While different control approaches have been developed for smooth and safe navigation, they are limited by the needs for model-based assumptions, true training target/reward function, and/or large sample data. To overcome these limitations, this study proposes a model-free neural control architecture with a generic plug-and-play online Multiple Proactive Behavior Learning (MPL) module. The MPL adapts robot neural control policy in an online unsupervised manner with small sample data by correlating its sensory inputs to a local planner command. As a result, it allows a mobile robot to autonomously and quickly learn and balance various proactive behaviors related to smooth motion and collision avoidance. It also compensates for the limited planning update rates and the planning model mismatch of an arbitrary local motion planner. Compared with existing control approaches without the MPL, our control architecture with the MPL leads to (1) a 10% improvement in the smoothness of robot motion and 30% fewer collisions in a narrow static environment, and (2) trading motion smoothness for up to 70% fewer collisions in an unknown dynamic environment. Taken together, this study also demonstrates how to apply model-free neural control with unsupervised learning to existing model-based control (e.g., local motion planner) for efficient proactive behavior learning and control of mobile robots.

We explore the use of uncertainty estimation in the maritime domain, showing the efficacy on toy datasets (CIFAR10) and proving it on an in-house dataset, SHIPS. We present a method joining the intra-class uncertainty achieved using Monte Carlo Dropout, with recent discoveries in the field of outlier detection, to gain more holistic uncertainty measures. We explore the relationship between the introduced uncertainty measures and examine how well they work on CIFAR10 and in a real-life setting. Our work improves the FPR95 by 8% compared to the current highest-performing work when the models are trained without out-of-distribution data. We increase the performance by 77% compared to a vanilla implementation of the Wide ResNet. We release the SHIPS dataset and show the effectiveness of our method by improving the FPR95 by 44.2 % with respect to the baseline. Our approach is model agnostic, easy to implement, and often does not require model retraining.

Marine vessels are subject to high wear and tear due to the conditions they operate in. To reduce risk of failure during operation, vessels are inspected periodically every five years. These inspections are prone to high subjectiveness that makes them hard to reproduce for the shipping owners. The purpose of this paper is to present a regressor to a Faster R-CNN network that can help alleviate some of the subjective assessment currently performed by human surveyors by estimating the severity of a corroded area, autonomously using drones. A feature pyramid backbone is shared between the Faster R-CNN and the added regression head. The goal of the regressor is to introduce a more objective assessment of the vessel that gives a consistent output for a consistent input. The system is evaluated on a real dataset, acquired in ballast tanks and the experimental results indicate that our deep learning approach can be used to detect and quantify corroded areas during the inspection process of marine vessels.

Autonomous robotic weeding in grasslands requires robust weed segmentation. Deep learning models can provide solutions to this problem, but they need to be trained on large amounts of images, which in the case of grasslands are notoriously difficult to obtain and manually annotate. In this work we introduce Few-leaf Learning, a concept that facilitates the training of accurate weed segmentation models and can lead to easier generation of weed segmentation datasets with minimal human annotation effort. Our approach builds upon the fact that each plant species within the same field has relatively uniform visual characteristics due to similar environmental influences. Thus, we can train a field-and-day-specific weed segmentation model on synthetic training data stemming from just a handful of annotated weed leaves. We demonstrate the efficacy of our approach for different fields and for two common grassland weeds: Rumex obtusifolius (broad-leaved dock) and Cirsium vulgare (spear thistle). Our code is publicly available at https://github.com/RGring/WeedAnnotator.

Inverse dynamics models are applied to a plethora of robot control tasks such as computed-torque control, which are essential for trajectory execution. The analytical derivation of such dynamics models for robotic manipulators can be challenging and depends on their physical characteristics. This paper proposes a machine learning approach for modeling inverse dynamics and provides information about its implementation on a physical robotic system. The proposed algorithm can perform online multi-target learning, thus allowing efficient implementations on real robots. Our approach has been tested both offline, on datasets captured from three different robotic systems and online, on a physical system. The proposed algorithm exhibits state-of-the-art performance in terms of generalization ability and convergence. Furthermore, it has been implemented within ROS for controlling a Baxter robot. Evaluation results show that its performance is comparable to the built-in inverse dynamics model of the robot.

Planetary rover exploration missions require accurate and computationally efficient robot localization in order to perform complex and cooperative tasks. The global localization on planetary environments can be competently addressed by incorporating orbital and ground rover imagery. An indicative approach could include (1) the extraction of regions of interest (ROIs) in orbital images, (2) the extraction of ROIs in rover images, (3) the ROI matching, and (4) the localization. In order to perform adequately in ROI matching, a model should be able to detect common ROIs. The work in hand tackles the problem of extracting such regions of interest that are observable on both orbital and rover images. The dedicated model that was designed and implemented contains a detection and a classification part. The detection of the ROIs is based on both their texture and their geometrical properties. Classification was performed on the result of the detection in order to annotate the ROIs and discard any outliers caused by false detection. The results prove that the model is able to detect commonly observable regions and, therefore, is considered to be an adequate preprocessing step in the context of a global rover localization system.

Space exploratory rovers do well in autonomous or composite semi-autonomous exploration of extraterrestrial surfaces, yet their localization relies on the particular spot they had landed, rather than being universal, i.e. based on the absolute coordinate system of the explored planet. The idea underlaying the work presented in this paper is the transition from the relative to absolute localization by inspecting common Regions of Interest (ROIs) on both rover and orbital imagery. In order to achieve that we propose a method comprising an offline and an onboard procedure. Particularly, prior to the mission the orbital images of the intended landing area are examined to extract ROIs and to construct an offline Global Network (GN). The onboard procedure is based on the rover's self localization which is performed via an inertial aided visual odometry (VO). During its roaming the rover extracts ROIs from the ground and forms a Local Network (LN). The last is iteratively matched with the GN by a specifically designed matching procedure based on Data-Aligned Rigidity-Constrained Exhaustive Search (DARCES). The proposed method is tested on real representative data collected during the ESA Seeker activity. The results indicate that the self-localization of a planetary rover in an absolute frame of reference is feasible, provided that the area includes few discriminative ROIs.

The congregation of crowd undoubtedly constitutes an important risk factor, which may endanger the safety of the gathered people. The solution reported against this significant threat to citizens safety is to consider careful planning and measures. Thereupon, in this paper, we address the crowd evacuation problem by suggesting an innovative technological solution, namely, the use of mobile robot agents. The contribution of the proposed evacuation system is twofold: (i) it proposes an accurate Cellular Automaton simulation model capable of assessing the human behavior during emergency situations and (ii) it takes advantage of the simulation output to provide sufficient information to the mobile robotic guide, which in turn approaches and redirects a group of people towards a less congestive exit at a time. A custom-made mobile robotic platform was accordingly designed and developed. Last, the performance of the proposed robot guided evacuation model has been examined in real-world scenarios exhibiting significant performance improvement during the crucial first response time window.