The present application relates generally to work machines. More particularly, the present application relates to object detection using ultrasonic sensors for work machines.
Work machines, such as compaction machines, can be used for compacting substrates. More particularly, after application of an asphalt layer on a ground surface, a compaction machine can be moved over the ground surface in order to achieve a planar ground surface. Compaction machines can be manual, autonomous, or semi-autonomous. To aid in control of the compaction machine, obstacle detection may be employed to detect obstacles with respect to the compaction machine. European Patent No. 1508819 B1 discloses a driving assistance system for an automobile that employs various types of sensors.
In one example, a work machine includes a frame, a sensor assembly, and an ultrasonic sensor. The frame includes a first portion and a second portion that includes a front bumper and is configured to pivot with respect to the first portion for steering the work machine. The sensor assembly is positioned on the work machine and is configured to sense data for detection of obstacles within a first area around the work machine. The ultrasonic sensor is positioned on the front bumper of the second portion and is configured to sense data for detection of obstacles within a second area around the work machine, the second area outside the first area when the second portion is in an articulated position with respect to the first portion.
In another example, a method for detecting obstacles during operation of a work machine includes sensing, using a first sensor assembly positioned on the work machine, data for detection of obstacles within a first area around the work machine; steering the work machine in a first direction by pivoting a second portion of the frame with respect to the first portion of the work machine; and sensing, using an ultrasonic sensor positioned on a front bumper of the second portion of the frame, sense data for detection of obstacles within a second area around the work machine, the second area outside the first area when the second portion is in an articulated position with respect to the first portion.
In another example, a compactor includes first and second frame portions, a sensor assembly, and an ultrasonic sensor. The second frame portion includes a front bumper and is configured to articulate with respect to the first portion for steering the compactor. The sensor assembly is positioned on the first frame portion or the second frame portion and is configured to sense data for detection of obstacles within a first area around the compactor. The ultrasonic sensor is positioned on the front bumper of the second frame portion and configured to sense data for detection of obstacles within a second area around the compactor, the second area outside the first area when the second frame portion is in an articulated position with respect to the first frame portion.
The compactor 100 includes a first frame 102, a second frame 104, an operator cab 106, wheels 108, and a compactor drum 110. The compactor drum 110 includes an outer surface that contacts the ground. An engine can be mounted on the compactor 100 for providing propulsion power. The engine may be an internal combustion engine such as a compression ignition diesel engine, or any other engine, including a gas turbine engine, for example. The operator cab 106 is mounted to the first frame 102. For manual or semi-autonomous machines, an operator of the compactor 100 can be seated within the operator cab 106 to perform one or more machine operations.
The second frame 104 may be connected to the first frame 102 such that the second frame 104 is able to articulate or pivot with respect to the first frame 102 to steer the compactor 100. The second frame 104 is configured to rotatably support the compactor drum 110, which moves along, and provides compaction for, the ground surface. The compactor drum 110 acts as a ground engaging member that rotates about a respective axis thereby propelling the compactor 100 on the ground surface along with the wheels 108. In other examples, the wheels 108 can be replaced with a second compactor drum that operates in a similar manner to the compactor drum 110.
The compactor 100 may include an obstacle detection system, for example, using one or more sensors or other devices configured to sense data for detection of obstacles around the compactor 100. For example, the compactor 100 may include two lidar assemblies 112a and 112b mounted to the first frame 102 and positioned to detect objects surrounding the compactor 100. Due to the field-of-view of the lidar assemblies 112a and 112b, two additional lidar assemblies may be needed to provide coverage for the area 114a when the frame 104 is articulated to steer the compactor 100 to the left (as illustrated by the arrow in
The obstacle detection system may also include a radar sensor 115. The radar sensor 115 may be mounted to the second frame 104 to provide further data regarding obstacles in front of the frame 104 of the compactor 100. For example, the radar sensor 115 may be able to provide coverage in front of the frame 104, as illustrated by area 114d. However, similar to the lidar assemblies 112a and 112b, the radar sensor 115 is unable to detect objects in the area 114a during a left turn.
To detect objects within the area 114a (and a corresponding area when the second frame 104 is articulated to turn the compactor 100 to the right), ultrasonic sensors 116a and 116b may be positioned on the front of the second frame 104. For example, due to the range provided by ultrasonic sensors, it may be desirable to position the ultrasonic sensors 116a and 1161) on opposite ends of a front bumper of the second frame 104, in front of the compactor drum 110. The placement of the ultrasonic sensors 116a and 116b on the right- and left-hand side of the front bumper of the second frame 104 provides steering coverage for the area 114a, eliminating the need for two additional lidar assemblies, providing a significant cost savings.
The compactor 100 may include a control and memory circuit 118 used to receive data from the lidar assemblies 112a and 112b, the radar sensor 115, the ultrasonic sensors 116a and 116b, and/or other sensors of an obstacle detection system. The control and memory circuit 118 can include, for example, software, hardware, and combinations of hardware and software configured to execute several functions related to, among others, obstacle detection for the compactor 100. The control and memory circuit 118 can be an analog, digital, or combination analog and digital controller including a number of components. As examples, the control and memory circuit 118 can include integrated circuit boards or ICB(s), printed circuit boards PCB(s), processor(s), data storage devices, switches, relays, or any other components. Examples of processors can include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or integrated logic circuitry.
The control and memory circuit 118 may include storage media to store and/or retrieve data or other information such as, for example, input data from the lidar assemblies 112a and 112b and the ultrasonic sensors 116a and 116b. Storage devices, in some examples, are described as a computer-readable storage medium. The data storage devices can be used to store program instructions for execution by processor(s) of control and memory circuit 118, for example. The storage devices, for example, are used by software, applications, algorithms, as examples, running on and/or executed by control and memory circuit 118. The storage devices can include short-term and/or long-term memory and can be volatile and/or non-volatile. Examples of non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Examples of volatile memories include random access memories (RAM), dynamic random-access memories (DRAM), static random-access memories (SRAM), and other forms of volatile memories known in the art.
While illustrated as positioned on the compactor 100, one or more control systems may be positioned remote from the compactors 100. For example, a remote computing system may be used by an operator to control the compactor 100 for a fully autonomous machine. In this example, the control and memory circuit 118 may communicate data to the remote computing device, or the lidar assemblies 112a and 112b and the ultrasonic sensors 116a and 116b may directly transmit data to the remote computing system.
The ultrasonic sensor 116a is positioned on the left side of the bumper 200, left of center with respect to the frame 102 and the ultrasonic sensor 116b is positioned on the right side of the bumper 200, right of center with respect to the frame 102. In some examples, the ultrasonic sensors 116a and 116b may mounted or otherwise attached at any position on the bumper 200. In an example, the ultrasonic sensors 116a and 116b are positioned as close to the edges of the bumper 200 as possible to increase coverage during steering of the compactor 100.
At step 304, a steering operation begins for the compactor 100. To accomplish the steering operation, the frame 104 articulates with respect to the frame 102 to turn the compactor 100. Because of the articulation of the compactor 100, the lidar assemblies 112a and 112b on the frame 102 do not sense data within the projected path of the compactor 100. At step 306, to sense data within the projected path, ultrasonic sensors 116a and 116b are employed. The ultrasonic sensors 116a and 116b may be positioned on the bumper 200 of the frame 104 forward of the compactor drum 110. For example, the ultrasonic sensor 116a on the left portion of the bumper 200 may be used to sense data during a left turn of the compactor 100, and the ultrasonic sensor 116b on the right portion of the bumper 200 may be used to sense data during a. Right turn of the compactor 100. At step 308, during turning of the compactor 100, obstacles are detected using both the lidar assemblies 112a and 112b and the respective ultrasonic sensor. Obstacles in the projected path may be detected using the ultrasonic sensors 116a and 116b, and obstacles to the sides and rear of the compactor may be detected using the lidar assemblies 112a and 112b.
In one illustrative example, the work machine is an articulated-type automated soil compactor. The automated soil compactor includes an obstacle detection system configured to detect obstacles around the soil compactor during operation of the soil compactor. The obstacle detection system may include at least two lidar assemblies and two ultrasonic sensors. The lidar assemblies are positioned on a first frame portion of the soil compactor and configured to sense data for detection of obstacles around the soil compactor. When a second portion of the frame turns with respect to the first portion to steer the soil compactor, the lidar assemblies sense insufficient data in the projected path of the compactor.
To sense data in the projected path, the ultrasonic sensors are used. One ultrasonic sensor is positioned on the second portion of the frame to sense data for detection of obstacles during a left turn of the compactor, and one ultrasonic sensor is positioned on the second portion of the frame to sense data for detection of obstacles during a right turn of the compactor. By using ultrasonic sensors, obstacle detection coverage during steering of the soil compactor can be accomplished without the need for additional lidar assemblies. Ultrasonic sensors are significantly cheaper than lidar assemblies and thus, by using ultrasonic sensors for steering coverage, the overall cost of the obstacle detection system is greatly reduced.
The above detailed description is intended to be illustrative, and not restrictive. The scope of the disclosure should, therefore, be determined with references to the appended claims, along with the full scope of equivalents to which such claims are entitled.
Number | Name | Date | Kind |
---|---|---|---|
4403220 | Donovan | Sep 1983 | A |
5529138 | Shaw et al. | Jun 1996 | A |
5719713 | Brown | Feb 1998 | A |
5732785 | Ran | Mar 1998 | A |
5734336 | Smithline | Mar 1998 | A |
6268803 | Gunderson | Jul 2001 | B1 |
6642839 | Gunderson | Nov 2003 | B1 |
6690413 | Moore | Feb 2004 | B1 |
7061372 | Gunderson et al. | Jun 2006 | B2 |
7130727 | Liu | Oct 2006 | B2 |
8427288 | Schofield | Apr 2013 | B2 |
10798303 | Camyre et al. | Oct 2020 | B1 |
11247610 | Carpenter | Feb 2022 | B2 |
20060287829 | Pashko-Paschenko | Dec 2006 | A1 |
20080263912 | Gharsalli et al. | Oct 2008 | A1 |
20120105638 | Englander | May 2012 | A1 |
20120154785 | Gilliland et al. | Jun 2012 | A1 |
20150210213 | Mitsuta | Jul 2015 | A1 |
20150253427 | Slichter et al. | Sep 2015 | A1 |
20160054283 | Stromsoe | Feb 2016 | A1 |
20160138247 | Conway | May 2016 | A1 |
20170010621 | Rio et al. | Jan 2017 | A1 |
20170118915 | Middelberg | May 2017 | A1 |
20170120800 | Liñan et al. | May 2017 | A1 |
20180077851 | Hatton | Mar 2018 | A1 |
20180170369 | Mitchell | Jun 2018 | A1 |
20180319392 | Posselius | Nov 2018 | A1 |
20180319396 | Foster | Nov 2018 | A1 |
20190003137 | Gao et al. | Jan 2019 | A1 |
20190014723 | Stanhope | Jan 2019 | A1 |
20190079532 | Crawley | Mar 2019 | A1 |
20190093299 | Meixner | Mar 2019 | A1 |
20190235504 | Carter | Aug 2019 | A1 |
20190382005 | Nishi | Dec 2019 | A1 |
20200113118 | Stanhope | Apr 2020 | A1 |
20200132835 | Han | Apr 2020 | A1 |
20200183008 | Chen | Jun 2020 | A1 |
20200231210 | Anderson | Jul 2020 | A1 |
20200256775 | White | Aug 2020 | A1 |
20200346581 | Lawson | Nov 2020 | A1 |
20210141080 | Oetken et al. | May 2021 | A1 |
20210240193 | Endo et al. | Aug 2021 | A1 |
20210350681 | Imaizumi | Nov 2021 | A1 |
20220050209 | Tsujimura | Feb 2022 | A1 |
Number | Date | Country |
---|---|---|
206599718 | Oct 2017 | CN |
207109515 | Mar 2018 | CN |
109826074 | May 2019 | CN |
102014209744 | Dec 2015 | DE |
102014111098 | Feb 2016 | DE |
102014014294 | Mar 2016 | DE |
1508819 | Jan 2008 | EP |
Entry |
---|
“U.S. Appl. No. 16/846,009, Non Final Office Action dated Jul. 2, 2021”, 10 pgs. |
“U.S. Appl. No. 16/846,009, Response filed Sep. 24, 2021 to Non Final Office Action dated Jul. 2, 2021”, 17 pages. |
“U.S. Appl. No. 16/846,009, Final Office Action dated Oct. 27, 2021”, 20 pgs. |
“U.S. Appl. No. 16/846,009, Response filed Jan. 26, 2022 to Final Office Action dated Oct. 27, 2021”, 10 pgs. |
“U.S. Appl. No. 16/846,009, Pre-Appeal Brief Request filed Jan. 26, 2022”, 5 pgs. |
“U.S. Appl. No. 16/846,009, Final Office Action dated Feb. 22, 2022”, 23 pgs. |
“U.S. Appl. No. 16/846,009, Decision on Pre-Appeal Brief Request mailed Feb. 9, 2022”, 2 pgs. |
“U.S. Appl. No. 16/846,009, Appeal Brief filed Jun. 13, 2022”, 31 pgs. |
Number | Date | Country | |
---|---|---|---|
20210318430 A1 | Oct 2021 | US |