pyswarming 1.0.0

A research toolkit for Swarm Robotics

pyswarming

pyswarming is a research toolkit for Swarm Robotics.

Installation

You can install pyswarming from PyPI using pip (Recommended):

pip install pyswarming

Dependencies

pyswarming's dependencies are: numpy.

Documentation

The official documentation is hosted on ReadTheDocs.

Algorithms covered

This library includes the following algorithms to be used in swarm robotics:

• Leaderless Coordination: the collective performs heading consensus [^1];
• Leader Following: the collective performs heading consensus with a leader [^2];
• Collision Avoidance: the robot stays away from neighbors in the vicinity [^3];
• Attraction and Alignment: the robot becomes attracted and aligned [^3];
• Preferred Direction: the robot has a preference to move toward a preset direction [^3];
• Modified Attraction and Alignment: the robot becomes attracted and aligned by considering a “social importance” factor [^4];
• Heading Consensus: the collective performs heading consensus [^5];
• Perimeter Defense: the robots maximize the perimeter covered in an unknown environment [^5];
• Aggregation: makes all the individuals aggregate collectively [^6];
• Alignment: the collective performs heading consensus [^6];
• Geofencing: attract the robots towards area A [^6];
• Repulsion: makes all the individuals repulse collectively [^6];
• Target: the robot goes to an specific target location [^6];

[^1]: Vicsek T, Czirók A, Ben-Jacob E, Cohen I, Shochet O. Novel Type of Phase Transition in a System of Self-Driven Particles. Phys Rev Lett 1995;75:1226–9. https://doi.org/10.1103/PhysRevLett.75.1226.

[^2]: Jadbabaie A, Jie Lin, Morse AS. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans Automat Contr 2003;48:988–1001. https://doi.org/10.1109/TAC.2003.812781.

[^3]: Couzin ID, Krause J, Franks NR, Levin SA. Effective leadership and decision-making in animal groups on the move. Nature 2005;433:513–6. https://doi.org/10.1038/nature03236.

[^4]: Freeman R, Biro D. Modelling Group Navigation: Dominance and Democracy in Homing Pigeons. J Navigation 2009;62:33–40. https://doi.org/10.1017/S0373463308005080.

[^5]: Chamanbaz M, Mateo D, Zoss BM, Tokić G, Wilhelm E, Bouffanais R, et al. Swarm-Enabling Technology for Multi-Robot Systems. Front Robot AI 2017;4. https://doi.org/10.3389/frobt.2017.00012.

[^6]: Zoss BM, Mateo D, Kuan YK, Tokić G, Chamanbaz M, Goh L, et al. Distributed system of autonomous buoys for scalable deployment and monitoring of large waterbodies. Auton Robot 2018;42:1669–89. https://doi.org/10.1007/s10514-018-9702-0.

Examples

Considering a swarm of robots, they can show different behaviors by using pyswarming. The following codes are simplified implementations, for detailed ones, see the Examples folder.

# importing the swarming behaviors
import pyswarming.behaviors as ps

# importing numpy to work with arrays
import numpy as np

Target

To simplify, considering just one robot.

# define the robot (x, y, z) position
r_i = np.asarray([0., 0., 0.])

# set the robot linear velocity
s_i = 1.0

# define a target (x, y, z) position
T = np.asarray([8., 8., 0.])

for t in range(15):

    # print the robot (x, y, z) position
    print(r_i)

    # update the robot (x, y, z) position
    r_i += s_i*ps.target(r_i, T)

Aggregation

Considering four robots.

# define each robot (x, y, z) position
r = np.asarray([[8., 8., 0.],
                [-8., 8., 0.],
                [8., -8., 0.],
                [-8., -8., 0.]])

# set the robot linear velocity
s_i = 1.0

for t in range(15):

    # print the robot (x, y, z) positions
    print(r)

    # update the robot (x, y, z) positions
    for r_ind in range(len(r)):
        r_i = r[r_ind]
        r_j = np.delete(r, np.array([r_ind]), axis=0)
        r[r_ind] += s_i*ps.aggregation(r_i, r_j)

Repulsion

Considering four robots.

# define each robot (x, y, z) position
r = np.asarray([[1., 1., 0.],
                [-1., 1., 0.],
                [1., -1., 0.],
                [-1., -1., 0.]])

# set the robot linear velocity
s_i = 1.0

for t in range(15):

    # print the robot (x, y, z) positions
    print(r)

    # update the robot (x, y, z) positions
    for r_ind in range(len(r)):
        r_i = r[r_ind]
        r_j = np.delete(r, np.array([r_ind]), axis=0)
        r[r_ind] += s_i*ps.repulsion(r_i, r_j, 3.0)

Aggregation + Repulsion

Considering four robots.

# define each robot (x, y, z) position
r = np.asarray([[8., 8., 0.],
                [-8., 8., 0.],
                [8., -8., 0.],
                [-8., -8., 0.]])

# set the robot linear velocity
s_i = 1.0

for t in range(15):

    # print the robot (x, y, z) positions
    print(r)

    # update the robot (x, y, z) positions
    for r_ind in range(len(r)):
        r_i = r[r_ind]
        r_j = np.delete(r, np.array([r_ind]), axis=0)
        r[r_ind] += s_i*(ps.aggregation(r_i, r_j) + ps.repulsion(r_i, r_j, 5.0))

Contributing to pyswarming

All kind of contributions are welcome:

• Improvement of code with new features, bug fixes, and bug reports
• Improvement of documentation
• Additional tests

Follow the instructions here for submitting a PR.

If you have any ideas or questions, feel free to open an issue.

Acknowledgements

This research is supported by CAPES (Coordination of Improvement of Higher Education Personnel), LOC/COPPE/UFRJ (Laboratory of Waves and Current - Federal University of Rio de Janeiro) and CNPq (Brazilian National Council for Scientific and Technological Development), which are gratefully acknowledged.