Autonomous control of underground mining vehicles using reactive navigation

Describes how many of the navigation techniques developed by the robotics research community over the last decade may be applied to a class of underground mining vehicles (LHDs and haul trucks). We review the current state-of-the-art in this area and conclude that there are essentially two basic methods of navigation applicable. We describe an implementation of a reactive navigation system on a 30 tonne LHD which has achieved full-speed operation at a production mine

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Radar-on-Lidar: metric radar localization on prior lidar maps

Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes

Adaptive Multi-sensor Perception for Driving Automation in Outdoor Contexts

In this research, adaptive perception for driving automation is discussed so as to enable a vehicle to automatically detect driveable areas and obstacles in the scene. It is especially designed for outdoor contexts where conventional perception systems that rely on a priori knowledge of the terrain's geometric properties, appearance properties, or both, is prone to fail, due to the variability in the terrain properties and environmental conditions. In contrast, the proposed framework uses a self-learning approach to build a model of the ground class that is continuously adjusted online to reflect the latest ground appearance. The system also features high flexibility, as it can work using a single sensor modality or a multi-sensor combination. In the context of this research, different embodiments have been demonstrated using range data coming from either a radar or a stereo camera, and adopting self-supervised strategies where monocular vision is automatically trained by radar or stereo vision. A comprehensive set of experimental results, obtained with different ground vehicles operating in the field, are presented to validate and assess the performance of the system

ESTIMATION OF VEHICLE TRAILER ROTATION USING CALIBRATED RADARS WITH MULTIPLE VIEWPOINTS

Multiple sensors are increasingly being deployed on systems for perception applications. In particular, vehicles are becoming equipped with a suite of sensors for advanced driver assistance features and autonomous driving. This dissertation considers automotive radar sensors mounted at the rear of a vehicle with the main objective of using their point cloud detections to estimate the rotation of a trailer which is attached to the vehicle\u27s hitch ball. A simulation-based study of the problem is presented first. Thereafter, the problem is considered with respect to experimental radar data collected in both indoor and outdoor environments; the environmental difference is in the roughness of the ground surfaces. The apparatus used for the data collection includes two radars, which provide point detections in two dimensions -- range and azimuth, installed in the tail light fixtures of a truck. The estimation algorithm, based on the experimental data, includes the fusion of radar detections onto a coordinate system centered at the hitch ball position, a rotational point set registration algorithm, constrained orthogonal Procrustes optimization, and state estimation with the Kalman filter to obtain smooth estimates of the trailer rotation angle. In one implementation of the estimation algorithm, the dimensions of the radar geometry, which are required in its radar fusion procedure, are obtained by direct measurement. In another implementation, the calibration of the radar geometry is considered; two extrinsic calibration methods which estimate the dimensions of the geometry using the radar detections are provided. The trailer angle estimation algorithm is then used with respect to the calibration parameters. The results presented show that the trailer angle estimates obtained with respect to a direct measurement of the radar geometry parameters are comparable with those obtained with respect to the calibration parameters and that the algorithms presented for trailer angle estimation and extrinsic radar calibration are feasible for deployment. It is also shown that the trailer angle estimation algorithm has improved performance with the indoor dataset than with the outdoor dataset. The challenges observed with the outdoor dataset are presented and recommended for future research

MAR-CPS: Measurable Augmented Reality for Prototyping Cyber-Physical Systems

Cyber-Physical Systems (CPSs) refer to engineering platforms that rely on the inte- gration of physical systems with control, computation, and communication technologies. Autonomous vehicles are instances of CPSs that are rapidly growing with applications in many domains. Due to the integration of physical systems with computational sens- ing, planning, and learning in CPSs, hardware-in-the-loop experiments are an essential step for transitioning from simulations to real-world experiments. This paper proposes an architecture for rapid prototyping of CPSs that has been developed in the Aerospace Controls Laboratory at the Massachusetts Institute of Technology. This system, referred to as MAR-CPS (Measurable Augmented Reality for Prototyping Cyber-Physical Systems), includes physical vehicles and sensors, a motion capture technology, a projection system, and a communication network. The role of the projection system is to augment a physical laboratory space with 1) autonomous vehicles' beliefs and 2) a simulated mission environ- ment, which in turn will be measured by physical sensors on the vehicles. The main focus of this method is on rapid design of planning, perception, and learning algorithms for au- tonomous single-agent or multi-agent systems. Moreover, the proposed architecture allows researchers to project a simulated counterpart of outdoor environments in a controlled, indoor space, which can be crucial when testing in outdoor environments is disfavored due to safety, regulatory, or monetary concerns. We discuss the issues related to the design and implementation of MAR-CPS and demonstrate its real-time behavior in a variety of problems in autonomy, such as motion planning, multi-robot coordination, and learning spatio-temporal fields.Boeing Compan