Shape formation by programmable particles

Shape formation (or pattern formation ) is a basic distributed problem for systems of computational mobile entities. Intensively studied for systems of autonomous mobile robots, it has recently been investigated in the realm of programmable matter , where entities are assumed to be small and with severely limited capabilities. Namely, it has been studied in the geometric Amoebot model, where the anonymous entities, called particles , operate on a hexagonal tessellation of the plane and have limited computational power (they have constant memory), strictly local interaction and communication capabilities (only with particles in neighboring nodes of the grid), and limited motorial capabilities (from a grid node to an empty neighboring node); their activation is controlled by an adversarial scheduler. Recent investigations have shown how, starting from a well-structured configuration in which the particles form a (not necessarily complete) triangle, the particles can form a large class of shapes. This result has been established under several assumptions: agreement on the clockwise direction (i.e., chirality ), a sequential activation schedule, and randomization (i.e., particles can flip coins to elect a leader). In this paper we obtain several results that, among other things, provide a characterization of which shapes can be formed deterministically starting from any simply connected initial configuration of n particles. The characterization is constructive: we provide a universal shape formation algorithm that, for each feasible pair of shapes $$(S_0, S_F)$$ ( S 0 , S F ) , allows the particles to form the final shape $$S_F$$ S F (given in input) starting from the initial shape $$S_0$$ S 0 , unknown to the particles. The final configuration will be an appropriate scaled-up copy of $$S_F$$ S F depending on n . If randomization is allowed, then any input shape can be formed from any initial (simply connected) shape by our algorithm, provided that there are enough particles. Our algorithm works without chirality, proving that chirality is computationally irrelevant for shape formation. Furthermore, it works under a strong adversarial scheduler, not necessarily sequential. We also consider the complexity of shape formation both in terms of the number of rounds and the total number of moves performed by the particles executing a universal shape formation algorithm. We prove that our solution has a complexity of $$O(n^2)$$ O ( n 2 ) rounds and moves: this number of moves is also asymptotically worst-case optimal.

[1]  Masafumi Yamashita,et al.  Distributed Anonymous Mobile Robots: Formation of Geometric Patterns , 1999, SIAM J. Comput..

[2]  Nicola Santoro,et al.  Line Recovery by Programmable Particles , 2018, ICDCN.

[3]  Aristides A. G. Requicha,et al.  Self-assembly and self-repair of arbitrary shapes by a swarm of reactive robots: algorithms and simulations , 2010, Auton. Robots.

[4]  Erik Winfree,et al.  Active self-assembly of algorithmic shapes and patterns in polylogarithmic time , 2013, ITCS '13.

[5]  P. Rothemund Folding DNA to create nanoscale shapes and patterns , 2006, Nature.

[6]  Radhika Nagpal,et al.  Programmable self-assembly in a thousand-robot swarm , 2014, Science.

[7]  Dana Randall,et al.  A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems , 2016, PODC.

[8]  Yukiko Yamauchi,et al.  Pattern Formation by Oblivious Asynchronous Mobile Robots , 2015, SIAM J. Comput..

[9]  Christian Scheideler,et al.  Universal coating for programmable matter , 2016, Theor. Comput. Sci..

[10]  Christian Scheideler,et al.  Leader Election and Shape Formation with Self-organizing Programmable Matter , 2015, DNA.

[11]  Julien Bourgeois,et al.  A distributed self-reconfiguration algorithm for cylindrical lattice-based modular robots , 2016, 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA).

[12]  Christian Scheideler,et al.  Improved Leader Election for Self-organizing Programmable Matter , 2017, ALGOSENSORS.

[13]  Tommaso Toffoli,et al.  Programmable Matter: Concepts and Realization , 1993, Int. J. High Speed Comput..

[14]  Erik D. Demaine,et al.  Self-Assembly of Arbitrary Shapes Using RNAse Enzymes: Meeting the Kolmogorov Bound with Small Scale Factor (extended abstract) , 2010, STACS.

[15]  Gregory S. Chirikjian,et al.  Kinematics of a metamorphic robotic system , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[16]  Masafumi Yamashita,et al.  Formation and agreement problems for synchronous mobile robots with limited visibility , 1995, Proceedings of Tenth International Symposium on Intelligent Control.

[17]  Masafumi Yamashita,et al.  Characterizing geometric patterns formable by oblivious anonymous mobile robots , 2010, Theor. Comput. Sci..

[18]  Christian Scheideler,et al.  An Algorithmic Framework for Shape Formation Problems in Self-Organizing Particle Systems , 2015, NANOCOM.

[19]  Othon Michail,et al.  Terminating distributed construction of shapes and patterns in a fair solution of automata , 2015, Distributed Computing.

[20]  Nicola Santoro,et al.  Forming sequences of geometric patterns with oblivious mobile robots , 2015, Distributed Computing.

[21]  Nancy M. Amato,et al.  Distributed reconfiguration of metamorphic robot chains , 2004, PODC '00.

[22]  Nicola Santoro,et al.  Arbitrary pattern formation by asynchronous, anonymous, oblivious robots , 2008, Theor. Comput. Sci..

[23]  Shlomi Dolev,et al.  In-vivo energy harvesting nano robots , 2016, 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE).

[24]  Matthew J. Patitz An introduction to tile-based self-assembly and a survey of recent results , 2014, Natural Computing.

[25]  Ke Li,et al.  Slime Mold Inspired Path Formation Protocol for Wireless Sensor Networks , 2010, ANTS Conference.

[26]  Erik Winfree,et al.  Universal Computation and Optimal Construction in the Chemical Reaction Network-Controlled Tile Assembly Model , 2015, DNA.

[27]  Christian Scheideler,et al.  Universal Shape Formation for Programmable Matter , 2016, SPAA.

[28]  Paul G. Spirakis,et al.  On the transformation capability of feasible mechanisms for programmable matter , 2019, J. Comput. Syst. Sci..

[29]  Christian Scheideler,et al.  On the runtime of universal coating for programmable matter , 2016, Natural Computing.