We investigate algorithmic control of a large swarm of mobile particles (such as robots, sensors, or building material) that
move in a 2D workspace using a global input signal (such as gravity or a magnetic field). Upon activation of the field, each
particle moves maximally in the same direction until forward progress is blocked by a stationary obstacle or another
stationary particle. In an open workspace, this system model is of limited use because it has only two controllable degrees
of freedom—all particles receive the same inputs and move uniformly. We show that adding a maze of obstacles to the
environment can make the system drastically more complex but also more useful. We provide a wide range of results for a
wide range of questions. These can be subdivided into external algorithmic problems, in which particle configurations
serve as input for computations that are performed elsewhere, and internal logic problems, in which the particle configurations
themselves are used for carrying out computations. For external algorithms, we give both negative and positive
results. If we are given a set of stationary obstacles, we prove that it is NP-hard to decide whether a given initial
configuration of unit-sized particles can be transformed into a desired target configuration. Moreover, we show that finding
a control sequence of minimum length is PSPACE-complete. We also work on the inverse problem, providing constructive
algorithms to design workspaces that efficiently implement arbitrary permutations between different configurations. For
internal logic, we investigate how arbitrary computations can be implemented. We demonstrate how to encode dual-rail
logic to build a universal logic gate that concurrently evaluates AND, NAND, NOR, and OR operations. Using many of these
gates and appropriate interconnects, we can evaluate any logical expression. However, we establish that simulating the full
range of complex interactions present in arbitrary digital circuits encounters a fundamental difficulty: a FAN-OUT gate cannot
be generated. We resolve this missing component with the help of 2 9 1 particles, which can create FAN-OUT gates that
produce multiple copies of the inputs. Using these gates we provide rules for replicating arbitrary digital circuits.
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Coordinated Particle Relocation with Global Signals and Local Friction (Media Exposition)
In this video, we present theoretical and practical methods for achieving arbitrary reconfiguration of a set of objects, based on the use of external forces, such as a magnetic field or gravity: Upon actuation, each object is pushed in the same direction. This concept can be used for a wide range of applications in which particles do not have their own energy supply or in which they are subject to the same global control commands. A crucial challenge for achieving any desired target configuration is breaking global symmetry in a controlled fashion. Previous work (some of which was presented during SoCG 2015) made use of specifically placed barriers; however, introducing precisely located obstacles into the workspace is impractical for many scenarios. In this paper, we present a different, less intrusive method: making use of the interplay between static friction with a boundary and the external force to achieve arbitrary reconfiguration. Our key contributions are theoretical characterizations of the critical coefficient of friction that is sufficient for rearranging two particles in triangles, convex polygons, and regular polygons; a method for reconfiguring multiple particles in rectangular workspaces, and deriving practical algorithms for these rearrangements. Hardware experiments show the efficacy of these procedures, demonstrating the usefulness of this novel approach.
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- NSF-PAR ID:
- 10163095
- Date Published:
- Journal Name:
- 36th International Symposium on Computational Geometry (SoCG 2020)
- Volume:
- 164
- Page Range / eLocation ID:
- 72:1--72:5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.4230/LIPIcs.SoCG.2020.72
