of Smart Teams 2008 • Degener, Bastian; Gehweiler, Joachim; Lammersen, Christiane: The Kinetic Facility Location Problem. In: Proceedings of the 11th Scandinavian Workshop on Algorithm Theory (SWAT), 2008 • Friedhelm Meyer auf der Heide, Barbara Schneider: Local Strategies for Connecting Stations by Small Robotic Networks. In: Proc. of 2nd IFIP International Conference on Biologically Inspired Computing (BICC’08) • Chia Ching Ooi, Christian Schindelhauer: Detours Save Energy in Mobile Wireless Networks. In: Proc. of 10th IFIP International Conference on Mobile and Wireless Communications Networks (MWCN’08) • Chia Ching Ooi, Christian Schindelhauer: Energy-Efficient Distributed Target Tracking using Wireless Relay Robots. In: 9th International Symposium on Distributed Autonomous Robotic Systems (DARS’08) 08. October 2010 DFG SPP 1183 Organic Computing 25
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