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PurposeTo construct a commercial agricultural manipulation for fruit picking and handling without human intervention.Design/methodology/approachDescribes a research activity involving a totally autonomous robot for fruit picking and handling crates.FindingsPicking time for the robotic fruit picker at 8.7 s per orange is longer than the evaluated cited time of 6 s per orange.Research limitations/implicationsThe final system, recently tested, has not yet achieved a level of productivity capable of replacing human pickers. Further mechanical modifications and more robust and adaptive algorithms are needed to achieve a stronger robot system.Practical implicationsExperimental results and new simulations look very promising.Originality/valueWill help to limit costs and guarantee a high degree of reliability.

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The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.

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