Lecture

Lecture notes and reading material

Preliminary lecture notes will be available in PDF format before the class (usually Monday evening) while definitive lecture notes will be available only after the class has been held, in a timely fashion (usually, at latest a couple of days after the lecture).

Lecture notes will be complemented by possible reading material listed on the syllabus and further pointers, all available on the student area. Due to copyright issues, electronic copies of the material are only available to EPFL students officially enrolled in this course. Students interested in downloading this material can do so from the student area by logging in using with their GASPAR account.

Week 1

TOPIC

Course organization (credits, workload, logistics) and content overview. Introduction to Swarm Intelligence (SI) and key principles, natural and artificial examples. Foraging, trail laying/following mechanisms. Open-space, multi-source foraging experiments: biological data and microscopic models. From real to virtual ants: Ant System (AS), the first combinatorial optimization algorithm based on ant trail/following principles. Application to a classical operational research problem: the Traveling Salesperson Problem (TSP).

READING

Primary Literature:

  • Bonabeau E., Dorigo M., and Theraulaz G., “Swarm Intelligence: From Natural to Artificial Systems”, SantaFe Studies in the Sciences of Complexity, Oxford University Press, 1999, Ch. 1 (pp.1-23) and Ch. 2 (pp. 25-36 and 39-56).

Secondary Literature:

  • Martinoli A., “Collective Complexity out of Individual Simplicity”. Invited book review on “Swarm Intelligence: From Natural to Artificial Systems”, by Bonabeau E., Dorigo M., and Theraulaz G. Artificial Life, Vol. 7, No. 3, pp. 315-319, 2001.
  • Beni G., “From Swarm Intelligence to Swarm Robotics”. In Şahin E. and Spears W., editors, Proc. of the SAB 2004 Workshop on Swarm Robotics, Santa Monica, CA, USA, July, 2004. Lecture Notes in Computer Science (2005), Vol. 3342, pp. 1-9.

Week 2

TOPIC

From AS to Ant Colony Optimization (ACO). Ant-based algorithms (ABC, Ant-Net) applied to routing in telecommunication networks.

READING

Primary:

  • Bonabeau E., Dorigo M., and Theraulaz G., “Swarm Intelligence: From Natural to Artificial Systems”, Santa Fe Studies in the Sciences of Complexity, Oxford University Press, 1999, Ch. 2 (pp. 80-107).

Secondary:

  • Dorigo M. and Stuetzle T., “Ant Colony Optimization”, MIT Press, 2004, Ch. 2 (pp. 25-46).

Week 3

TOPIC

Introduction to mobile robotics: basic concepts centered around the differential drive vehicle used in the course (e-puck) and the high-fidelity, open-source robotic simulator (Webots). Introduction to control architecture for mobile robots with special focus on reactive control architectures.

READING

Primary:

  • Michel O., “Webots: Professional Mobile Robot Simulation”. Int. J. of Advanced Robotic Systems, 1: 39-42, 2004.
  • Mondada F., Bonani M., Raemy X., Pugh J., Cianci C., Klaptocz A., Magnenat S., Zufferey J.-C., Floreano D., Martinoli A., “The e-puck, a Robot Designed for Education in Engineering”. Proc. of the 9th Conference on Autonomous Robot Systems and Competitions, May 2009, Castelo Branco, Portugal, Vol.1, pp. 59-65.
  • Siegwart R. and Nourbakhsh I. R., “Introduction to Autonomous Mobile Robots”, MIT Press, 2004, Ch. 4 (pp. 89-98).

Secondary:

  • Brooks R., “A Robust Layered Control System for a Mobile Robot”. IEEE Trans. on Robotics and Automation, 2(1): 14-23, 1986.
  • Arkin R. C., “Motor Schema Based Mobile Robot Navigation”. Int. J. of Robotics Research, 8(4): 92-112, 1989.

Week 4

TOPIC

Localization methods in mobile robotics: positioning systems, odometry-based and feature-based localization. Sources of localization uncertainties and corresponding handling methods for mobile robots.

READING

Primary:

  • Siegwart R. and Nourbakhsh I. R., “Introduction to Autonomous Mobile Robots”, MIT Press, 2004, Ch. 3 (pp. 47-53), Ch. 4 (pp. 145-154).

Week 5

TOPIC

Localization methods in mobile robotics: uncertainties in wheel-based odometry, Kalman filters for 2D localization. Collective movements in natural societies; focus on flocking phenomena. Collective movements in artificial systems: Reynolds’ virtual agents (Boids).

READING

Primary:

  • Siegwart R. and Nourbakhsh I. R., “Introduction to Autonomous Mobile Robots”, MIT Press, 2004, Ch. 5 (pp. 181-200).
  • Maybeck P. S. “Stochastic Models, Estimation, and Control”, Academic press, 1979, Ch. 1 (pp.1-16).
  • Reynolds C. W. “Flocks, Herds, and Schools: A Distributed Behavioral Model, in Computer Graphics”, Proc. of SIGGRAPH ’87, 21(4), pp. 25-34, 1987.

Secondary:

  • Siegwart R. and Nourbakhsh I. R., “Introduction to Autonomous Mobile Robots”, MIT Press, 2004, Ch. 5 (212-214; 227-244).

Week 6

TOPIC

Experiments with multi-robot systems on flocking and formation (behavior-based). Graph-based distributed control for spatial consensus (rendez-vous, formation).

READING

Primary:

  • Fredslund J. and Matarić M. J., “A General, Local Algorithm for Robot Formations”, IEEE Transactions on Robotics and Automation,, special issue on Advances in Multi-Robot Systems, Vol. 18, p.5, pp. 837-846, 2002.
  • Gowal S., “A Framework for Graph-Based Distributed Rendezvous of Nonholonomic Multi-Robot Systems”, EPFL Thesis no. 5845, Ch. 6 and 7 (pp. 49-60), 2013.
  • Falconi R., Gowal S., and Martinoli A., “Graph-Based Distributed Control of Non-Holonomic Vehicles Endowed with Local Positioning Information Engaged in Escorting Missions”. Proc. of the 2010 IEEE Int. Conf. on Robotics and Automation, May 2010, Anchorage, AK, U.S.A., pp. 3207-3214.

Secondary

  • Balch T. and Arkin T. C., “Behavior-Based Formation Control for Multirobot Teams”. IEEE Trans. on Robotics and Automation, 1998, Vol. 14, No. 6, pp. 926-939.
  • Pugh J., Raemy X., Favre C., Falconi R., and Martinoli A., “A Fast On-Board Relative Positioning Module for Multi-Robot Systems”. Special issue on Mechatronics in Multi-Robot Systems, Chow M.-Y., Chiaverini S., Kitts C., editors, IEEE Trans. on Mechatronics, 14(2): 151-162, 2009.
  • Ren W., Beard R. W., and Atkins E. M., “A Survey of Consensus Problems in Multi-Agent Coordination”, Proc. of the 2005 American Control conference, pp. 1859-1864, 2005.

Week 7

TOPIC

Introduction to multi-level modeling techniques (underlying methodological framework, levels, assumptions, principles); linear example; calibration of model parameters.

READING

Primary

  • Lerman K., Martinoli A., and Galstyan A., “A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems”. In Sahin E. and Spears W., editors, Proc. of the SAB 2004 Workshop on Swarm Robotics, July 2004, Santa Monica, CA, USA. Lecture Notes in Computer Science (2005), Vol. 3342, pp. 143-152.
  • Martinoli A., Easton K., and Agassounon W., “Modeling of Swarm Robotic Systems: A Case Study in Collaborative Distributed Manipulation”. Special Issue on Experimental Robotics, Siciliano B., editor, Int. Journal of Robotics Research, Vol. 23, No. 4, pp. 415-436, 2004.

Secondary

  • Correll N. and Martinoli A., “Collective Inspection of Regular Structures using a Swarm of Miniature Robots”. In Ang Jr., M.H. and Khatib, O., editors, Proc. of the Ninth Int. Symp. Experimental Robotics, June 2004, Singapore. Springer Tracts in Advanced Robotics (2006), Vol. 21, pp. 375–385.

Week 8

TOPIC

Challenging multi-level modeling case studies (distributed seed assembly and collaborative stick-pulling). Combined modeling and machine-learning methods for control optimization; diversity and specialization metrics.

READING

Primary

  • Agassounon W., Martinoli A., and Easton K., “Macroscopic Modeling of Aggregation Experiments using Embodied Agents in Teams of Constant and Time-Varying Sizes”. Autonomous Robots, special issue on Swarm Robotics, Dorigo M. and Sahin E., editors, 17(2-3): 163-192, 2004.
  • Li L., Martinoli A., and Abu-Mostafa Y., “Learning and Measuring Specialization in Collaborative Swarm Systems”. Special issue on Mathematics and Algorithms of Social Insects, Balch T. and Anderson C., editors, Adaptive Behavior, 12, No. 3-4, pp. 199-212, 2004.

Secondary

  • Martinoli A., Ijspeert A. J., and Gambardella L. M., “A Probabilistic Model for Understanding and Comparing Collective Aggregation Mechanisms”. In Floreano D., Mondada F., and Nicoud J.-D., editors, of the Fifth Europ. Conf. on Artificial Life, September 1999, Lausanne, Switzerland. Lectures Notes in Artificial Intelligence (1999), Vol. 1674, pp. 575–584.

Week 9

TOPIC

Introduction to machine-learning techniques and metaheuristic optimization: terminology and classification. Particle Swarm Optimization (PSO): algorithm and performances on benchmark functions. Application of metaheuristic methods to automatic control design and optimization of single-robot systems.

READING

Primary

  • Eberhart R. C. and Kennedy J., “A New Optimizer using Particle Swarm Theory”. of the  Sixth  IEEE Int. Symp. Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39–43.
  • Shi, Y. H., Eberhart, R. C. “A Modified Particle Swarm Optimizer” of the IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May 1998, pp. 69-73.
  • Poli R., Kennedy J., and Blackwell T., “Particle Swarm Optimization: An Overview”. Swarm Intelligence Journal, 1(1): 33-57, 2007.
  • Pugh J., Zhang Y., and Martinoli A., “Particle Swarm Optimization for Unsupervised Robotic Learning”. of the Second IEEE Symp. on Swarm Intelligence, Pasadena, CA, USA, June 2005, pp. 92-99.
  • Engelbrecht A. P., “Particle Swarm Optimization: Where Does it Belong?” of the Third IEEE Symp. on Swarm Intelligence, Indianapolis, IN, USA, May 2006, pp. 48-54.

Secondary

  • Floreano D. and Mondada F., “Evolution of Homing Navigation in a Real Mobile Robot”, IEEE Trans. on System, Man, and Cybernetics: Part B, 26(3): 396-407, 1996.
  • Lipson, H., Pollack J. B., “Automatic Design and Manufacture of Artificial Lifeforms”, Nature, 406: 974-978, 2000.
  • Bongard J., Zykov V., Lipson H. (2006), “Resilient Machines Through Continuous Self-Modeling”, Science 314. no. 5802, pp. 1118 – 1121.

Week 10

TOPIC

Noisy and expensive optimization problems; noise-resistant algorithms. Application of machine-learning techniques to automatic control design and optimization of multi-robot systems. Specific issues for automatic control design and optimization in distributed systems (e.g., credit assignment problem).

READING

Primary

  • Pugh J. and Martinoli A., “Distributed Scalable Multi-Robot Learning using Particle Swarm Optimization”. Swarm Intelligence Journal, 3(3): 203-222, 2009.
  • Di Mario E. and Martinoli A., “Distributed Particle Swarm Optimization for Limited Time Adaptation with Real Robots”. Chirikjian G. and Hsieh A., editors, Special issue on Distributed Robotics, Robotica, 32(2): 193-208,
  • Di Mario E., Navarro I., and Martinoli A., “Analysis of Fitness Noise in Particle Swarm Optimization: From Robotic Learning to Benchmark Functions”. of the 2014 IEEE Congress on Evolutionary Computation, July 2014, Beijing, China, pp. 2785-2792.
  • Di Mario E., Navarro I., and Martinoli A., “A Distributed Noise-Resistant Particle Swarm Optimization Algorithm for High-Dimensional Multi-Robot Learning”, of the 2015 IEEE International Conference on Robotics and Automation, May 2015, pp. 5970–5976.

Secondary

  • C. Chen, J. Lin, E. Yücesan, and S. E. Chick, “Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization,” Discrete Event Dynamic Systems: Theory and Applications, pp. 251–270, 2000.
  • Di Mario E., Navarro I., and Martinoli A., “Distributed Particle Swarm Optimization using Optimal Computing Budget Allocation for Multi-Robot Learning,” in IEEE Congress on Evolutionary Computation, 2015, pp. 566–572.

Week 11

TOPIC

Division of labor and task-allocation mechanisms: threshold-based algorithms and market-based algorithms; comparison between threshold-based and market-based algorithms.

READING

Primary

  • Stentz A., Dias M. B., “A free market architecture for coordinating multiple robots”. Technical report CMU-RI-TR-99-42, Robotics Institute, Carnegie Mellon University, December 1999.
  • Bonabeau E., Dorigo M., and Theraulaz G., “Swarm Intelligence: From Natural to Artificial Systems”, SantaFe Studies in the Sciences of Complexity, Oxford University Press, 1999, pp. 109-139 (Chapter 3).
  • Kalra N. and Martinoli A., “A Comparative Study between Threshold-Based and Market-Based Task Allocation”. Proc. of the Eight Int. Symp. on Distributed Autonomous Robotic Systems, July 2006, Minneapolis/St. Paul, MN, U.S.A. Distributed Autonomous Robotic Systems 7 (2006), pp. 91–102.

Secondary

  • Dias M. B., Zlot R., Kalra N., and Stentz A., “Market-Based Multirobot Coordination: A Survey and Analysis”. IEEE Proceedings, 94(7): 1257-1270, 2006.
  • Agassounon W. and Martinoli A., “Efficiency and Robustness of Threshold-Based Distributed Allocation Algorithms in Multi-Agent Systems”. of the First ACM Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems, July 2002, Bologna, Italy, pp. 1090–1097.

Week 12

TOPIC

Division of labor and task-allocation mechanisms (continuation).

READING

Same as Week 11

Week 13

TOPIC

Distributed environmental sensing using static wireless sensor networks.

READING

Primary

  • Culler D., Estrin D., and Srivastava M., “Guest Editors’ Introduction: Overview of Sensor Networks”. IEEE Computer, Vol. 37, No. 8, pp.41-49, 2004.
  • Barrenetxea G., Ingelrest F., Schaefer G. and Vetterli M., “The Hitchhiker’s Guide to Successful Wireless Sensor Network Deployments”. Proc. of the 6th ACM Conference on Embedded Networked Sensor Systems (SenSys 2008). Raleigh, NC, USA, 5-7 November 2008.
  • Prorok A., Cianci C. M., and Martinoli A., “Towards Optimally Efficient Field Estimation with Threshold-Based Pruning in Real Robotic Sensor Networks”. Proc. of the 2010 IEEE Int. Conf. on Robotics and Automation, May 2010, Anchorage, AK, U.S.A, pp. 5453-5459.
  • Evans W. C., Bahr A., and Martinoli A., “Evaluating Efficient Data Collection Algorithms for Environmental Sensor Networks”. Proc. of the Tenth Int. Symp. on Distributed Autonomous Robotic Systems, November 2010, Lausanne, Switzerland; Springer Tracts in Advanced Robotics (2013), Vol. 83, pp. 77-90.

Secondary

  • Evans W. C., Bahr A., and Martinoli A., “Distributed Spatiotemporal Suppression for Environmental Data Collection in Real-World Sensor Networks”. of the 2013 IEEE Int. Conf. on Distributed Computing in Sensor Systems, May 2013, Boston, U.S.A., pp. 70-79.

Week 14

TOPIC

Distributed environmental sensing using robotic sensor networks. General take home messages of the course. Discussion about student feedback for the course.

READING

Primary

  • Marjovi A., Arfire A., and Martinoli A., “Extending Urban Air Pollution Maps beyond the Coverage of a Mobile Sensor Network: Data Sources, Methods, and Performance Evaluation,” Proc. of the Int. Conf. on Embedded Wireless Systems and Networks, February 2017, Uppsala, Sweden, pp. 12-23.
  • Arfire A., Marjovi A., and Martinoli A., “Mitigating slow dynamics of low-cost chemical sensors for mobile air quality monitoring sensor networks,” Proc. of the Int. Conf. on Embedded Wireless Systems and Networks, February 2016, Graz, Austria, pp. 159-167.