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This paper deals with the problem of obstacle avoidance during an automated preflight inspection. During this mission, safety appears to be a key issue, as numerous obstacles are lying in a close neighborhood of the aircraft. They are very different in terms of size, shape and mobility and are often unforeseen. To cope with this highly evolutive environment, it is necessary to design a method sufficiently generic to deal with the obstacles variety and efficient enough to guarantee non collision and avoid classical problems such as local minima, singularities, etc. In this paper, we have proposed a new sensorbased control strategy fulfilling these two requirements. It consists in defining and following an adaptative spiral around the encountered obstacles while performing preflight inspection. It relies on two main elements: (i) the definition of a spiral whose parameters are continuously updated depending on the robot motion and on the environment; (ii) the coupling of two sensorbased controllers allowing to track the spiral while avoiding singularities. Experimental results conducted on our robot show the relevance and the efficiency of the proposed control strategy.

This paper deals with multi-arms fruits picking in orchards. More specifically, the goal is to control the arms to approach the fruits position. To achieve this task a VPC strategy has been designed to take into account the dynamic of the environment as well as the various constraints inherent to the mechanical system, visual servoing manipulation and shared workspace. Our solution has been evaluated in simulation using on PR2 arms model. Different models of visual features prediction have been tested and the entire VPC strategy has been run on various cases. The obtained results show the interest and the efficiency of this strategy to perform a fruit picking task.

This paper deals with autonomous farming and with the autonomous navigation of an agricultural robot in orchards. These latter are typical semi-structured environments where the dense canopy prevents from using GPS signal and embedded sensors are often preferred to localize the vehicle. To move safely in such environments, it is necessary to provide the robot the ability of detecting and localizing trees. This paper focuses on this problem. It presents a low cost but efficient vision-based system allowing to detect accurately, quickly and robustly the trees. It is made of four stereo cameras which provide a point cloud characterizing the environment. The key idea is to find the tree trunks by detecting their shadows which are materialized by concavities in the obtained point cloud. In this way, branches and leaves are not taken into account, improving the detection robustness and therefore the navigation strategy. The method has been implemented using ROS and validated using data sequences taken in several different orchards. The obtained results definitely validate the approach and its performances show that the processing time (around 1ms) is sufficiently short for the data to be used at the control level. A comparison with other approaches from the literature is also provided.

This paper provides a preliminary analysis of an autonomous uncooperative collision avoidance strategy for unmanned aircraft using image-based visual control. Assuming target detection, the approach consists of three parts. First, a novel decision strategy is used to determine appropriate reference image features to track for safe avoidance. This is achieved by considering the current rules of the air (regulations), the properties of spiral motion and the expected visual tracking errors. Second, a spherical visual predictive control (VPC) scheme is used to guide the aircraft along a safe spiral-like trajectory about the object. Lastly, a stopping decision based on thresholding a cost function is used to determine when to stop the avoidance behaviour. The approach does not require estimation of range or time to collision, and instead relies on tuning two mutually exclusive decision thresholds to ensure satisfactory performance.