4.Conclusions
In this paper, a highly robust autonomous navigation system is proposed
for dynamic greenhouse agricultural environments. Initially, a hardware
system incorporating multiple sensors is designed and implemented
tailored for the greenhouse environment. Then, in order to enhance
environmental perception and reduce complex environmental disturbances,
a relocalization module is devised and integrated into the SLAM system.
Lastly, the local planner of OpenPlanner is enhanced, taking into
account additional metrics and introducing a hysteresis strategy for
improved performance.The proposed navigation system has been tested in
various complex greenhouse environments, demonstrating robust
performance throughout all evaluations. As it significantly reduces the
absolute pose error in localization, along with ensuring safer
trajectories in the path planning process. Consequently, this system
exhibits exceptional performance and holds promising potential in the
context of greenhouse agricultural scenarios. However, the inclusion of
more modules also leads to increased computational demand. Future work
may entail the design of additional sensors within the robot hardware
system to further improve performance. Moreover, reducing the
computational load of the path planning algorithm to enable deployment
on more lightweight computational units will be a key focus moving
forward.