2 research outputs found
OptSample: A Resilient Buffer Management Policy for Robotic Systems based on Optimal Message Sampling
Modern robotic systems have become an alternative to humans to perform risky
or exhausting tasks. In such application scenarios, communications between
robots and the control center have become one of the major problems. Buffering
is a commonly used solution to relieve temporary network disruption. But the
assumption that newer messages are more valuable than older ones is not true
for many application scenarios such as explorations, rescue operations, and
surveillance. In this paper, we proposed a novel resilient buffer management
policy named OptSample. It can uniformly sampling messages and dynamically
adjust the sample rate based on run-time network situation. We define an
evaluation function to estimate the profit of a message sequence. Based on the
function, our analysis and simulation shows that the OptSample policy can
effectively prevent losing long segment of continuous messages and improve the
overall profit of the received messages. We implement the proposed policy in
ROS. The implementation is transparent to user and no user code need to be
changed. Experimental results on several application scenarios show that the
OptSample policy can help robotic systems be more resilient against network
disruption