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We present a platform to foster research in active scene understanding, consisting of high-fidelity simulated environments and a simple yet powerful API that controls a mobile robot in simulation and reality. In contrast to static, pre-recorded datasets that focus on the perception aspect of scene understanding, agency is a top priority in our work. We provide three levels of robot agency, allowing users to control a robot at varying levels of difficulty and realism. While the most basic level provides pre-defined trajectories and ground-truth localisation, the more realistic levels allow us to evaluate integrated behaviours comprising perception, navigation, exploration and SLAM. In contrast to existing simulation environments, we focus on robust scene understanding research using our environment interface (BenchBot) that provides a simple API for seamless transition between the simulated environments and real robotic platforms. We believe this scaffolded design is an effective approach to bridge the gap between classical static datasets without any agency and the unique challenges of robotic evaluation in reality. Our BenchBot Environments for Active Robotics (BEAR) consist of 25 indoor environments under day and night lighting conditions, a total of 1443 objects to be identified and mapped, and ground-truth 3D bounding boxes for use in evaluation. BEAR website: https://qcr.github.io/dataset/benchbot-bear-data/. [ABSTRACT FROM AUTHOR]

The rapid evolution of herbicide-resistant weed species has revitalized research in nonchemical methods for weed destruction. Robots with vision-based capabilities for online weed detection and classification are a key enabling factor for the specialized treatment of individual weed species. This paper describes the design, development, and testing of a modular robotic platform with a heterogeneous weeding array for agriculture. Starting from requirements derived from farmer insights, technical specifications are put forward. A design of a robotic platform is conducted based on the required technical specifications, and a prototype is manufactured and tested. The second part of the paper focuses on the weeding mechanism attached to the robotic platform. This includes aspects of vision for weed detection and classification, as well as the design of a weeding array that combines chemical and mechanical methods for weed destruction. Field trials of the weed detection and classification system show an accuracy of 92.3% across a range of weed species, while the heterogeneous weed management system is able to selectively apply a mechanical or chemical control method based on the species of weed. Together, the robotic platform and weeding array demonstrate the potential for robotic plant-species-specific weed management enabled by the vision-based online detection and classification algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit. [ABSTRACT FROM AUTHOR]