# Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

arXiv: https://arxiv.org/abs/2005.12815 · HTML: https://mlsanity.com/paper/2005.12815/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 8 of 8)

- [Learned Visual Navigation for Under-Canopy Agricultural Robots](https://mlsanity.com/paper/2107.02792/) (2021)
- [Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14155/) (2022)
- [Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows](https://mlsanity.com/paper/2107.00700/) (2021)
- [Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection](https://mlsanity.com/paper/2304.04333/) (2023)
- [Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering](https://mlsanity.com/paper/2206.11623/) (2022)
- [A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops](https://mlsanity.com/paper/2112.03816/) (2021)
- [Low Cost, High Efficiency: LiDAR Place Recognition in Vineyards with Matryoshka Representation Learning](https://mlsanity.com/paper/2601.18714/) (2026)
- [Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey](https://mlsanity.com/paper/2601.22198/) (2026)

## Abstract (arXiv)

With the advent of agriculture 3.0 and 4.0, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the relevant environment.