Towards an Infrastructureless Guidance System. A Proposed Guidance System
for Autonomous Underground Vehicles. CANADIAN INSTITITE OF MINING ...
Towards an Infrastructureless Guidance System A Proposed Guidance System for Autonomous Underground Vehicles CANADIAN INSTITITE OF MINING JOURNAL, JANUARY 2001
Leif Bloomquist – Research and Development Engineer Automated Mining Systems Inc., Aurora, Canada
[email protected] Eric Hinton – Mining Automation Program Supervisor Inco Limited, Sudbury, Canada
[email protected]
Mr. Bloomquist graduated from the University of Waterloo with a Bachelor of Applied Science in Systems Design Engineering in 1997. He worked at Inco's Mines Research group as an undergraduate. Upon graduation, Leif joined Automated Mining Systems Inc. where he works as a Research and Development Engineer. He is a member of the Professional Engineers of Ontario. Mr. Hinton received diplomas of Mining Technology from the Haileybury School of Mines in 1984 and 1985. He graduated from Queen’s University at Kingston with a Bachelor of Science (Mining Engineering) in 1988. He joined Inco’s Mines Technical Services group and worked as a planner in the engineering office until moving into Mines Research. Eric has worked on the Mining Automation Program for Inco Limited and finished a Masters of Applied Science (Mineral Resource Engineering) at Laurentian University in Sudbury in 2000. He is a member of the Professional Engineers of Ontario, the Canadian Institute of Mining and Metallurgy and President of the Haileybury School of Mines Alumni Association.
1. Introduction This paper describes a guidance system for autonomous underground mining vehicles, which does not require any infrastructure or inertial positioning systems to aid the guidance process.
2. Background Inco Limited mines approximately ten million tons per annum through 12 operating mines in the Sudbury, Ontario, Canada area. (Figure 1) With challenges from operating in a cyclical marketplace as well as new technologies making alternative nickel ores more competitive to refine, the Mines Research department embarked on a strategy that pointed toward automation as a tool to help the company become even more economical in mining.
The mining industry has been looking for ways and means to mine orebodies more and more inexpensively. Automation and the lessons learned from manufacturing had a great influence in the direction of research at Inco Limited over the past ten years. Guidance systems have evolved over the years but the majority of commercially available systems rely on some form of infrastructure. This, in part, has caused some resistance from operators to install and maintain automated or semiautomated systems for guidance of underground machinery. Internal industrial studies cited travel time to and from the workplace as one of the major time sinks in the shift of underground miners. Depending on the mine, it may be as much as two hours moving in and out of an area. Some work areas have accommodated this by having “hot changes” at the work place. However, at some point in time there are two people tasked with the same job and on site at the same time to make this overlap occur.
Teleoperation of underground hard rock equipment eliminated this overlap and also provided a comfortable environment in which to work. By taking advantage of the communications network installed, the miners proved that they could operate three machines with relative ease. This was accomplished by a vision-based, “painted line”, autonomous guidance system termed the light rope system. With a few years’ experience with the system, some issues have arisen, including the cost of the rope itself. (Figure 2) The vehicle’s route is limited to the paths defined by the light rope, and a system of switching power to the light rope to select different routes is required. The rope itself is subject to damage from nearby blasts. Also, the system offers no obstacle detection; the vehicle may be switched off as a means of shutting the machine down protecting personnel in the area. In 1995, some Inco researchers went to see the work that was going on at the Pittsburgh Research Center of the United States Bureau of Mines (USBM) and discovered a small scanner that was being used in the Autonomously Guided Vehicle (AGV) market as a safety device. (Figure 3) The USBM researchers were looking at it for other applications and the Inco team also went searching for uses of this system. One idea was to use the system for surveying purposes. This system evolved into the automated toping vehicle currently being placed through trials for accuracy.
The proposed system does not rely on any artificial infrastructure, or positioning signal from GPS (which is unavailable underground) or inertial positioning system, which would add cost. Odometry may be used as a check but is not strictly necessary.
3. System Overview 3.1 Sensors The system uses a 2D laser range finder to sense the immediate environment. In a “corridor” (a drift, crosscut, or ramp) where there is only one path to follow, the vehicle will simply follow the center path of the drift. When an intersection is encountered, the vehicle looks at an internal map to determine which option to take. The most suitable 2D laser range finder found to date is the Laser Measurement System (LMS) from Sick Optic-Electronic. An earlier version of this scanner, the Proximity Laser Scanner (PLS) was evaluated aboard a manually driven vehicle at Inco’s Stobie Mine in 1995. A sample scan from the unit is shown in Figure 4 [Bloomquist]. Note the clear distinction between the two corridors and the intersection connecting them. These groups have also more recently used this particular scanner successfully in the underground environment:
Another use was to consider development of a guidance system that would require only a rudimentary map of the locale and use the scanner for collecting information about the surrounding environment to give an estimate of where it may be in the mine. The concept had a great deal of flexibility over the light rope system, as it was totally flexible in the software system.
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Tamrock’s AutoMine System [Woof].
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Lateral Dynamic’s Loader Automation System.
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LHD guidance research at the Australian Center for Field Robotics and CMTE Australia. [Madhavan].
The primary requirements were for a guidance system that required no infrastructure, and allowed for selectable routes from the surface tele-operation system.
3.2 Map Representation The proposed system differs from those listed
above primarily in the representation of the mine map. Rather than store each point found by the laser scanner to build a large, detailed map of the mine, the internal map is stored as a connected graph that represents the mine’s topology. This is an approach that takes advantage of the drifts and crosscuts common in Inco’s mining methods. To illustrate, Figure 5 shows a section of a mine level. Figure 6 shows a connected graph of the level. This approach to representing mine levels is similar to that used in the traffic control system described by [Vagenas].
3.3 Stages of Operation In practice, the system would use the following stages: 1) Prior Preparation--A connected graph is created to represent the mine level. At first this will be done manually, but in future could be created automatically from engineering drawings or a GIS database of the mine [Hinton]. 2) Route Planning--the operator selects a destination and dispatches the vehicle. The system uses a breadth-first graph search to derive a route. 3) Autonomous Navigation - the vehicle travels along the planned route, switching between intersection and corridor guidance as required. The route is analogous to a finite state machine. 4) Route Complete/Interrupted--the vehicle reports its location and the reason it stopped. The system restores tele-operation control. When guidance resumes, the system returns to Stage 2.
3.4 Guidance When in the autonomous navigation state, the particular guidance algorithm changes depending on where the vehicle is in the connected graph. For example, in a corridor (Figure 7) the vehicle has only to keep centered in the drift or crosscut and watch for obstacles. A PID centering algorithm is sufficient for this stage, but more sophisticated approaches can be introduced to enable higher vehicle speeds.
When an intersection is reached, the system will determine which is the next corridor to aim for from the graph, and then switch to a guidance algorithm such as “virtual force fields” (Figure 8), where the vehicle is drawn towards the goal while being repelled from the walls.
3.5 Collision Avoidance In addition to the range data provided, the Sick LMS has two “zones” which may be programmed into it, a warning zone and a safety zone. The LMS has digital outputs it sets when these zones are infringed, independent of the control software. This will allow the system to stop the vehicle in a reflexive manner if a wall or other large obstacle is encountered. The LMS cannot see obstacles outside of its plane of view, and the forward facing LMS will have to be mounted quite high on LHDs to clear the bucket and its load. This will unfortunately prevent detection of objects below the plane of view.
4. System Architecture The control software will use a ‘layered’ approach. The lowest layers closely connect the systems' sensors to its actuators. Each layer has the bare minimum of resources and complexity required to carry out its task. This approach makes the resulting system highly reactive, efficient, modular and easier to troubleshoot. The system contains three groups of layers: 1) a mission planner that interfaces with the operator; 2) a long-range planner that searches
routes through the mine; and 3) a local path planner that steers the vehicle. Figure 9 shows these three groups and the layers of intelligence applied to operate the vehicle.
5. Trials 5.1 Test AGV The system was first tested using a small AGV using the Engineering 3 Building at the University of Waterloo, Canada to simulate a mine. This test area provided corridors, intersections, and unexpected events and obstacles. The results of this test showed that the system guided along corridors and avoided or stopped for obstacles correctly. The intersection recognition and guidance algorithms worked reliably. The system is ready for tests aboard a mine vehicle in a real mine environment.
5.2 Wagner ST6C LHD Preparation will be made to install an updated version of the system on a Wagner ST6C LHD, to be tested at Inco’s 175 Ore Body test mine in Sudbury, Canada. This will allow further verification and refinement of the algorithms in an actual mine environment.
6. Future Work Hypothesizing on the implementation of the architecture is first on the requirement list in order to make the system a success to the end user. Quite often, the science of a problem interests the people and the whole process is missed. This may lead to problems in implementation and a great deal more effort is called upon to bring technology into the
workplace. While the SICK LMS provides excellent range data at a fairly high rate, there are some issues with its use. It adds significantly to the cost of the automation system and only becomes costeffective against the luminous rope for long trams. Also, it is based on time-of flight from a rotating mirror, which may be subject to problems from the shocks and sustained vibration found on an LHD. Cheaper alternatives, based on solid-state technologies, will be examined. The corridor steering algorithm will be tested first using a simple PID approach based on the error between the alignment of the vehicle and the center of the drift. However, since the layout of the drift ahead is known in detail from the laser range data, a more enhanced path planning and following system will be introduced. There are several different methods for detecting and classifying intersections – Pattern matching, range thresholds, neural networks [Beranger], and others. The different approaches will be evaluated and the one (more likely a combination) that provides the most robust detection will be selected.
7. References Woof, Robot’s First Step [World Mining Equipment Article], (April 2000) Vagenas, A Dispatch Procedure for Traffic Control of a Fleet of Remote Controlled/Automatic LHDs in Underground Hard Rock Mines (1990).
Madhavan, Dissanayake, Durrent-Whyte, Autonomous Navigation of an LHD using a Combined ICP-EKF Approach (1998). Beranger, Herve, Recognition of Intersections
in Corridor Environments (1996). Bloomquist, Maki, Collision Avoidance System for Autonomous Underground Vehicles (1995). Hinton, Requirements for Positioning of Underground Hard Rock Mining Equipment and Experimentation with a Position Estimating System {2000}
Figure 1 –The Sudbury Mining Camp. Production of copper and Nickel has been ongoing for over 100 years.
Figure 2 -Toro 450 fitted with light rope guidance system at the Inco Limited 175 Orebody test facility.
Figure 5 - Section of Mine Level
Figure 3- The PLS 100 from Sick Optic of Germany
Figure 6 – Connected Graph Representing Section of Mine Level
Figure 7 – Corridor Guidance
Figure 4 – Sample Scan from the LMS
Figure 8 – Intersection Guidance
Figure 9 – Software Architecture
