The emerging collective motions of swarms of interacting agents are a subject of great interest in application areas ranging from biology to physics and robotics. In this paper, we conduct a careful analysis of the collective dynamics of a swarm of self-propelled heterogeneous, delay-coupled agents. We show the emergence of collective motion patterns and segregation of populations of agents with different dynamic properties; both of these behaviors (pattern formation and segregation) emerge naturally in our model, which is based on self-propulsion and attractive pairwise interactions between agents. We derive the bifurcation structure for emergence of different swarming behaviors in the mean field as a function of physical parameters and verify these results through simulation.

This paper establishes the method of controlled Lagrangian particle tracking (CLPT) to analyse the offsets between physical positions of marine robots in the ocean and simulated positions of controlled particles in an ocean model. This offset (which we term CLPT error) has characteristics that are not previously seen in free-drifting ocean sampling platforms with no active control. CLPT error growth over time is exponential until it reaches a turning point that depends only on the resolution of the ocean model, after which the error growth is bounded by polynomial functions of time. In the ideal case, a theoretical lower bound on the steady-state CLPT error can be derived. These characteristics are proved theoretically for particles moving in a planar flow field. The method of CLPT may be applied to improve the accuracy of ocean circulation models and navigation performance of marine robots.

The wide applications of cyber-physical systems (CPS) call for effective design strategies that optimize the performance of both computing units and physical plants.We study the task scheduling problem for a class of CPS whose behaviors are regulated by feedback control laws. We co-design the control law and the task scheduling algorithm for predictable performance and power consumption for both the computing and the physical systems. We use a typical example, multiple inverted pendulums controlled by one processor, to illustrate our method.