Biophysics-Inspired Robot Swarms
Published:
Using ideas from biology and statistical mechanics to enable swarm robot behaviors leveraging local encounters as a source of information.
Posts by Collection
portfolio
projects
Building Robot Swarms
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From low cost vibration-driven Brushbots to the Robotarium—creating physical platforms that let anyone experiment with swarm robotics.
Graph Neural Networks for Multi-Robot Coordination
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Using structured communication and learning to coordinate robot teams—going beyond simple local rules to achieve globally effective behaviors.
Resilience in Heterogeneous Multi-Robot Systems
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How do you keep a robot team working effectively when things go wrong? Algorithms that assign the right robots to the right tasks—and adapt when robots fail or conditions change.
publications
Safe open-loop strategies for handling intermittent communications in multi-robot systems
Published in Proceedings of International Conference on Robotics and Automation, 2017
Recommended citation: Siddharth Mayya, Magnus Egerstedt, "Safe open-loop strategies for handling intermittent communications in multi-robot systems", Robotics and Automation (ICRA) 2017 IEEE International Conference on, pp. 5818-5823, 2017. https://ieeexplore.ieee.org/abstract/document/7989683/
Collisions as Information Sources in Densely Packed Multi-Robot Systems Under Mean-Field Approximations
Published in Proceedings of the Robotics: Science and Systems Conference , 2017
As the spatial scale of robots decrease in multirobot systems, collisions cease to be catastrophic events that need to be avoided at all costs. This implies that less conservative, coordinated control strategies can be employed, where collisions are not only tolerated, but can potentially be harnessed as an information source. In this paper, we follow this line of inquiry by employing collisions as a sensing modality that provides information about the robots’ surroundings. We envision a collection of robots moving around with no sensors other than binary, tactile sensors that can determine if a collision occurred, and let the robots use this information to determine their locations. We apply a probabilistic localization technique based on mean-field approximations that allows each robot to maintain and update a probability distribution over all possible locations. Simulations and real multi-robot experiments illustrate the feasibility of the proposed approach, and demonstrate how collisions in multi-robot systems can indeed be employed as useful information sources.
Recommended citation: Siddharth Mayya, Pietro Pierpaoli, Girish Nair, and Magnus Egerstedt. Collisions as Information Sources in Densely Packed Multi-Robot Systems Under Mean-Field Approximations. In Proceedings of Robotics: Science and Systems, Cambridge, Massachusetts, July 2017. doi:10.15607/RSS.2017.XIII.044. http://www.roboticsproceedings.org/rss13/p44.pdf
Localization In Densely Packed Swarms using Inter-Robot Collisions as a Sensing Modality
Published in Transactions on Robotics. Accepted. To Appear, 2018
As the size of robots decreases in multi-robot systems, collisions cease to be catastrophic events that need to be avoided at all costs. This implies that less conservative, coordinated control strategies can be employed, where collisions are not only tolerated, but can potentially be harnessed as an information source. In this paper, we follow this line of inquiry by employing collisions as a sensing modality that provides information about the robots’ surroundings. We envision a collection of robots moving around with no sensors other than binary, tactile sensors that can determine if a collision occurred, and let the robots use this information to determine their locations. We apply a probabilistic localization technique based on mean-field approximations that allows each robot to maintain and update a probability distribution over all possible locations. Simulations and real multi-robot experiments illustrate the feasibility of the proposed approach.
talks
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.