Collaboration Through the Exploitation of Local Interactions in Autonomous Collective Robotics: The Stick Pulling Experiment

This article presents an experiment which investigates how collaboration in a group of simple reactive robots can be obtained through the exploitation of local interactions. A test-bed experiment is proposed in which the task of the robots is to pull sticks out of the ground —an action which requires the collaboration of two robots to be successful. The experiment is implemented in a physical setup composed of groups of 2 to 6 Khepera robots, and in Webots, a 3D simulator of Khepera robots. The results using these two implementations are compared with the predictions of a probabilistic modelling methodology (Martinoli, Ijspeert, & Mondada, 1999a; Martinoli, Ijspeert, & Gambardella, 1999b) which is here extended for the characterization and the prediction of a collaborative manipulation experiment. Instead of computing trajectories and sensory information, the probabilistic model represents the collaboration dynamics as a set of stochastic events based on simple geometrical considerations. It is shown that the probabilistic model qualitatively and quantitatively predicts the collaboration dynamics. It is significantly faster than a traditional sensor-based simulator such as Webots, and its minimal set of parameters allows the experimenter to better identify the effect of characteristics of individual robots on the team performance. Using these three implementations (the real robots, Webots and the probabilistic model), we make a quantitative investigation of the influence of the number of workers (i.e robots) and of the primary parameter of the robots’ controller —the gripping time parameter— on the collaboration rate, i.e. the number of sticks successfully taken out of the ground over time. It is found that the experiment presents two significantly different dynamics depending on the ratio between the amount of work (the number of sticks) and the number of robots, and that there is a superlinear increase of the collaboration rate with the number of robots. Furthermore, we investigate the usefulness of heterogeneity in the controllers’ parameters and of a simple signalling scheme among the robots. Results show that, compared to homogeneous groups of robots without communication, heterogeneity and signalling can significantly increase the collaboration rate when there are fewer robots than sticks, while presenting a less noticeable or even negative effect otherwise.