1 research outputs found
Improved Swarm Engineering: Aligning Intuition and Analysis
We present a set of metrics intended to supplement designer intuitions when
designing swarm-robotic systems, increase accuracy in extrapolating swarm
behavior from algorithmic descriptions and small test experiments, and lead to
faster and less costly design cycles. We build on previous works studying
self-organizing behaviors in autonomous systems to derive a metric for swarm
emergent self-organization. We utilize techniques from high performance
computing, time series analysis, and queueing theory to derive metrics for
swarm scalability, flexibility to changing external environments, and
robustness to internal system stimuli such as sensor and actuator noise and
robot failures. We demonstrate the utility of our metrics by analyzing four
different control algorithms in two scenarios: an indoor warehouse object
transport scenario with static objects and a spatially unconstrained outdoor
search and rescue scenario with moving objects. In the spatially constrained
warehouse scenario, efficient use of space is key to success so algorithms that
use mechanisms for traffic regulation and congestion reduction are the most
appropriate. In the search and rescue scenario, the same will happen with
algorithms which can cope well with object motion through dynamic task
allocation and randomized search trajectories. We show that our intuitions
about comparative algorithm performance are well supported by the quantitative
results obtained using our metrics, and that our metrics can be synergistically
used together to predict collective behaviors based on previous results in some
cases