SUMMARY

Traditionally, robotic systems are built to move as fast as possible. In contrast to this, we investigate slowness and its effects on heterogeneous robotic teams inspired by biological systems.
An assignment problem for static targets and a team pursuit problem for heterogeneous evaders are addressed.
The value of slowness in solving these problems optimally is examined. We further assemble the optimal teams for given problems by finding a compromise between performance and energy consumption or monetary cost.
The results are validated in simulation and implemented on a robotic testbed.