Vision-based operations of a large industrial vehicle : autonomous hot metal carrier

Hot metal carriers (HMCs) are large forklift-type vehicles used to move molten metal in aluminum smelters. This paper reports on field experiments that demonstrate that HMCs can operate autonomously and in particular can use vision as a primary sensor to locate the load of aluminum. We present our complete system but focus on the vision system elements and also detail experiments demonstrating reliable operation of the materials handling task. Two key experiments are described, lasting 2 and 5 h, in which the HMC traveled 15 km in total and handled the load 80 times.

Autonomous hot metal carrier: Navigation and manipulation with a 20 tonne industrial vehicle

This paper reports work on the automation of a hot metal carrier, which is a 20 tonne forklift-type vehicle used to move molten metal in aluminium smelters. To achieve efficient vehicle operation, issues of autonomous navigation and materials handling must be addressed. We present our complete system and experiments demonstrating reliable operation. One of the most significant experiments was five-hours of continuous operation where the vehicle travelled over 8 km and conducted 60 load handling operations. Finally, an experiment where the vehicle and autonomous operation were supervised from the other side of the world via a satellite phone network are described.

Autonomous hot metal carrier

This paper reports work involved with the automation of a Hot Metal Carrier — a 20 tonne forklift-type vehicle used to move molten metal in aluminium smelters. To achieve efficient vehicle operation, issues of autonomous navigation and materials handling must be addressed. We present our complete system and experiments demontrating reliable operation. One of the most significant experiments was five-hours of continuous operation where the vehicle travelled over 8 km and conducted 60 load handling operations. We also describe an experiment where the vehicle and autonomous operation were supervised from the other side of the world via a satellite phone network.

Robust sensing for a 3,500 tonne field robot

The mining industry is highly suitable for the application of robotics and automation technology since the work is arduous, dangerous and often repetitive. This paper discusses a robust sensing system developed to find and trade the position of the hoist ropes of a dragline. Draglines are large `walking cranes' used in open-pit coal mining to remove the material covering the coal seam. The rope sensing system developed uses two time-of-flight laser scanners. The finding algorithm uses a novel data association and tracking strategy based on pairing rope data.

Robot control of a 3,500 tonne mining machine

The mining industry is highly suitable for the application of robotics and automation technology since the work is arduous, dangerous and often repetitive. This paper describes the development of an automation system for a physically large and complex field robotic system - a 3,500 tonne mining machine (a dragline). The major components of the system are discussed with a particular emphasis on the machine/operator interface. A very important aspect of this system is that it must work cooperatively with a human operator, seamlessly passing the control back and forth in order to achieve the main aim - increased productivity.

Vision-based control for mining automation

The mining industry is highly suitable for the application of robotics and automation technology since the work is both arduous and dangerous. Visual servoing is a means of integrating noncontact visual sensing with machine control to augment or replace operator based control. This article describes two of our current mining automation projects in order to demonstrate some, perhaps unusual, applications of visual servoing, and also to illustrate some very real problems with robust computer vision

Experiments and experiences with dependability for a large autonomous industrial vehicle

This paper describes the experiences gained performing multiple experiments while developing a large autonomous industrial vehicle. Hot Metal Carriers (HMCs) are large forklift-type vehicles used in the light metals industry to move molten or hot metal around a smelter. Autonomous vehicles of this type must be dependable as they are large and potentially hazardous to infrastructure and people. This paper will talk about four aspects of dependability, that of safety, reliability, availability and security and how they have been addressed on our experimental autonomous HMC.

From vision to actions: Towards adaptive and autonomous humanoid robots

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.