Abstract

Abstract
Develop cost-effective ground robots for crop monitoring in steep slope vineyards is a complex challenge. The terrain presents harsh conditions for mobile robots and most of the time there is no one available to give support to the robots. So, a fully autonomous steep-slope robot requires a robust automatic recharging system. This work proposes a multilevel system that monitors a vineyard robot autonomy, to plan off-line the trajectory to the nearest recharging point and dock the robot on that recharging point considering visual tags. The proposed system called VineRecharge was developed to be deployed into a cost-effective robot with low computational power. Besides, this paper benchmarks several visual tags and detectors and integrates the best one into the VineRecharge system. © Springer International Publishing AG 2018.

Abstract
Localization and Mapping of autonomous robots in an harsh and unstable environment such as a steep slope vineyard is a challenging research topic. Dead Reckoning systems can fail due to the harsh conditions of the terrain, and the Global Position System can be affected by noise or even be unavailable. Agriculture is moving towards precision agriculture, with advanced monitoring systems and wireless sensor networks. These systems and wireless sensors are installed in the crop field and can be considered relevant landmarks for robot localization. In this paper the distance accuracy provided by bluetooth based sensors is deeply studied and characterized. It is considered a multi antenna receiver bluetooth system and obtained the transfer functions (from Received Signal Strength Indication (RSSI) to distance estimation) for each set of antenna and sensors. The performance of this technology is compared against Time-of-flight based technologies (Pozyx). The obtained results show that the agricultural wireless sensors can be used as redundant artificial landmarks for localization purposes. Besides, the RSSI characterization allowed to improve the previous results of our Beacon Mapping Procedure (BMP) required for accurate and reliable localization systems. © 2018 IEEE.

Abstract
The intensive use of agricultural machinery is promoting the soil compaction. The use of agricultural robots or autonomous machinery can intensify this problem, due its capacity to replicate the same trajectories. One of the possible strategies to minimize the effects of soil compaction is to control agricultural traffic instead of common random traffic. Since geo-referencing systems are present in almost all field operations it is possible to optimize trajectories to avoid to damage the crop and intensify the soil compaction. The controlled agricultural traffic on farms will not only increase production capacity, the incomes as well as the quality of the soil. In this work a novel approach based on A-star algorithm is proposed to reduce soil compaction in steep slope vineyards. © 2018 IEEE.

Abstract
Developing ground robots for crop monitoring and harvesting in steep slope vineyards is a complex challenge due to two main reasons: harsh condition of the terrain and unstable localization accuracy obtained with Global Navigation Satellite System (GNSS). In this context, a reliable localization system requires an accurate detector for high density of natural/artificial features. In previous works, we presented a novel visual detector for Vineyards Trunks and Masts (ViTruDe) with high levels of detection accuracy. However, its implementation on the most common processing units -central processing units (CPU), using a standard programming language (C/C++), is unable to reach the processing efficiency requirements for real time operation. In this work, we explored parallelization capabilities of processing units, such as graphics processing units (GPU), in order to accelerate the processing time of ViTruDe. This work gives a general perspective on how to parallelize a generic problem in a GPU based solution, while exploring its efficiency when applied to the problem at hands. The ViTruDe detector for GPU was developed considering the constraints of a cost-effective robot to carry-out crop monitoring tasks in steep slope vineyard environments. We compared the proposed ViTruDe implementation on GPU using Compute Unified Compute Unified Device Architecture(CUDA) and CPU, and the achieved solution is over eighty times faster than its CPU counterpart. The training and test data are made public for future research work. This approach is a contribution for an accurate and reliable localization system that is GNSS-free.

Abstract
Robotic platforms are being developed for precision agriculture, to execute repetitive and long term tasks. Autonomous monitoring, pruning, spraying and harvesting are some of these agricultural tasks, which requires an advanced path planning system aware of maximum robot capabilities (mobile platform and arms), terrain slopes and plant/fruits position. The state of the art path planning systems have two limitations: are not optimized for large regions and the path planning is not aware of agricultural tasks requirements. This work presents two solutions to overcome these limitations. It considers the VGR2TO (Vineyard Grid Map to Topological) approach to extract from a 2D grid map a topological map, to reduce the total amount of memory needed by the path planning algorithm and to reduce path search space. Besides, introduces an extension to the chosen algorithm, the Astar algorithm, to ensure a safe path and a maximum distance from the vine trees to enable robotic operations on the tree and its fruits.