Wednesday, January 04, 2006


Autonomous land vehicles have been an intense area of research and development for the last decades. An excellent introduction and summary of the state-of-the-art is given in this chapter. In this section we report on successful projects in application areas that relate to the proposed forest based system we are aiming at.

  • Forest Vehicles

  • In our search for research related to our proposed development project, we found many research articles related to Ground Navigation Vehicles from the different people from all over the world, these valuable research articles are found in web sites. Only one project with similar goals has been found: The ROFOR project run by Anibal Ollero, University of Seville, Spain. Not much information are to be found on the project, which has been going on since 1997. The objective of the project is the design, development and implementation of a control system for a forest processor machine (felling, cutting and to heap up). The project also includes the design of the robot arm and vehicle control system. The latest information ( to be found reports:

    “In this project a forest processing machine has been partially automated. thus, autonomous and tele-operated functions have been combined in the control system. A distributed control system has been developed and implemented in the machine. The system can be implemented by using both cable and wireless communications between the operator cabin and a PLC in the processing unit. The tele-operation system integrates the joystick and other devices for machine operation, and a graphical interface for the visualization of the processing functions during operation. The system also includes functions for machine diagnosis, operator and machine production control, and communications with a control centre using GSM. The tasks during 2000 have been mainly devoted to debugging, integration of the system, and testing.”

  • Path tracking Vehicles

  • Path tracking methods aim at keeping the vehicle approximately on a pre defined path, and bring it back to the path when unacceptable deviations occur. Various approaches for this task have been presented for AGV usage.
    The main goal of the path tracking vehicle is to present a working solution for autonomous path tracking navigation, to be implemented in a vehicle for operation in forest terrains. The Autonomous Ground Vehicle (AGV) should operate in two modes: Path Recording, and Path Tracking. In the Path Recording mode, a human driver drives along the chosen path, recorded in the computer memory. In the Path Tracking mode, the computer assumes control over propulsion and steering. The vehicle then automatically travels along a memorized path. The operation has to account for unplanned deviations from the path, caused by imperfect sensing of position, and also by the vehicle sliding and jumping along the path. Another important part involves detection of new obstacles appearing on the path. In some cases the system should stop the vehicle and alert the human operator, who should be given the option of manually correcting the vehicle position, or giving the system the green light to go ahead along the original path.

  • Agricultural vehicles

  • A lot of research and development with autonomous vehicles for use in agriculture has been conducted the last decades. The primary agricultural activities addressed have been harvesting, mowing and applications of pesticides. O’Connor et al. at Stanford University developed a system for agricultural equipment that follows a preplanned path. A four-antenna system with Differential GPS (DGPS) provided a heading accuracy of 0.1 degrees and offset accuracy of 2.5 cm. A row-following system for harvesting in cauliflower fields was developed by Marchant et al. At Carnegie-Mellon Robotics Institute an autonomous vehicle for cutting forage using vision-based perception on the cut and uncut regions of crop was developed . The developed system used DGPS combined with wheel encoders and gyro data to compute estimates of both position and attitude. The vision sensing included functions for vehicle guidance (row-following), “end-of-row” detection, correction of illumination due to shadows and obstacle detection. An adaptive Fisher discriminant classifier was used to segment the images in cut/uncut regions by pixel wise classification based on RGB values. The obstacle detection was implemented with similar techniques where each pixel was classified as “normal” or “abnormal” relative to a training image. The probabilities for a pixel belonging to the probability distributions constructed from the training image were used to decide if the pixel belongs to an obstacle or not. Regions with a large number of such pixels were identified as obstacles. Three onboard computers were used, one for image analysis, one for control and one for task management. A pure pursuit algorithm is used for the path tracking task.

  • Mass excavation

  • Automatic digging is an active field of research. One approach is to let a human operator select the digging point and to let the autonomous system take over to complete the dig. Another, more difficult, approach is to use active sensing and automatically select the dig point. Stentz et al. [Sten01] developed a fully autonomous 25-ton hydraulic excavator for loading a truck with soft material such as dirt. The machine uses laser rangefinders to recognize the truck, detect obstacles and controlling the digging and loading process. The scanners scan vertically and are mounted on a pan table swept left and right, thereby covering all space of interest. According to the authors, the excavator is a fully operational prototype but is “unlikely to appear commercially as such, at least not initially”. It does not work under all weather conditions. The system is able to detect most obstacles, but “could not be left alone to work a complete shift without incident”. One suggested way to use it commercially is to demand a human operator to remain on the machine to monitor for safety, but to let the computer take over the actual digging/loading. Alternatively, a remote operator could monitor the work, check plans for repositioning and manually make corrections when necessary.

  • Mining machines

  • Mining is an important application domain for autonomous off-road vehicles. Stentz et al. [StOl99] report on a development of two mining aids: a system to measure and control forward motion of a continuous miner, and a system to measure and control the machine’s heading. Both measures are important to ensure high cutting quality of the coal. The heading task is solved by Kalman fusion of a fiber-optic gyro with a laser/camera system. The motion is computed by correlating stereo images of the roof of the mine, taken with a short delay. A recent project, reported in [ACFR01], use a scanning laser to detect guideposts located on the side of the haul road. The system aims at determining safe manual driving control of large haulage vehicles.

  • Target machines

  • The final target machine will be a standard forest machine, configured for the automatic computer control and for the need of sensors of various kinds. This is the most common approach to AGV design: simply automate an existing manual vehicle. To simplify things, many manufacturers provide an “automation option” on manual vehicles. E.g. Kalmar Industries (of Finland) who manufactures container straddle carriers and forestry logging equipment. Komatsu-Haulpack (large mining trucks and excavators) and Vost-alpine (underground mining equipment) also offer automation options for their products. However, in our case it will be necessary to install an automation interface since the plan is to use a standard forest machine from Partek Forest AB.

    Some parts of the project can be run without access to a real forest machine. For this purpose a development robot should be purchased and installed in a “miniature forest” inside or outside the research premises. Some parts, such as cameras or laser detectors, have to be mounted on the real target machine that can provide a realistic environment for development and testing. Of course, the final complete system will be also installed on a real forest machine and tested under real conditions.

  • Development robot

  • The project will benefit from an additional platform in development and testing, for the following reasons:
    It will increase the productivity of the development work since a full sized forest machine will need considerably more effort to access and use.
    The modifications of the forest machine are not trivial and can be performed in parallel with the development work.

    A lot of hardware and software will have to be purchased, learned and tested as part of the project. This work is most efficiently done in-doors, but need a reasonably realistic substitute for the eventual target machine In addition to serving the goals of this project, a development robot will be useful since the research team will learn both hardware and software relevant for many future projects. The equipment can also be used for teaching robotics, aim at developing education in autonomous systems for off-road use. The approach with a small-scaled robot is encouraged in [HaCa01], and also considered as a better alternative than computer simulation. After developing and testing on a small-scale robot, the systems can be transferred to the target vehicle, requiring much less modification than a simulated counterpart.

    Two main alternatives for a development robot exist: buying standard equipment or having a custom designed robot constructed and manufactured. The latter alternative has the advantage of providing a platform that can mimic certain characteristics of the real forest machine that can be of value for the development work. This would for example be the construction of the steering device. The controller for a vehicle with articulated joint steering will differ significantly from the controller in a vehicle with a differential drive or with Ackerman (“carlike”) steering. Other important characteristic that is lost in a standard robot is the size aspects. The placement of sensors such as cameras or laser range scanners affects the behavior of the autonomous vehicle to a large extent. However, even a custom designed robot will differ significantly from the final target machine. Extensive work will therefore have to be performed on the target machine, regardless of the choice of development robot. The advantage with buying a standard robot is first of all that it is available and works immediately (in a reasonably ideal world).


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