An autonomous navigation system with a trajectory prediction-based decision mechanism for rubber forest navigation.

The use of rubber-tapping robots capable of autonomous navigation in place of manual rubber-tapping is a growing trend, but the challenging multi-objective navigation task in forest environments impedes their autonomous operation. To tackle this issue, an autonomous navigation system with a trajectory prediction-based decision mechanism for rubber forest navigation is designed. This navigation decision mechanism is comprised of obtaining coordinates of target points (OCTP), selecting the next coordinate (SNC), generating the additional coordinates (GAC), and optimizing the planned paths (OPP).

An improved YOLOv8n-IRP model for natural rubber tree tapping surface detection and tapping key point positioning.

Aiming at the problem that lightweight algorithm models are difficult to accurately detect and locate tapping surfaces and tapping key points in complex rubber forest environments, this paper proposes an improved YOLOv8n-IRP model based on the YOLOv8n-Pose. First, the receptive field attention mechanism is introduced into the backbone network to enhance the feature extraction ability of the tapping surface. Secondly, the AFPN structure is used to reduce the loss and degradation of the low-level and high-level feature information.