Package: pyswarming
pyswarmingpyswarmingis a research toolkit for Swarm Robotics.InstallationYou can installpyswarmingfrom PyPI using pip (Recommended):pip install pyswarmingDependenciespyswarming's dependencies are:numpy,numdifftoolsandmatplotlib.DocumentationThe official documentation is hosted onReadTheDocs.Algorithms coveredThis library includes the following algorithms to be used in swarm robotics:Leaderless heading consensus: the collective performs heading consensus [^1];Inverse power: ajustable attraction and repulsion laws [^2];Spring: allows the robots to maintain a desired distance between them [^2];Force law: mimics the gravitational force [^3];Repulsive force: makes the individuals repulse each other [^4];Body force: introduces a body force that considers the radii of the robots [^4];Inter robot spacing: allows the robots to maintain a desired distance between them [^5];Dissipative: a dissipative force that reduces the "energy" of the robots [^5];Leader following: the collective performs heading consensus with a leader [^6];Collision avoidance: the robot stays away from neighbors in the vicinity [^7];Attraction alignment: the robot becomes attracted and aligned [^7];Preferred direction: the robot has a preference to move toward a preset direction [^7];Lennard-Jones: allows the formation of lattices [^8];Virtual viscosity: a viscous force that reduces the "oscillation" of the robots [^8];Modified attraction alignment: the robot becomes attracted and aligned by considering a “social importance” factor [^9];Heading consensus: the collective performs heading consensus [^10];Perimeter defense: the robots maximize the perimeter covered in an unknown environment [^10];Environment exploration: provides spatial coverage [^10];Aggregation: makes all the individuals aggregate collectively [^11];Alignment: the collective performs heading consensus [^11];Geofencing: attract the robots towards area A [^11];Repulsion: makes all the individuals repulse collectively [^11];Target: the robot goes to an specific target location [^11];Area coverage: using the Geofencing and Repulsion algorithms [^11];Collective navigation: using the Target and Repulsion algorithms [^11];Flocking: using the Aggregation, Repulsion and Alignment algorithms [^11];[^1]: T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, and O. Shochet, “Novel Type of Phase Transition in a System of Self-Driven Particles,” Phys. Rev. Lett., vol. 75, no. 6, pp. 1226–1229, Aug. 1995.https://doi.org/10.1103/PhysRevLett.75.1226.[^2]: J. H. Reif and H. Wang, “Social potential fields: A distributed behavioral control for autonomous robots,” Robot. Auton. Syst., vol. 27, no. 3, pp. 171–194, May 1999.https://doi.org/10.1016/S0921-8890(99)00004-4.[^3]: W. M. Spears and D. F. Gordon, “Using artificial physics to control agents,” in Proceedings 1999 International Conference on Information Intelligence and Systems (Cat. No.PR00446), Bethesda, MD, USA: IEEE Comput. Soc, 1999, pp. 281–288.https://doi.org/10.1109/ICIIS.1999.810278.[^4]: D. Helbing, I. Farkas, and T. Vicsek, “Simulating dynamical features of escape panic,” Nature, vol. 407, no. 6803, pp. 487–490, Sep. 2000.https://doi.org/10.1038/35035023.[^5]: N. E. Leonard and E. Fiorelli, “Virtual leaders, artificial potentials and coordinated control of groups,” presented at the IEEE Conference on Decision and Control, 2001.https://doi.org/10.1109/CDC.2001.980728.[^6]: A. Jadbabaie, Jie Lin, and A. S. Morse, “Coordination of groups of mobile autonomous agents using nearest neighbor rules,” IEEE Trans. Autom. Control, vol. 48, no. 6, pp. 988–1001, Jun. 2003.https://doi.org/10.1109/TAC.2003.812781.[^7]: I. D. Couzin, J. Krause, N. R. Franks, and S. A. Levin, “Effective leadership and decision-making in animal groups on the move,” Nature, vol. 433, no. 7025, pp. 513–516, Feb. 2005.https://doi.org/10.1038/nature03236.[^8]: C. Pinciroli et al., “Lattice Formation in Space for a Swarm of Pico Satellites,” in Ant Colony Optimization and Swarm Intelligence, M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stützle, and A. F. T. Winfield, Eds., in Lecture Notes in Computer Science, vol. 5217. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 347–354.https://doi.org/10.1007/978-3-540-87527-7_36.[^9]: R. Freeman and D. Biro, “Modelling Group Navigation: Dominance and Democracy in Homing Pigeons,” J. Navig., vol. 62, no. 1, pp. 33–40, Jan. 2009.https://doi.org/10.1017/S0373463308005080.[^10]: M. Chamanbaz et al., “Swarm-Enabling Technology for Multi-Robot Systems,” Front. Robot. AI, vol. 4, Apr. 2017.https://doi.org/10.3389/frobt.2017.00012.[^11]: B. M. Zoss et al., “Distributed system of autonomous buoys for scalable deployment and monitoring of large waterbodies,” Auton. Robots, vol. 42, no. 8, pp. 1669–1689, Dec. 2018.https://doi.org/10.1007/s10514-018-9702-0.Citing PySwarmingIf you make use of PySwarming for your research, please cite ourJOSS publication. Here is the corresponding BibTeX entry:@article{deAndrade2023,
    doi = {10.21105/joss.05647},
    url = {https://doi.org/10.21105/joss.05647},
    year = {2023},
    publisher = {The Open Journal},
    volume = {8},
    number = {89},
    pages = {5647},
    author = {Emerson Martins de Andrade and Antonio Carlos Fernandes and Joel Sena Sales},
    title = {PySwarming: a research toolkit for Swarm Robotics},
    journal = {Journal of Open Source Software}
}Examples using pyswarming.swarm# importing the swarm creatorimportpyswarming.swarmaspsRepulsion# creating the swarmmy_swarm=ps.Swarm(n=10,# number of robotslinear_speed=0.5,# linear speed of each robotdT=1.0,# sampling timedeployment_point_limits=[[0.0,0.0,0.0],[5.0,5.0,0.0]],# lower and upper limits for the position deploymentdeployment_orientation_limits=[[0.0,0.0,0.0],[0.0,0.0,2*3.1415]],# lower and upper limits for the orientation deploymentdistribution_type='uniform',# type of distribution used to deploy the robotsplot_limits=[[-50.0,50.0],[-50.0,50.0]],# plot limits x_lim, y_limbehaviors=['repulsion'])# list of behaviorsmy_swarm.simulate()Collective navigation# creating the swarmmy_swarm=ps.Swarm(n=10,# number of robotslinear_speed=0.5,# linear speed of each robotdT=1.0,# sampling timedeployment_point_limits=[[0.0,0.0,0.0],[5.0,5.0,0.0]],# lower and upper limits for the position deploymentdeployment_orientation_limits=[[0.0,0.0,0.0],[0.0,0.0,2*3.1415]],# lower and upper limits for the orientation deploymentdistribution_type='uniform',# type of distribution used to deploy the robotsplot_limits=[[-50.0,50.0],[-50.0,50.0]],# plot limits x_lim, y_limbehaviors=['collective_navigation'])# list of behaviorsmy_swarm.behaviors_dict['r_out']['collective_navigation']['alpha']=2.0# setting the strength of the repulsionmy_swarm.behaviors_dict['r_out']['collective_navigation']['T']=[-40,-40,0]# setting the targetmy_swarm.simulate()Target + Aggregation# creating the swarmmy_swarm=ps.Swarm(n=10,# number of robotslinear_speed=0.5,# linear speed of each robotdT=1.0,# sampling timedeployment_point_limits=[[0.0,0.0,0.0],[5.0,5.0,0.0]],# lower and upper limits for the position deploymentdeployment_orientation_limits=[[0.0,0.0,0.0],[0.0,0.0,2*3.1415]],# lower and upper limits for the orientation deploymentdistribution_type='uniform',# type of distribution used to deploy the robotsplot_limits=[[-50.0,50.0],[-50.0,50.0]],# plot limits x_lim, y_limbehaviors=['target','aggregation'])# list of behaviorsmy_swarm.behaviors_dict['r_out']['target']['T']=[-40,-40,0]# setting the targetmy_swarm.simulate()Other ExamplesConsidering a swarm of robots, they can show different behaviors by usingpyswarming. The following codes are simplified implementations, for detailed ones, see theexamplesfolder.# importing the swarming behaviorsimportpyswarming.behaviorsaspb# importing numpy to work with arraysimportnumpyasnpTargetTo simplify, considering just one robot.# define the robot (x, y, z) positionrobot_position_i=np.asarray([0.,0.,0.])# set the robot speedrobot_speed_i=1.0# define a target (x, y, z) positiontarget_position=np.asarray([8.,8.,0.])fortinrange(15):# print the robot (x, y, z) positionprint(robot_position_i)# update the robot (x, y, z) positionrobot_position_i+=robot_speed_i*pb.target(robot_position_i,target_position)AggregationConsidering four robots.# define each robot (x, y, z) positionrobot_position=np.asarray([[8.,8.,0.],[-8.,8.,0.],[8.,-8.,0.],[-8.,-8.,0.]])# set the robot speedrobot_speed=1.0fortime_iinrange(15):# print the robot (x, y, z) positionsprint(robot_speed)# update the robot (x, y, z) positionsforr_indinrange(len(robot_speed)):r_i=robot_speed[r_ind]r_j=np.delete(robot_speed,np.array([r_ind]),axis=0)robot_speed[r_ind]+=robot_speed*pb.aggregation(r_i,r_j)RepulsionConsidering four robots.# define each robot (x, y, z) positionrobot_position=np.asarray([[1.,1.,0.],[-1.,1.,0.],[1.,-1.,0.],[-1.,-1.,0.]])# set the robot speedrobot_speed=1.0fortime_iinrange(15):# print the robot (x, y, z) positionsprint(robot_position)# update the robot (x, y, z) positionsforr_indinrange(len(robot_position)):r_i=robot_position[r_ind]r_j=np.delete(robot_position,np.array([r_ind]),axis=0)robot_position[r_ind]+=robot_speed*pb.repulsion(r_i,r_j,3.0)Aggregation + RepulsionConsidering four robots.# define each robot (x, y, z) positionrobot_position=np.asarray([[8.,8.,0.],[-8.,8.,0.],[8.,-8.,0.],[-8.,-8.,0.]])# set the robot speedrobot_speed=1.0fortime_iinrange(15):# print the robot (x, y, z) positionsprint(robot_position)# update the robot (x, y, z) positionsforr_indinrange(len(robot_position)):r_i=robot_position[r_ind]r_j=np.delete(robot_position,np.array([r_ind]),axis=0)robot_position[r_ind]+=s_i*(pb.aggregation(r_i,r_j)+pb.repulsion(r_i,r_j,5.0))Contributing to pyswarmingAll kind of contributions are welcome:Improvement of code with new features, bug fixes, and  bug reportsImprovement of documentationAdditional testsFollow the instructionsherefor submitting a PR (Pull Request).If you have any ideas or questions, feel free to open an issue.AcknowledgementsThe authors would like to thank the Human Resources Program from the National Agency of Oil, Gas and Bio Combustibles – PRH-ANP for the financial support.
This work was supported by "Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES)",LOC/COPPE/UFRJ(Laboratory of Waves and Current-Federal University of Rio de Janeiro) and the National Council for Scientific and Technological Development (CNPq), which are gratefully acknowledged.