This paper presents an investigation of a novel development of a multifunctional mobile platform for agriculture applications. This is achieved through a reinvention process of a mechatronic design by spinning off space robotic technologies in terrestrial applications in the AgriRover project. The AgriRover prototype is the first of its kind in exploiting and applying space robotic technologies in precision farming. To optimize energy consumption of the mobile platform, a new dynamic total cost of transport algorithm is proposed and validated. An autonomous navigation system has been developed to enable the AgriRover to operate safely in unstructured farming environments. An object recognition algorithm specific to agriculture-has been investigated and implemented. A novel soil sample collecting mechanism has been designed and prototyped for on-board and in-situ soil quality measurement. The design of the whole system has benefited from the use of a mechatronic design process known as the Tiv model through which a planetary exploration rover is reinvented into the AgriRover for agricultural applications. The Agri-Rover system has gone through three sets of field trials in the UK and some of these results are reported.
With the increasing demands for unmanned aerial vehicle (UAV) based autonomous inspections in the oil and gas industry, one of the challenging issues for 3D UAV positioning has emerged due to the satellite signal blocking. Considering the existing characteristics of the ultrasonic based technique, such as the low cost, extremely lightweight and high positioning accuracy, it can be promising as the potential solution. Nevertheless, the low position update rate and vulnerable positioning performance to the changing environment still limit its applications on UAV. Therefore, in this article, an ultrasonic and inertial measurement unit (IMU) based localisation algorithm and low cost UAV autonomous inspection system are presented. With the incorporation of the IMU, the position update rate, accuracy and stability of the algorithm can all be significantly improved. This is done by the adaptively estimated noise covariance matrices through the proposed adaptive extended Kalman filter (AEKF) algorithm and the added weighting factors. Followed by, an additional virtual observation process is presented to overcome the unavailability of the observation information for further performance improvement. Finally, extensive numerical results and field tests demonstrate that the proposed algorithm and system can achieve the high update rate, reliable, accurate and precision UAV positioning in oil and gas pressure vessels and are feasible for the UAV autonomous inspection in these environments.
Battery life is critical for battery-powered agricultural rovers, so techniques such as optimized moving path planning are of great significance in this field. Finding an optimized path other than straight-line path could save energy and prolong the battery life. Compared with traditional straight-line path planning, an energy-optimized path planning is realized based on artificial potential field algorithm. In simulation studies, most of the uphill is avoided and at least 10.15 % of energy is saved with the optimized path planning. We believe this energy optimization path planning algorithm is a feasible solution to extend the battery life for field operated agricultural rover.
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