SYSTEM AND METHOD FOR CHARGING VEHICLES WITH RENEWABLE ENERGY

Abstract

A computer, encompassing the programming for: Forecasting, in a vehicle at a multitude of locations, a quantity of electricity generation associated with each location; wherein the amount of electricity generated is based on data that the computer receives from one or more sensors; and wherein the sensors measure data in real time at the vehicle's location; Select one of the locations based on at least the projected electricity generation volumes; Determine that a movement threshold has been reached; and Moving the vehicle to the selected location after the movement threshold is reached, the determination of whether a movement threshold has been reached includes the following: Determining the electricity generation difference between a current location and the selected location; and Determining the energy required to move the vehicle to the selected location; The threshold of motion is reached when the difference is greater than the required energy.

Description

TECHNICAL FIELD

[0001] A system and method for charging vehicles with renewable energy. Autonomous electric vehicles are becoming increasingly widespread. Such vehicles typically charge by plugging into a utility grid or can be charged using available renewable resources, such as solar or wind power. However, renewable resources may not be available in certain locations and may be less efficient or inefficient.

[0002] The document US 2013 / 0 285 841 A1 discloses a solar-powered motor vehicle with a battery charged by a solar panel. Furthermore, a computer system is provided that generates a list of parking spaces using programs and makes parking recommendations to the vehicle operator based on the solar radiation.

[0003] WO 2015 / 114 200 A1 discloses a device, a method, and a computer program for charging a vehicle. The device receives information from a solar-powered vehicle, including the vehicle's location and the distribution of solar energy. Based on this information, a solar power charging strategy is determined, which the solar-powered vehicle accesses.

[0004] EP 2 578 997 A1 discloses a system for assisting a user of an electrically powered vehicle, comprising a position information acquisition device, an electrical energy quantity estimation device, an undersupply risk estimation device, and a notification device. This informs the user when a predetermined threshold regarding the estimated risk of an undersupply of an electrical energy quantity is exceeded. DRAWINGS Fig. 1 is a block diagram of the components of an example of renewable energy charging; Fig. 2 Illustrates a flowchart of an example method for charging a vehicle; and Fig. 3 is a top plan view showing vehicles with the system of Fig. 1, which illustrates the process of Fig. 2. DETAILED DESCRIPTION THE INTRODUCTION

[0005] A charging system for renewable energies 20 (see Fig. 1) provides a hardware and software infrastructure to improve the efficiency of an autonomous rechargeable electric vehicle 300 (see Fig. 3) that receives energy from natural renewable sources such as sun and wind. The system 20 provides a solution to the problem of rechargeable vehicles 300 that are parked in locations that are not ideal for charging, such as locations in the shade or on the left side of a building. Accordingly, a computer 30 of the system 20 is mounted in a vehicle 300 (see Fig. 1 and Fig. 3) programmed to forecast power generation at a plurality of locations, select one of the locations based at least on the forecasted power generation, and move the vehicle 300 to the selected location. The amount of power generation is based on data the computer receives from one or more sensors, which measure data in real time at the vehicle's location. The computer 30 may take additional considerations into account when selecting locations, such as distances to the locations, location types, and risk factors associated with the locations.

[0006] Once the location is selected and prior to moving to the selected location, the computer 30 determines whether a movement threshold, i.e., a value indicating that the vehicle 300 should be moved to the location for charging, is met by the selected location, e.g., whether the energy expended to move to the selected location is less than the projected gains in power generation at the selected location.

[0007] In the following description, the adjectives “first” and “second” are used in this document as identifiers and are not intended to imply meaning or order. THE SYSTEM

[0008] As in Fig. 1, A renewable energy charging system 20 includes a computer 30 in communication with one or more sensors 35, a steering system 40, a drive train 45, a navigation device 50, and a communication device 55.

[0009] The computer 30 is a computing device that includes a processor 32 and a memory 34.

[0010] Processor 32 is implemented using circuits, chips, or other electronic components and may include one or more microcontrollers, one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more custom integrated circuits, etc. Processor 32 is programmable to process data and communications via memory 34, sensors 35, steering 40, powertrain 45, navigation device 50, and communications device 55. Processing the data and communications may include moving vehicle 300 from one location to another while forecasting power generation at each location, selecting one of the locations based on the forecast power generation, and moving vehicle 300 to the selected location when a relocation threshold is reached.As described below, the processor 32 instructs the components of the vehicle 300 to actuate based on the sensor data.

[0011] Memory 34 is implemented via circuits, chips, or other electronic components and may include one or more of read-only memory (ROM), random access memory (RAM), flash memory, electrically programmable memory (EPROM), electrically programmable and erasable memory (EEPROM), embedded MultiMediaCard (eMMC), a hard disk, or any volatile or non-volatile media, etc. Memory 34 may store instructions for performing the processes described herein and data collected from sensors and communications.

[0012] The computer 30 is in electronic communication with one or more conventional and known (and therefore not shown in the drawings) input devices for providing data to the computer 30 and one or more output devices for receiving data and / or instructions from the computer 30, e.g., operating an output device. Example input devices include: human machine interfaces (HMIs) such as a switch or graphical user interface (GUI); imaging devices such as LiDAR, still and / or video cameras, infrared sensors, the steering 40, the powertrain 45, the navigation device 50, the communication device 55, etc., as well as other sensors and / or electronic control units (ECUs) known to provide data, e.g.,in a vehicle connected to the communications bus or network, such as radar, ultrasonic sensors, accelerometers, gyroscopes, pressure sensors, thermometers, barometers, altimeters, current sensors, voltage sensors, microphones, light sensors, etc. Example output devices that may be actuated by computer 30 include: warning light and audible subsystems; HMIs, the steering system 40, the powertrain 45, the navigation device 50, the communication device 55, etc.

[0013] Sensors 35 collect and send data to computer 30. Sensors 35 can detect internal conditions of vehicle 300, such as wheel speed, wheel orientation, and engine and transmission variables. Sensors 35 can detect the position or orientation of vehicle 300, for example, global positioning sensors (GPS); accelerometers such as piezoelectric or microelectromechanical systems (MEMS); gyroscopes such as rate, ring laser, or fiber optic gyroscopes; inertial measurement units (IMUs); and magnetometers. Sensors 35 can detect the outside world, for example, light measurement sensors, photometers, wind speed measurement sensors, radar sensors, scanning laser range finders, light detection and illumination devices (LIDAR), and image processing sensors such as cameras.

[0014] The steering system 40 is in communication with the computer 30. When actuated by the computer 30, the steering system 40 decouples and / or maintains a steering angle of one or more tires of the vehicle 300. Thus, the steering system 40 controls known electrical and hydraulic systems in the vehicle 300 that affect the steering angle of one or more tires of the vehicle 300.

[0015] The drivetrain 45 is in communication with the computer 30. When actuated by the computer 30, the drivetrain 45 provides torque to the wheels of the vehicle 300, propelling the vehicle 300 forward. The drivetrain 45 may include an electric motor, a battery pack, and a charging system such as known solar and wind charging systems. The drivetrain 45 may also include an internal combustion engine. The battery pack is connected to the electric motor and the charging system. In a conventional electric drive, the electric motor is rotatably coupled to the transmission. In a hybrid drive, the electric motor is coupled to the transmission and transfers rotating kinetic energy to the transmission, and the internal combustion engine may be coupled to the electric motor or to the transmission.The transmission transfers the kinetic energy from the electric motor and / or the internal combustion engine to a drive axle and ultimately to wheels of the vehicle 300, while providing a gear ratio that allows different compromises between torque and speed.

[0016] The navigation device 50 is in communication with the computer 30. The navigation device 50 determines a location of the vehicle 300 relative to the stored map data. To determine the location, the navigation device 50 may rely on information from a global navigation satellite system, ranging data from sensors 35 attached to a powertrain of the vehicle 300, a gyroscope, and / or an accelerometer. The map data may be stored locally, such as on the memory 34, or on the navigation device 50. Additionally and / or alternatively, the map data may be stored on a remote computer or network accessible via the communication device 55. Example navigation devices 50 include known navigation devices that utilize GPS (global positioning system), Wi-Fi, and / or other technologies.

[0017] Communication device 55 is in communication with computer 30. Communication device 55 wirelessly transmits and receives information from other communication devices, allowing signals, data, and other information to be exchanged with other computer and network systems. Example communication devices 55 include well-known WLAN systems, radio transmitters and receivers, telecommunications systems, Bluetooth systems, cellular systems, and mobile satellite communication devices. THE PROCEDURE

[0018] In relation to Fig. 2, the process 200 begins on the basis of the computer 30 in a rechargeable vehicle 300 (see Fig. 3) receiving an instruction to begin process 200, or by generating its own instruction to begin process 200. For example, computer 30 may receive input from a user via an HMI connected to computer 30. In another example, the computer itself may determine that the instruction to begin, for example, should be based on the time of day, from data received from sensors 35, such as data indicating a change in the amount of light hitting the vehicle from a park, an instruction generated after autonomous vehicle 300 reaches a desired destination, etc.

[0019] Next, at a block 210, the computer 30 causes the vehicle 300 to move to a first location. The computer 30 may cause the vehicle 300 to move by operating the drivetrain 45 and the steering 40. The computer 30 may use the navigation device 50 and the map data stored therein to determine coordinates, e.g., geo-coordinates, of the first location. For example, location data may include data identifying a Cartesian coordinate system location, such as latitudinal and longitudinal coordinates of the first location. The location data may alternatively or additionally include vector data, including a direction and a distance, from a reference location, such as an intersection or a building. The location data may be used to determine distances between other locations, a location type, and risk factors associated with the location, as discussed further below.The computer 30 can provide navigation data with the navigation device 50, which is used in operating the drive 45 and the steering 40 when moving the vehicle 300 to the location.

[0020] The distance to the first (and a subsequent) location may be shorter, e.g., 20 feet, or longer, e.g., 100 meters, depending on the surrounding geographic and other features, such as the user's preferred locations, the proximity of the locations to the user's home, work location, etc. For example, the computer 30 may include instructions to limit the movement of the vehicle 300 within a certain geographic area. The distance to the first location may be as small as zero, e.g., if the vehicle 300 starts the process 200 at the first location. In such a situation, the computer 30 may begin the process at a block 215.

[0021] At block 215, while the vehicle 300 is at the first location, the computer 30 forecasts a first amount of power generation. The amount of power generation is based on data the computer receives from one or more sensors 35. For example, the sensors 35 may measure the brightness of the light hitting the vehicle 300 to estimate power generation from solar energy, where the higher the measured brightness, the higher the forecast power generation. For example, a location with 30,000 lux has higher forecast power generation than a location of 25,000 lux. In another example, the sensors 35 may measure wind speed to estimate power generation from wind energy. The higher the measured wind speed, the higher the forecast power generation. For example, a location measuring 15 mph average wind speed has higher forecast power generation than a location with a wind speed of 5 km / h.The measured brightness and wind speed can be used to predict power generation by comparing the measured sensor values ​​with the sensor values ​​associated with power generation in a lookup table 30 stored as a file on the computer. The relationship between sensor values ​​and power generation rates can be predetermined and incorporated into the table, at least based on performance characteristics of various solar and wind energy systems supported on the vehicle 300.

[0022] At a block 220, the computer 30 moves the vehicle 300 to a second location.

[0023] The computer can cause the vehicle 300 to move by actuating the drivetrain 45 and the steering 40. The manner in which the steering 40 and the drive 45 actuate the drivetrain can be based at least on information from the navigation device 50. The computer 30 can use the navigation device 50 and the data stored therein to determine location data, e.g., as described above, of the second location. The computer 30 can use the navigation device 50 to provide navigation data for navigation in order to navigate the vehicle 300 to and from the second location.

[0024] At a block 225, while the vehicle 300 is at the second location, the computer 30 predicts a second amount of power generation, e.g., as described above.

[0025] At a block 230, the computer 30 causes the vehicle 300 to move to another location, e.g., a third location, a fourth location, ... Ththe location. The computer can cause the vehicle 300 to move by operating the drive train 45 and the steering 40. The manner in which the steering 40 and the drive 45 actuate the drive train can be based at least on information from the navigation device. The computer 30 can use the navigation device 50 and the data stored therein to determine location data, e.g., as described above, of the various locations. The computer 30 can use the navigation device 50 to provide navigation data to navigate the vehicle 300 to and from the other location.

[0026] At a block 235, while the vehicle 300 is at the other location, the computer 30 predicts an amount of power generation, e.g., as described above.

[0027] At a block 240, the computer 30 determines whether a sufficient number of locations have forecasted the associated power generation. The sufficient number of locations is equal to or less than an available number of locations. The determination of whether a sufficient number of power generation locations have been forecast may be determined based on whether the forecasting power was performed at a predetermined number of locations; whether a location with forecast power generation above a threshold was identified; whether power generation was forecast over a particular geographic area or route, etc. If the computer 30 determines that the sufficient number of locations have been measured, the computer moves to a block 245. If the computer 30 determines that the sufficient number of locations have not been measured, the computer returns to block 230.

[0028] At a block 245, the computer 30 determines a current location of the vehicle 300. The computer 30 may utilize the navigation device 50 and the data stored therein to determine location data of the current location. Location data may include data identifying a Cartesian coordinate system location, such as latitudinal and longitudinal coordinates of the current location. The location data may include vector data, including a direction and a distance, from a reference location, such as an intersection or a building.

[0029] At a block 250, while the vehicle 300 is at the current location, the computer 30 predicts an amount of power generation, e.g., as described above.

[0030] At block 255, computer 30 determines the distances between the current location and each of the other locations, such as the first location and the second location, associated with a predicted power generation. Computer 30 may use navigation device 50, map data, and location data associated with each location to determine the distances, e.g., using known techniques.

[0031] At a block 260, the computer 30 determines a location type for each of the locations associated with forecasted power generation. Location types are categories of locations that affect the availability of the location as a parking space for the vehicle 300 and / or the user's ability to access the vehicle 300. Example location types include: user-approved locations, private locations, public locations, proximity to a user's home or work, etc. To determine the location type, the computer 30 may compare the location with data available on map data stored on the computer; obtain information about the location, including the location type, from a user, such asthrough an HMI; Obtain location information from another computer or information network, such as information received via communication device 55 from a remote server, a portable computing device, or information otherwise available on the Internet.

[0032] At a block 265, the computer 30 determines risk factors associated with each of the locations. Risk factors are factors that could affect the availability of continued power generation at the forecasted power generation rate. Example risk factors may include: An expected duration available at the location for the parking vehicle 300, such as parking spaces with maximum permitted parking time; An expected charging duration, such as finding a parking space that may be in the shade within a period of time, e.g., 1 hour; Distance from the user's work or home; Distance from the current location; Whether the location is in a parking lot or on a street; Etc. The expected duration of charging availability may be based on information the computer 30 receives via a weather forecast or on information about the geographical characteristics, e.g.Physical landscape features of an area located near the area. Location, such as information about the height of a building, tree, or other structure. Determining the risk factors for each location can be performed as a single processing step, identifying all risk factors associated with the various locations. Alternatively, risk factor determination can be broken down into a series of discrete steps, such as determining the risk factors for a location while the vehicle 300 is at that location, for example, in the power generation forecasting rule.An exemplary calculation of the risk factor is found below, where n corresponds to a number of points, n-power is the number of points for a given amount of power generation, n-risk is the number of points deducted based on the risk factor(s), and n-location is the resulting number of points associated with the location. The number of points for n-power and n-risk can be determined using a lookup table stored as a file on computer 30, the lookup table associating the rates of forecasted power generation with a given number of points, and certain risk factors with a given number of points. npower−nrisk=nlocation

[0033] At a block 270, the computer 30 selects one of the locations based at least on the forecasted level of power generation. The computer 30 may consider distances from the current location, location types, and the risk factors for each location. For example, the computer 30 may assign a certain number of points to a location based on the forecasted amount of power generation at that location, where the higher the forecast amount of power generation, the more points are allocated. The computer 30 may then adjust the location's point allocation based on the associated risk factors. For example, if the location is predicted to be in the shade, points may be deducted. In another example, points are added if the location is a user-selected location. In another example, the greater the distance from the current location, the more points are deducted.Once the points for each location are awarded and adjusted, the computer 30 can select the location associated with the highest number of points.

[0034] At block 275, computer 30 determines whether a relocation threshold has been reached. The relocation threshold can be used to determine whether it is worthwhile to move vehicle 300 to the selected location, i.e., whether vehicle 300 should be relocated to the selected location.

[0035] To determine whether the movement threshold has been reached, the computer 30 determines the difference between the forecasted power generation at the selected location and the forecasted power generation at the current location. The determined difference is compared with the movement threshold. an amount of energy intended to move the vehicle 300 to the selected location. If the difference in the predicted power generation is greater than the movement threshold, the computer 30 determines that the movement threshold has been reached. If the difference in the predicted power generation is smaller than the movement threshold, e.g., the energy required to move to the selected location, the computer 30 can determine that the movement threshold has not been reached.

[0036] In another example, computer 30 may compare the number of points associated with the selected location, as discussed above, to a moving threshold, which is a number of points. The moving threshold number of points may be set by the manufacturer of vehicle 300 or by the user of vehicle 300. If the points associated with the selected location are greater than the threshold number of points, computer 30 may determine that the moving threshold has been met. If the points associated with the selected location are less than the threshold number of points, computer 30 may determine that the moving threshold has not been met.

[0037] At block 280, if the relocation threshold is reached, the computer 30 causes the vehicle 300 to relocate to the selected location. The computer may cause the vehicle 300 to move by actuating the drivetrain 45 and the steering 40. The manner in which the steering 40 and the drive 45 actuate the drivetrain may be based at least on information from the navigation device 50.

[0038] In relation to Fig. 3, autonomous vehicles 300a and 300b are shown with the system 20 performing various blocks of the process 200. References in the following discussion to the vehicles 300a and 300b performing various operations after process blocks 200 are understood to use the system 20, such as using the computer 30, to perform the various movements, predictions, and determinations.

[0039] Vehicle 300a is parked at location 320a. Before parking at location 320a, vehicle 300a first moved to location 320b (block 210) and forecast a first amount of power generation (block 215). Then, vehicle 300a moved to location 320a (block 220) and forecast a second amount of power generation (block 225). Then, vehicle 300a moved to location 320c and forecast a third amount of power generation. Location 320c had a lower forecast power generation than the other locations due to a tree 330 casting shadow. While at location 320c, vehicle 300a determined its current location (block 245) and the distance to the other locations 220a and 220b (block 255). Next, vehicle 300a determined the risk factors associated with locations 320a-c (Block 265). Location 320b had a risk factor created by a building 335 that could create a shadow later in the day.After determining the risk factors, vehicle 300a selected a selected location 320a because location 320c had lower predicted power generation and location 320b had higher risk factors (block 270). Vehicle 300a then determined that the increase in power generation at the selected location 320a, e.g., the difference between predicted power generation at locations 320a and 320c, was greater than the energy it would need to move power from location 320c to the selected location 320a, and concluded that the transition threshold had been met (block 275). Vehicle 300a then traveled to the selected location 320a (block 280).

[0040] Vehicle 300b is parked at location 320d. Before parking at location 320d, vehicle 300b stopped near the user's residence 340 to drop the user. The user instructed vehicle 300b via an HMI to stay near residence 340. Vehicle 300b then moved to location 320d (block 220) and forecast a first amount of power generation (block 225). Vehicle 300b then moved to location 320e (block 210) and forecast a second amount of power generation (block 215). The forecast power generation at location 320e was lower than at location 320d because location 320 was in the shadow of a garage of residence 340. While at location 320e, vehicle 300b determined its current location (block 245) and the distance to location 320d (block 255). Vehicle 300b selected location 320d because location 320e had predicted lower power generation (block 270).Vehicle 300b then determined that the increase in power generation at the selected location 320d was greater than the energy required to move from the current location 320e to the selected location 320d and concluded that the transition threshold had been reached (block 275). Vehicle 300b then moved to the selected location 320d (block 280). CONCLUSION

[0041] Computing devices, as discussed herein, typically each contain instructions executable by one or more computing devices, such as those mentioned above, and for performing blocks or steps of processes described above. Computer-executable instructions may be compiled from or interpreted from computer programs created using a variety of programming languages ​​and / or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, Visual Basic, Java Script, Perl, HTML, etc. Typically, a processor (e.g., a microprocessor) receives instructions, e.g., from memory, a computer-readable medium, etc., and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein.Such instructions and other data may be stored and transmitted using a variety of computer-readable media. A file in computing device 105 is typically a collection of data stored on a computer-readable medium, such as a storage medium, random access memory, etc.

[0042] A computer-readable medium includes any medium involved in providing data (e.g., instructions) that can be read by a computer. Such a medium can take many forms, including, but not limited to, non-volatile media, volatile media, etc. Non-volatile media includes, for example, optical or magnetic hard disks and other persistent memory. Volatile media includes dynamic random access memory (DRAM), which is typically main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punched cards, paper tape, any other physical medium with holes, a RAM, a PROM, an EPROM, a FLASH EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read.

Claims

[1] A computer comprising programming to: Forecasting, in a vehicle at a plurality of locations, an amount of electricity generation associated with a particular location; wherein the amount of power generated is based on data that the computer receives from one or more sensors; and wherein the sensors measure the data in real time at the vehicle's location; Selecting one of the locations based on at least the forecast electricity generation volumes; Determining that a movement threshold has been reached; and Moving the vehicle to the selected location after the movement threshold is reached, wherein determining whether a movement threshold is reached comprises: Determining a power generation difference between a current location and the selected location; and Determine the energy required to move the vehicle to the selected location; where the movement threshold is reached when the difference is greater than the required energy. [2] The computer of claim 1, further comprising programming to: After forecasting the power generation quantities at each of the plurality of locations, determining a current location of the vehicle; and Determining the distances between the plurality of locations and the current location; wherein the selection of one of the locations is further based on at least the respective distances from the current location to the locations. [3] The computer according to claim 1, wherein the power generation amount is predicted based on at least data received from a light sensor supported on the vehicle. [4] The computer according to claim 1, wherein the power generation amount is predicted based on at least data received from a wind sensor supported on the vehicle. [5] The computer of claim 1, further comprising programming to: Identify a risk factor associated with each of the multiple locations; wherein the selection of one of the locations is further based on at least the risk factors. [6] The computer of claim 5, wherein the risk factors are based at least on information about the geographical characteristics of the neighboring areas of the plurality of locations. [7] The computer of claim 1, further comprising programming to: Determining the location type of each of the plurality of locations; wherein the selection of one of the locations is further based at least on the identified location types. [8] The computer of claim 1, further comprising programming to: Determining the respective distances between a structure and each of the plurality of locations; wherein the selection of one of the locations is further based at least on the distances from the structure to the locations. [9] Procedure comprising: Forecasting, in a vehicle at a plurality of locations, an amount of electricity generation associated with each location; wherein the amount of power generated is based on data that the computer receives from one or more sensors; and wherein the sensors measure the data in real time at the vehicle's location; Selecting one of the locations based on at least the forecast electricity generation volumes; Determining that a movement threshold has been reached; and Moving the vehicle to the selected location after the movement threshold is reached, wherein determining that the movement threshold is reached comprises: Determining a power generation difference between a current location and the selected location; and Determine the energy required to move the vehicle to the selected location; where the movement threshold is reached when the difference is greater than the required energy. [10] The method of claim 9, further comprising: After determining the amount of electricity generated at the plurality of locations, determining a current location of the vehicle; and Determining the distances between the current location and each of the plurality of locations; wherein the selection of one of the locations is further based on at least the determined distances between the current location and the plurality of locations. [11] The method of claim 9, wherein the available power generation amount is predicted based on at least data received from a light sensor supported on the vehicle. [12] A method according to claim 9, wherein the available power generation amount is predicted based on at least data received from a wind sensor supported on the vehicle. [13] The method of claim 9, wherein the at least one of the plurality of locations is a user-defined location. [14] The method of claim 9, further comprising programming to: Determining a risk factor associated with each of the plurality of locations; wherein the selection of one of the locations is further based on at least the identified risk factors associated with the locations. [15] A method according to claim 14, wherein the identified risk factors associated with the locations are based at least on an expected charging time available at the locations. [16] The method of claim 9, further comprising programming to: Determining the location types for the multitude of locations; wherein selecting one of the locations is further based at least on the particular location types of the locations. [17] The method of claim 9, further comprising programming to: Determining a distance between a structure and each of the plurality of locations; wherein selecting one of the locations is further based on at least the determined distances between the structure and the locations.

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