Navigation is an indispensable component of ground and aerial mobile robots. Although there is a plethora of path planning algorithms, most of them generate paths that are not smooth and have angular turns. In many cases, it is not feasible for the robots to execute these sharp turns, and a smooth trajectory is desired. We present ‘ITC: Infused Tangential Curves’ which can generate smooth trajectories for mobile robots. The main characteristics of the proposed ITC algorithm are: (1) The curves are tangential to the path, thus maintaining G1 continuity, (2) The curves are infused in the original global path to smooth out the turns, (3) The straight segments of the global path are kept straight and only the sharp turns are smoothed, (4) Safety is embedded in the ITC trajectories and robots are guaranteed to maintain a safe distance from the obstacles, (5) The curvature of ITC curves can easily be controlled and smooth trajectories can be generated in real-time, (6) The ITC algorithm smooths the global path on a part-by-part basis thus local smoothing at one point does not affect the global path. We compare the proposed ITC algorithm with traditional interpolation based trajectory smoothing algorithms. Results show that, in case of mobile navigation in narrow corridors, ITC paths maintain a safe distance from both walls, and are easy to generate in real-time. We test the algorithm in complex scenarios to generate curves of different curvatures, while maintaining different safety thresholds from obstacles in vicinity. We mathematically discuss smooth trajectory generation for both 2D navigation of ground robots, and 3D navigation of aerial robots. We also test the algorithm in real environments with actual robots in a complex scenario of multi-robot collision avoidance. Results show that the ITC algorithm can be generated quickly and is suitable for real-world scenarios of collision avoidance in narrow corridors.

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

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Many tasks involved in viticulture are labor intensive. Farmers frequently monitor the vineyard to check grape conditions, damage due to infections from pests and insects, grape growth, and to estimate optimal harvest time. Such monitoring is often done manually by the farmers. Manual monitoring of large vineyards is time and labor consuming process. To this end, robots have a big potential to increase productivity in farms by automating various tasks. We propose a low-cost semantic monitoring system for vineyards using autonomous robots. The system uses inexpensive cameras, processing boards, and sensors to remotely provide timely information to the farmers on their computer and smart phone. Unlike traditional systems, the proposed system logs data ‘semantically’, which enables pin-pointed monitoring of vineyards. In other words, the farmers can monitor only specific areas of the vineyard as desired. The proposed algorithm is robust for occlusions, and intelligently logs image data based on the movement of the robot. The proposed system was tested in actual vineyards with real robots. Due to its compactness and portability, the proposed system can be used as an extension in conjunction with already existing autonomous robot systems used in vineyards. The results show that pin-pointed remote monitoring of desired areas of the vineyard is a very useful and inexpensive tool for the farmers to save a lot of time and labor.

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