Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

With the advent of agriculture 3.0 and 4.0, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the relevant environment

Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images

This work tackles scene understanding for outdoor robotic navigation, solely relying on images captured by an on-board camera. Conventional visual scene understanding interprets the environment based on specific descriptive categories. However, such a representation is not directly interpretable for decision-making and constrains robot operation to a specific domain. Thus, we propose to segment egocentric images directly in terms of how a robot can navigate in them, and tailor the learning problem to an autonomous navigation task. Building around an image segmentation network, we present a generic affordance consisting of 3 driveability levels which can broadly apply to both urban and off-road scenes. By encoding these levels with soft ordinal labels, we incorporate inter-class distances during learning which improves segmentation compared to standard "hard" one-hot labelling. In addition, we propose a navigation-oriented pixel-wise loss weighting method which assigns higher importance to safety-critical areas. We evaluate our approach on large-scale public image segmentation datasets ranging from sunny city streets to snowy forest trails. In a cross-dataset generalization experiment, we show that our affordance learning scheme can be applied across a diverse mix of datasets and improves driveability estimation in unseen environments compared to general-purpose, single-dataset segmentation.Comment: Accepted in Robotics and Automation Letters (RA-L 2022). Supplementary video available at https://youtu.be/q_XfjUDO39

Recognising, Representing and Mapping Natural Features in Unstructured Environments

This thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology

A Novel Real-Time Moving Target Tracking and Path Planning System for a Quadrotor UAV in Unknown Unstructured Outdoor Scenes

A quadrotor unmanned aerial vehicle (UAV) should have the ability to perform real-time target tracking and path planning simultaneously even when the target enters unstructured scenes, such as groves or forests. To accomplish this task, a novel system framework is designed and proposed to accomplish simultaneous moving target tracking and path planning by a quadrotor UAV with an onboard embedded computer, vision sensors, and a two-dimensional laser scanner. A support vector machine-based target screening algorithm is deployed to select the correct target from multiple candidates detected by single shot multibox detector. Furthermore, a new tracker named TLD-KCF is presented in this paper, in which a conditional scale adaptive algorithm is adopted to improve the tracking performance for a quadrotor UAV in cluttered outdoor environments. According to distance and position estimation for a moving target, our quadrotor UAV can acquire a control point to guide its fight. To reduce the computational burden, a fast path planning algorithm is proposed based on elliptical tangent model. A series of experiments are conducted on our quadrotor UAV platform DJI M100. Experimental video and comparison results among four kinds of target tracking algorithms are given to show the validity and practicality of the proposed approach

Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach

The main aim of this work is the development of a vision-based road detection system fast enough to cope with the difficult real-time constraints imposed by moving vehicle applications. The hardware platform, a special-purpose massively parallel system, has been chosen to minimize system production and operational costs. This paper presents a novel approach to expectation-driven low-level image segmentation, which can be mapped naturally onto mesh-connected massively parallel SIMD architectures capable of handling hierarchical data structures. The input image is assumed to contain a distorted version of a given template; a multiresolution stretching process is used to reshape the original template in accordance with the acquired image content, minimizing a potential function. The distorted template is the process output.Comment: See http://www.jair.org/ for any accompanying file

Development of bent-up triangular tab shear transfer (BTTST) enhancement in cold-formed steel (CFS)-concrete composite beams

Cold-formed steel (CFS) sections, have been recognised as an important contributor to environmentally responsible and sustainable structures in developed countries, and CFS framing is considered as a sustainable 'green' construction material for low rise residential and commercial buildings. However, there is still lacking of data and information on the behaviour and performance of CFS beam in composite construction. The use of CFS has been limited to structural roof trusses and a host of nonstructural applications. One of the limiting features of CFS is the thinness of its section (usually between 1.2 and 3.2 mm thick) that makes it susceptible to torsional, distortional, lateral-torsional, lateral-distortional and local buckling. Hence, a reasonable solution is resorting to a composite construction of structural CFS section and reinforced concrete deck slab, which minimises the distance from the neutral-axis to the top of the deck and reduces the compressive bending stress in the CFS sections. Also, by arranging two CFS channel sections back-to-back restores symmetricity and suppresses lateraltorsional and to a lesser extent, lateral-distortional buckling. The two-fold advantages promised by the system, promote the use of CFS sections in a wider range of structural applications. An efficient and innovative floor system of built-up CFS sections acting compositely with a concrete deck slab was developed to provide an alternative composite system for floors and roofs in buildings. The system, called Precast Cold-Formed SteelConcrete Composite System, is designed to rely on composite actions between the CFS sections and a reinforced concrete deck where shear forces between them are effectively transmitted via another innovative shear transfer enhancement mechanism called a bentup triangular tab shear transfer (BTTST). The study mainly comprises two major components, i.e. experimental and theoretical work. Experimental work involved smallscale and large-scale testing of laboratory tests. Sixty eight push-out test specimens and fifteen large-scale CFS-concrete composite beams specimens were tested in this program. In the small-scale test, a push-out test was carried out to determine the strength and behaviour of the shear transfer enhancement between the CFS and concrete. Four major parameters were studied, which include compressive strength of concrete, CFS strength, dimensions (size and angle) of BTTST and CFS thickness. The results from push-out test were used to develop an expression in order to predict the shear capacity of innovative shear transfer enhancement mechanism, BTTST in CFS-concrete composite beams. The value of shear capacity was used to calculate the theoretical moment capacity of CFSconcrete composite beams. The theoretical moment capacities were used to validate the large-scale test results. The large-scale test specimens were tested by using four-point load bending test. The results in push-out tests show that specimens employed with BTTST achieved higher shear capacities compared to those that rely only on a natural bond between cold-formed steel and concrete and specimens with Lakkavalli and Liu bent-up tab (LYLB). Load capacities for push-out test specimens with BTTST are ii relatively higher as compared to the equivalent control specimen, i.e. by 91% to 135%. When compared to LYLB specimens the increment is 12% to 16%. In addition, shear capacities of BTTST also increase with the increase in dimensions (size and angle) of BTTST, thickness of CFS and concrete compressive strength. An equation was developed to determine the shear capacity of BTTST and the value is in good agreement with the observed test values. The average absolute difference between the test values and predicted values was found to be 8.07%. The average arithmetic mean of the test/predicted ratio (n) of this equation is 0.9954. The standard deviation (a) and the coefficient of variation (CV) for the proposed equation were 0.09682 and 9.7%, respectively. The proposed equation is recommended for the design of BTTST in CFSconcrete composite beams. In large-scale testing, specimens employed with BTTST increased the strength capacities and reduced the deflection of the specimens. The moment capacities, MU ) e X p for all specimens are above Mu>theory and show good agreement with the calculated ratio (>1.00). It is also found that, strength capacities of CFS-concrete composite beams also increase with the increase in dimensions (size and angle) of BTTST, thickness of CFS and concrete compressive strength and a CFS-concrete composite beam are practically designed with partial shear connection for equal moment capacity by reducing number of BTTST. It is concluded that the proposed BTTST shear transfer enhancement in CFS-concrete composite beams has sufficient strength and is also feasible. Finally, a standard table of characteristic resistance, P t a b of BTTST in normal weight concrete, was also developed to simplify the design calculation of CFSconcrete composite beams

A Real-Time local path planning method based on SVM for UGV

Path planning is one of essentials of unmanned ground vehicle (UGV). For the case of poor lighting and weather, traditional vision based methods can not extract effective route boundaries to generate reasonable path stably in unstructured road. By ta king advantage of distance-sensing technology (e.g. 64-beam LiDAR), th is paper proposes an efficient real-time path planning approach. In this approach, given grid map fro m 64-bea m LiDAR, obstacles on both sides of the road are regarded as two classes fed to Support Vector Machine (SVM) to generate an initial safe path. During driving, a time weight based least square fitting is adopted to refine path fro m mu ltiple safe paths which will be described by quartic polynomial, providing stable driving route. Co mbined with UGV's state, controls points from the refined path are adopted to generate the final path through Bezier curve fitting. Experiments on real UGV under different road scenario are conducted, showing that the proposed method can obtain stable and reasonable path with promising performance