Boosting Cost-Efficiency in Robotics: A Distributed Computing Approach
for Harvesting Robots
Abstract
Multi-arm harvesting robots offer a promising solution to the labor
shortage in fruit harvesting, due to their ability to improve harvesting
efficiency. However, multi-arm harvesters necessitate additional visual
sensors to acquire distribution information of fruits within larger
working spaces. Greater demands are consequently imposed on graphics
computation, leading to increased costs in computing hardware of robot
system. To balance the graphics computing cost and reduce energy
consumption, distributed graphics computation frameworks for multi-arm
robot vision system are proposed in this study. First, a host-edge
framework is proposed to assign the tasks of image inference and depth
alignment to host computer and edge computing modules through a
decentralized mode of local connection. Moreover, to increase the
endurance time of robot in application, the edge computing modules are
reduced and the fifth generation mobile communication is integrated into
robot graphics computing system to transfer on-board image processing to
a remote computing server with MQTT protocol. To verify the
effectiveness of the proposed framework, comprehensive experiments were
performed, demonstrating that, compared with traditional computing
framework, the proposed local distributed framework reduced 35.6%
average time consumption, and over 20 FPS average processing speed can
be achieve. The remote distributed framework has reduced the
computational power consumption of the on-board system by approximately
23.1% while ensuring the performance is not lower than the local
distributed framework. Finally, by discussing the two frameworks in
terms of stability and cost, we present the commercial viability for the
application of multi-arm harvesting robot.