No part of this invention was a result of any federally sponsored research.
The present invention relates in general to autonomous systems, and, more specifically, to autonomous truck loading for mining and construction applications.
A portion of the disclosure of this patent application may contain material that is subject to copyright protection. The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyrights whatsoever.
Certain marks referenced herein may be common law or registered trademarks of third parties affiliated or unaffiliated with the applicant or the assignee. Use of these marks is by way of example and should not be construed as descriptive or to limit the scope of this invention to material associated only with such marks.
A number of autonomous trucks are being developed for the mining and construction industries. Much of the automation on these trucks concentrates on the excavators, and on the autonomous driving of the trucks; however, as of now, not much autonomous function exists for loading the trucks.
Trucks in a mine generally move dirt, ore, and other matter from one location to another. The ore is usually loaded by an excavator or a loader. In manned vehicles, there is a sequence of coordinated maneuvers as part of the loading process. These coordinated maneuvers include tasks that are performed with the truck, and tasks that are performed solely with the attached excavator or loader. Currently, the humans performing the tasks have a relatively small amount of sensors helping them, but there are also many techniques that the loading operator uses intuitively:
On the loader side, the loading procedure is also significantly affected by the machinery being used. For example, an excavator will follow a different procedure than a front-end loader, and a feeder may require significantly different maneuvers.
For each of these alternatives, there are slightly different loading techniques that are used, both by the truck driver and by the loader. All these peculiarities of the problem are learned with experience and (to a certain degree) with some trial and error on the job. In order to automate the process, this knowledge needs to be explicitly encoded as part of the automation process.
The present invention encodes this knowledge into a database of preferred loading conditions and creates a set of automated maneuvers that accomplish the loading actions. The invention assumes that the truck has a drive-by-wire kit and that it is capable of moving under computer control.
This invention provides a set of tools that allows for the automation of the loading process. The invention is relevant to situations where the truck is autonomous and the excavator is not, or when both are automated.
To minimize the limitations in the prior art, and to minimize other limitations that will be apparent upon reading and understanding the present specification, the present invention describes autonomous truck loading for mining and construction applications.
These and other advantages and features of the present invention are described herein with specificity so as to make the present invention understandable to one of ordinary skill in the art, both with respect to how to practice the present invention and how to make the present invention.
The system is composed of a number of sensors that can be placed both on the loader, on the truck, or in the mining or construction area. By describing the maneuver, we can teach the different automation steps. The process of loading can be divided into three distinct phases: alignment/docking, loading, and departure.
The invention provides a series of tools and behaviors that can be used in each one of these phases. Moreover, the invention has a scripting language that allows for a mining/construction operator to modify and customize the process at each step:
Depending on the type of mining operation or construction needs, it is not uncommon for the particulars of the loading area to change often. The scripting language has to be sufficiently streamlined where these maneuvers can either be learned, or scripted in a relatively simple way.
Using the invention, the maneuvers in each of the phases above can be driven (or teleoperated) by an operator, and then have the system “replay” that maneuver. The scripting language can use these learned trajectories and concatenate them into new more complex maneuvers.
The scripting language allows the mine operators to assemble and compose new autonomous vehicle behavior. In this particular case, the behavior in focus is the loading of the autonomous truck.
The scripting language in the invention is a graphical user interface, where blocks in the display represent the elementary behavior upon which more complex behavior is built upon.
In particular, the scripting language has behavior blocks, sensing blocks, and logic blocks. Some of the blocks can be learned. For example, the operator may choose to record a trajectory. This trajectory becomes a behavior block. Now, the operator can link two or more of these behaviors to create a more complex behavior. The sensor blocks allow the operator to concatenate behavior until a particular sensor (or combination of sensors) achieve a certain value.
For example, let's say that the mine operator would like to create a new loading behavior. He/she can take a behavior block that encapsulates the motion of the truck to the loading area, then he/she can use the behavior block that positions the middle of the truck perpendicular to the excavator tracks; finally, he/she can use a sensing block and a logic block that forces the truck to stay in that position, until the truck is loaded and the excavator arm is out of the way. Finally, the operator can concatenate another behavior block that has the truck undock and go to the dumping area. The scripting language in the invention is hierarchical, in the sense that more complex behaviors can be encapsulated by using simpler blocks. The preferred embodiment of the invention uses a visual language, as it is simpler to understand by the mining operators; however, other embodiments may have other scripting languages that are not visual, and use text to describe the sequences of actions.
There are three distinct steps on setting up the system:
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. Note with respect to the materials of construction, it is not desired nor intended to thereby unnecessarily limit the present invention by reason of such disclosure.
The present invention describes the development of a system creating and executing loading behavior between a truck and a loader that is comprised of a truck with a drive-by-wire kit, a database of stored maneuvers that are relevant to the phases of the loading process and a controller that executes a series of maneuvers that place the truck within the workspace of the loader, and moves the truck as to facilitate the process of loading.
A drive-by-wire kit is a complete hardware and software system that allows seamless electronic control of a vehicle's brake, throttle, steering, and shifting to enable testing for autonomous vehicle applications.
This system that has been developed has some or all of the stored maneuvers created by driving the vehicle manually, created by teleoperating the vehicle, or by using a route planner.
There is a scripting language that allows the mining or construction operator to assemble the maneuver from the different behaviors in this system. There is a simulator that allows the operator to verify the script.
A scripting or script language is a programming language for a special run-time environment that automates the execution of tasks; the tasks could alternatively be executed one-by-one by a human operator. Scripting languages are often interpreted.
In this system, the different behaviors account for variation of the truck being used, or the loader being used.
In this system, the behaviors use sensors in the truck and/or leader to verify that the loading process has been completed.
In this system that has been developed, the truck is equipped with weight measuring sensors that can indicate where the maximum load capacity has been reached.
The maneuvers are different depending on the type of load, or wetness of the load. This system is further enhanced with a sensor or sensor located on the loaders, the truck, or in the mining/construction areas (LADAR, stereo pair, cameras, RF beacons, DGPS, acoustic sensors, or RADAR), which provide the autonomous truck with accurate positioning.
Laser Detection and Ranging (LADAR) illuminates a target with pulsed or modulated laser light and then measures the reflected energy with a sensor. Differences in laser return times and wavelengths are then used to generate accurate target representations via high-res 3D shape and detailed vibration spectrum data that is as unique as a fingerprint. This data is then compared to an existing database of similar items, and the precision results are instantly conveyed back to the user. Generally, this technology is also known as Light Imaging, Detection, and Ranging (LIDAR).
Stereo pair refers to a pair of flat perspective images of the same object obtained from different points of view. When a stereopair is viewed in such a way that each eye sees only one of the images, a three-dimensional (stereoscopic) picture giving a sensation of depth is perceived.
In navigation, a radio frequency (RF) beacon is a kind of beacon, a device which marks a fixed location and allows direction finding equipment to find relative bearing. Radio beacons transmit a radio signal which is picked up by radio direction finding systems on ships, aircraft and vehicles to determine the direction to the beacon.
Differential Global Positioning System (DGPS) is an enhancement to the Global Positioning System (GPS) which provides improved location accuracy, in the range of operations of each system, from the 15-meter nominal GPS accuracy to about 1-3 cm in case of the best implementations.
Rayleigh scattering based distributed acoustic sensing (DAS) systems use fiber optic cables to provide distributed strain sensing. In DAS, the optical fiber cable becomes the sensing element and measurements are made, and in part processed, using an attached optoelectronic device. Such a system allows acoustic frequency strain signals to be detected over large distances and in harsh environments.
Radio Detection and Ranging (RADAR) refers to a detection system that uses radio waves to determine the range, angle, or velocity of objects. It can be used to detect aircraft, ships, spacecraft, guided missiles, motor vehicles, weather formations, and terrain.
In this system that has been developed, the sensors are also used to detect humans, vehicles, and other obstacles, and slows down or stops to avoid collisions. The weight of each trailer in the truck is transmitted to the loader. The weight on each wheel in each of the parts of the truck is transmitted to the trailer.
In this system, the leader and the trucks share localization information that is used as part of the scripting language.
In this system that has been developed, multiple loaders are used to speed up the process of loading the autonomous trucks.
In
The present application is a continuation of U.S. patent application Ser. No. 16/676,544, filed Nov. 7, 2019 and issued as U.S. Pat. No. 11,656,626, which claims benefit of and priority under 35 U.S.C. § 119(e) to and is a non-provisional of U.S. Provisional Patent Application No. 62/759,963, filed Nov. 12, 2018, each of which is hereby incorporated by reference herein in its entirety.
Number | Name | Date | Kind |
---|---|---|---|
5555503 | Kyrtsos et al. | Sep 1996 | A |
6044312 | Sudo | Mar 2000 | A |
6341372 | Datig | Jan 2002 | B1 |
9383754 | Takeda | Jul 2016 | B2 |
9925662 | Jules et al. | Mar 2018 | B1 |
10048692 | Hamada | Aug 2018 | B2 |
10071893 | High et al. | Sep 2018 | B2 |
10108196 | Kadono | Oct 2018 | B2 |
10394250 | Kadono | Aug 2019 | B2 |
10662613 | Ready-Campbell | May 2020 | B2 |
10802503 | Minagawa | Oct 2020 | B2 |
11353865 | Lacaze | Jun 2022 | B2 |
11656626 | Lacaze | May 2023 | B2 |
20040158355 | Holmqvist | Aug 2004 | A1 |
20080021632 | Amano | Jan 2008 | A1 |
20080243381 | Villalobos et al. | Oct 2008 | A1 |
20090076674 | Kiegerl | Mar 2009 | A1 |
20110029238 | Lee et al. | Feb 2011 | A1 |
20120092486 | McDaniel | Apr 2012 | A1 |
20120136509 | Everett | May 2012 | A1 |
20120136524 | Everett et al. | May 2012 | A1 |
20130173109 | Hukkeri | Jul 2013 | A1 |
20130261870 | Halder et al. | Oct 2013 | A1 |
20130325208 | Osagawa | Dec 2013 | A1 |
20140110989 | McKinley | Apr 2014 | A1 |
20140309841 | Hara | Oct 2014 | A1 |
20140371947 | Stratton | Dec 2014 | A1 |
20150057871 | Ono et al. | Feb 2015 | A1 |
20150285650 | Lewis | Oct 2015 | A1 |
20160040397 | Kontz | Feb 2016 | A1 |
20160264032 | Terada et al. | Sep 2016 | A1 |
20160271795 | Vicenti | Sep 2016 | A1 |
20160314224 | Wei et al. | Oct 2016 | A1 |
20160349754 | Mohr et al. | Dec 2016 | A1 |
20160379152 | Rodoni | Dec 2016 | A1 |
20170017235 | Tanaka | Jan 2017 | A1 |
20170247033 | Vandapel | Aug 2017 | A1 |
20170253237 | Diessner | Sep 2017 | A1 |
20170285655 | Katou | Oct 2017 | A1 |
20170314955 | Lynn | Nov 2017 | A1 |
20170315515 | Vandapel et al. | Nov 2017 | A1 |
20180004224 | Arndt et al. | Jan 2018 | A1 |
20180016124 | Keller | Jan 2018 | A1 |
20180044888 | Chi | Feb 2018 | A1 |
20180088591 | Friend | Mar 2018 | A1 |
20180267537 | Kroop | Sep 2018 | A1 |
20190033877 | Wei | Jan 2019 | A1 |
20190072953 | Maheshwari et al. | Mar 2019 | A1 |
20190073762 | Kean | Mar 2019 | A1 |
20190113919 | Englard | Apr 2019 | A1 |
20190212745 | Wendt et al. | Jul 2019 | A1 |
20190279508 | Wang | Sep 2019 | A1 |
20190286148 | Hase et al. | Sep 2019 | A1 |
20190302794 | Kean | Oct 2019 | A1 |
20190370726 | Ha et al. | Dec 2019 | A1 |
20200033847 | Way et al. | Jan 2020 | A1 |
20200050192 | O'Donnell et al. | Feb 2020 | A1 |
20200117201 | Oetken et al. | Apr 2020 | A1 |
20200150656 | Lacaze et al. | May 2020 | A1 |
20200150668 | Lacaze et al. | May 2020 | A1 |
20200150687 | Halder et al. | May 2020 | A1 |
20200174486 | Uo et al. | Jun 2020 | A1 |
20200180924 | Acaze et al. | Jun 2020 | A1 |
20200225675 | Lacaze et al. | Jul 2020 | A1 |
20200344622 | Campbell, Jr. et al. | Oct 2020 | A1 |
20200362541 | Takaoka | Nov 2020 | A1 |
20200384987 | Preissler | Dec 2020 | A1 |
20200394813 | Theverapperuma et al. | Dec 2020 | A1 |
20200401134 | Lacaze et al. | Dec 2020 | A1 |
20210064050 | Pickett et al. | Mar 2021 | A1 |
20210124359 | Wei | Apr 2021 | A1 |
20210141372 | Lacaze et al. | May 2021 | A1 |
20220253062 | Gan | Aug 2022 | A1 |
20220356674 | Norfleet | Nov 2022 | A1 |
Number | Date | Country |
---|---|---|
106627456 | May 2017 | CN |
H08-263138 | Oct 1996 | JP |
2017180430 | Oct 2017 | WO |
Entry |
---|
Notice of Allowance, issued Feb. 2, 2023 (Feb. 2, 2023), in U.S. Appl. No. 16/676,544. (8 pages). |
Office Action, issued Jul. 30, 2021 (Jul. 30, 2021), in U.S. Appl. No. 16/676,544. (19 pages). |
Office Action, issued Feb. 18, 2022 (Feb. 18, 2022), in U.S. Appl. No. 16/676,544. (37 pages). |
Office Action, issued Jul. 11, 2022 (Jul. 11, 2022), in U.S. Appl. No. 16/676,544. (38 pages). |
Office Action, issued Nov. 2, 2022 (Nov. 2, 2022), in U.S. Appl. No. 16/676,544. (19 pages). |
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20230251666 A1 | Aug 2023 | US |
Number | Date | Country | |
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62759963 | Nov 2018 | US |
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Parent | 16676544 | Nov 2019 | US |
Child | 18132539 | US |