Why animals swirl and how they group

We report a possible solution for the long-standing problem of the biological function of swirling motion, when a group of animals orbits a common center of the group. We exploit the hypothesis that learning processes in the nervous system of animals may be modelled by reinforcement learning (RL) and apply it to explain the phenomenon. In contrast to hardly justified models of physical interactions between animals, we propose a small set of rules to be learned by the agents, which results in swirling. The rules are extremely simple and thus applicable to animals with very limited level of information processing. We demonstrate that swirling may be understood in terms of the escort behavior, when an individual animal tries to reside within a certain distance from the swarm center. Moreover, we reveal the biological function of swirling motion: a trained for swirling swarm is by orders of magnitude more resistant to external perturbations, than an untrained one. Using our approach we analyze another class of a coordinated motion of animals—a group locomotion in viscous fluid. On a model example we demonstrate that RL provides an optimal disposition of coherently moving animals with a minimal dissipation of energy.

Adaptive Coordinated Motion Constraint Control for Cooperative Multi-manipulator Systems

A cooperative multi-manipulator system is a nonlinear, time-varying, strong coupling system with multiple inputs and outputs. Because of external disturbance, load change and other factors, a cooperative multi-manipulator system needs to address the problems of uncertain environment interaction, feasible control methods and coordinated planning. In this study, the coordinated motion of a multi-manipulator is divided into two types: coupling and superposition motions. An adaptive coordinated motion constraint scheme is proposed for the two different motion forms. The coupled and superposition motions were investigated by coordinating a carrying task and a circle drawing task, respectively, and the effectiveness of the adaptive coordinated motion scheme was verified. Based on the adaptive kinematics constraint algorithm of the multi-manipulator, co-simulation analysis of the multi-manipulator coordinated motion is conducted. Simulation results show that the multi-manipulator preserves the coordination relationship at all points of the trajectory planning path and has a good motion effect. Finally, an experimental platform for a cooperative multi-manipulator system was built to conduct experimental research on the coordinated motion of the multi-manipulator. Experimental results show that the proposed adaptive motion constraint control scheme for cooperative multi-manipulator systems has a good coordination effect, strong adaptivity and high-control precision.