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.
Organization meeting, timetable. Overview of the course. Introduction to Swarm Intelligence (SI) and key principles (e.g., self-organization, stigmergy), natural and artificial examples, computational and embedded SI. 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 Salesman Problem (TSP).
- 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).
- 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.
From AS to Ant Colony Optimization (ACO). Ant-based algorithms (ABC, Ant-Net) applied to routing in telecommunication networks.
- 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).
- Dorigo M. and Stuetzle T., “Ant Colony Optimization”, MIT Press, 2004, Ch. 2 (pp. 25-46).
Introduction to mobile robotics: basic hardware and software concepts centered around the differential drive vehicle used in the course (e-puck) and the high-fidelity robotic simulator (Webots). Introduction to control architecture for mobile robots with special focus on reactive control architectures.
- 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).
- 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.
Localization methods in mobile robotics: positioning systems, odometry-based and feature-based localization. Sources of localization uncertainties and corresponding handling methods for mobile robots.
- Siegwart R. and Nourbakhsh I. R., “Introduction to Autonomous Mobile Robots”, MIT Press, 2004, Ch. 3 (pp. 47-53), Ch. 4 (pp. 145-154), Ch. 5 (pp. 181-200).
- Maybeck P. S. “Stochastic Models, Estimation, and Control”, Academic press, 1979, Ch. 1 (pp.1-16).
Collective movements in natural societies; focus on flocking phenomena. Collective movements in artificial systems: Reynolds’ virtual agents (Boids) and experiments with multi-robot systems (flocking, formation). Graph-based distributed control for spatial consensus (rendez-vous, formation).
- Reynolds C. W. “Flocks, Herds, and Schools: A Distributed Behavioral Model, in Computer Graphics”, Proc. of SIGGRAPH ’87, 21(4), pp. 25-34, 1987.
- 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.
- 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.
- 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.
- Balch T. and Arkin T. C., “Behavior-Based Formation Control for Multirobots 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.
Introduction to multi-level modeling techniques (underlying methodological framework, levels, assumptions, principles). Linear and nonlinear modeling case studies.
- 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, Journal of Robotics Research, Vol. 23, No. 4, pp. 415-436, 2004.
Calibration of model parameters; an additional challenging multi-level modeling case study (distributed seed assembly). Combined modeling and machine-learning methods for control optimization; diversity and specialization metrics.
- 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, of the Ninth Int. Symp. Experimental Robotics, June 2004, Singapore. Springer Tracts in Advanced Robotics (2006), Vol. 21, pp. 375–385.
- 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.
- 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.
- 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.
Introduction to unsupervised multi-agent machine-learning techniques for automatic design and optimization: terminology and classification, Particle Swarm Optimization (PSO), performance comparison with Genetic Algorithms. Application of machine-learning techniques to automatic control design and optimization of single-robot systems. Noisy and expensive optimization problems; noise-resistant algorithms.
- 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.
- 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.
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). Advanced techniques for expensive and noisy optimization problems.
- 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.
- 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.
Division of labor and task-allocation mechanisms: threshold-based algorithms, market-based algorithms and comparisons between the two algorithmic classes.
- 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”. 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.
- 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.
Collective decision-making and introduction to wireless sensor networks.
- Culler D., Estrin D., and Srivastava M., “Guest Editors’ Introduction: Overview of Sensor Networks”. IEEE Computer, Vol. 37, No. 8, pp.41-49, 2004.
- Cianci C., Raemy X., Pugh J., and Martinoli A., “Communication in a Swarm of Miniature Robots: The e-puck as an Educational Tool for Swarm Robotics”. of the SAB 2006 Workshop on Swarm Robotics, September-October 2006, Rome, Italy. Lecture Notes in Computer Science (2007), Vol. 4433, pp. 103-115.
- Halloy J., Sempo G., Caprari G., Rivault C., Asadpour M., Tâche F., Saïd I., Durier V., Canonge S., Amé J.M., Detrain C., Correll N., Martinoli A., Mondada F., Siegwart R. R., and Deneubourg J. L., “Social integration of robots in groups of cockroaches to control self-organized choices”. Science, 318(5853): 1155-1158, 2007.
- Hamman H., “Swarm Robotics: A Formal Approach”, Springer, Ch. 6 (pp. 129-162).
- Bose T., Reina A., and Marshall J. AR, “Collective decision-making”. Current Opinion in Behavioral Sciences, 16:30-34, 2017.
- Cianci C., Pugh J, and Martinoli A. “Exploration of an Incremental Suite of Microscopic Models for Acoustic Event Monitoring Using a Robotic Sensor Network”. of the 2008 IEEE Int. Conf. on Robotics and Automation, May 2008, Pasadena, U.S.A., pp. 3290-3295.
- Valentini G., Hamann H., and Dorigo M., “Self-Organized Collective Decision Making: The Weighted Voter Model”, of the ACM Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems, May 2014, Paris, France, pp. 45-52.
Distributed environmental sensing using static wireless sensor networks.
- Barrenetxea G., Ingelrest F., Schaefer G. and Vetterli M., “The Hitchhiker’s Guide to Successful Wireless Sensor Network Deployments”. of the 6th ACM Conference on Embedded Networked Sensor Systems (SenSys 2008). Raleigh, NC, USA, 5-7 November 2008.
- Evans W. C., Bahr A., and Martinoli A., “Evaluating Efficient Data Collection Algorithms for Environmental Sensor Networks”. 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.
- Prorok A., Cianci C. M., and Martinoli A., “Towards Optimally Efficient Field Estimation with Threshold-Based Pruning in Real Robotic Sensor Networks”. 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., “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.
Distributed environmental sensing using mobile and robotic sensor networks. General take home messages of the course. Discussion about student feedback for the course.
- 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,” of the Int. Conf. on Embedded Wireless Systems and Networks, February 2017, Uppsala, Sweden, pp. 12-23.
- Lochmatter T. and Martinoli A., “Tracking an Odor Plume in a Laminar Wind Field with Bio-Inspired Algorithms”. of the Eleventh Int. Symp. Experimental Robotics, July 2008, Athens, Greece, Springer Tracts in Advanced Robotics (2008), Vol. 54, pp. 473-482, 2008.
- Soares J. M., Aguiar A. P., Pascoal A. M., and Martinoli A., “A Distributed Formation-based Odor Source Localization Algorithm – Design, Implementation, and Wind Tunnel Evaluation,” IEEE Int. Conf. on Robotics and Automation, 2015, pp. 1830–1836.
- Marjovi A., Arfire A., and Martinoli A., “High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks”. of the 2015 IEEE Int. Conf. on Distributed Computing in Sensor Systems, June 2015, Fortaleza, Brazil, Boston, U.S.A., pp. 11-20.
- Arfire A., Marjovi A., and Martinoli A., “Mitigating slow dynamics of low-cost chemical sensors for mobile air quality monitoring sensor networks,” of the Int. Conf. on Embedded Wireless Systems and Networks, February 2016, Graz, Austria, pp. 159-167.
- Quraishi A., Bahr A., Schill F., and Martinoli A., “Autonomous Feature Tracing and Adaptive Sampling in Real-World Underwater Environments,” IEEE Int. Conf. on Robotics and Automation, 2018, pp. 5699-5704.
Course project defenses (Tue and Wed in regular class room).