Reinforcement and Curriculum Learning for Off-Road Navigation of an UGV with a 3D LiDAR

This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor in off-road environments. For training, both the robotic simulator Gazebo and the Curriculum Learning paradigm are applied. Furthermore, an Actor–Critic Neural Network (NN) scheme is chosen with a suitable state and a custom reward function. To employ the 3D LiDAR data as part of the input state of the NNs, a virtual two-dimensional (2D) traversability scanner is developed. The resulting Actor NN has been successfully tested in both real and simulated experiments and favorably compared with a previous reactive navigation approach on the same UGV.Partial funding for open access charge: Universidad de Málag

Watch Your Step! Terrain Traversability for Robot Control

Watch your step! Or perhaps, watch your wheels. Whatever the robot is, if it puts its feet, tracks, or wheels in the wrong place, it might get hurt; and as robots are quickly going from structured and completely known environments towards uncertain and unknown terrain, the surface assessment becomes an essential requirement. As a result, future mobile robots cannot neglect the evaluation of terrain’s structure, according to their driving capabilities. With the objective of filling this gap, the focus of this study was laid on terrain analysis methods, which can be used for robot control with particular reference to autonomous vehicles and mobile robots. Giving an overview of theory related to this topic, the investigation not only covers hardware, such as visual sensors or laser scanners, but also space descriptions, such as digital elevation models and point descriptors, introducing new aspects and characterization of terrain assessment. During the discussion, a wide number of examples and methodologies are exposed according to different tools and sensors, including the description of a recent method of terrain assessment using normal vectors analysis. Indeed, normal vectors has demonstrated great potentialities in the field of terrain irregularity assessment in both on‐road and off‐road environments

3D exploration and navigation with optimal-RRT planners for ground robots in indoor incidents

Navigation and exploration in 3D environments is still a challenging task for autonomous robots that move on the ground. Robots for Search and Rescue missions must deal with unstructured and very complex scenarios. This paper presents a path planning system for navigation and exploration of ground robots in such situations. We use (unordered) point clouds as the main sensory input without building any explicit representation of the environment from them. These 3D points are employed as space samples by an Optimal-RRTplanner (RRT ∗ ) to compute safe and efficient paths. The use of an objective function for path construction and the natural exploratory behaviour of the RRT ∗ planner make it appropriate for the tasks. The approach is evaluated in different simulations showing the viability of autonomous navigation and exploration in complex 3D scenarios

Unevenness Point Descriptor for Terrain Analysis in Mobile Robot Applications

In recent years, the use of imaging sensors that produce a three-dimensional representation of the environment has become an efficient solution to increase the degree of perception of autonomous mobile robots. Accurate and dense 3D point clouds can be generated from traditional stereo systems and laser scanners or from the new generation of RGB-D cameras, representing a versatile, reliable and cost-effective solution that is rapidly gaining interest within the robotics community. For autonomous mobile robots, it is critical to assess the traversability of the surrounding environment, especially when driving across natural terrain. In this paper, a novel approach to detect traversable and non-traversable regions of the environment from a depth image is presented that could enhance mobility and safety through integration with localization, control and planning methods. The proposed algorithm is based on the analysis of the normal vector of a surface obtained through Principal Component Analysis and it leads to the definition of a novel, so defined, Unevenness Point Descriptor. Experimental results, obtained with vehicles operating in indoor and outdoor environments, are presented to validate this approach

Vision based obstacle detection for all-terrain robots

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresThis dissertation presents a solution to the problem of obstacle detection in all-terrain environments,with particular interest for mobile robots equipped with a stereo vision sensor. Despite the advantages of vision, over other kind of sensors, such as low cost, light weight and reduced energetic footprint, its usage still presents a series of challenges. These include the difficulty in dealing with the considerable amount of generated data, and the robustness required to manage high levels of noise. Such problems can be diminished by making hard assumptions, like considering that the terrain in front of the robot is planar. Although computation can be considerably saved, such simplifications are not necessarily acceptable in more complex environments, where the terrain may be considerably uneven. This dissertation proposes to extend a well known obstacle detector that relaxes the aforementioned planar terrain assumption, thus rendering it more adequate for unstructured environments. The proposed extensions involve: (1) the introduction of a visual saliency mechanism to focus the detection in regions most likely to contain obstacles; (2) voting filters to diminish sensibility to noise; and (3) the fusion of the detector with a complementary method to create a hybrid solution, and thus, more robust. Experimental results obtained with demanding all-terrain images show that, with the proposed extensions, an increment in terms of robustness and computational efficiency over the original algorithm is observe