Route planning method, route planning device, and program

Abstract

To provide a method for planning an optimal route for at least one of a plurality of requests while satisfying the plurality of requests contradictory to each other.SOLUTION: A route planning method executed by a computer comprises the steps of: calculating a plurality of movement routes of a movable body from the initial position to the destination and selecting a multi-purpose optimal solution when targeting achievement of a requirement about movements in mutually-trade-off relation from the plurality of movement routes; setting a constraint condition and an objective function necessary for formulating a route plan of the movable body as an optimization problem with the achievement level of the requirement in the movement route selected as the multi-purpose optimal solution as a reference; and planning the movement route and the speed of the movable body by solving the optimization problem.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a path planning method, a path planning device, and a program. [Background technology]

[0002] In order to move a moving object to a destination, it is necessary to plan a route while considering operational objectives and constraints. Patent Document 1 discloses a technology for planning a safe route to a destination by using a neural network that has been trained to detect environmental changes around the vehicle with a sensor and convert the detected changes into a vehicle trajectory so that the vehicle can travel to the destination while avoiding obstacles such as other vehicles and pedestrians while satisfying constraints such as speed. Patent Document 1 does not disclose a technology for planning an optimal route by considering multiple conflicting evaluation indexes such as travel time and energy consumption.

[0003] Non-Patent Document 1 also discloses a method for planning an optimal route taking into consideration four evaluation indices: travel time, smoothness of the route for simplifying operations, energy consumption, and obstacle avoidance. Since these four evaluation indices are mutually exclusive, a Pareto-optimal route, which is in a trade-off relationship, is randomly searched for, and an operator determines one optimal solution by considering the balance of each evaluation indices from among multiple routes. In the method described in Non-Patent Document 1, a route is planned taking into consideration multiple conflicting evaluation indices, but it is assumed that travel will be at a constant speed, and it is not possible to optimize both the route and the speed.

[0004] Patent Document 2 describes a technology that, when an autonomous vehicle detects an obstacle and changes its route, plans the speed for moving on the changed route as a speed that minimizes the speed cost calculated based on the initial speed, speed limit, etc. Minimizing the speed cost can reduce sudden speed changes. In the technology of Patent Document 2, both the route and the speed are optimized, but the route and the speed are optimized using separate evaluation functions, and it is not possible to optimize travel time and energy consumption that change depending on both route changes and speed changes. In addition, the technology of Patent Document 2 plans only a local route and speed, so it is not possible to consider travel time to the destination or total energy amount as constraints. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Special Publication No. 2019-519851 [Patent Document 2] JP 2019-128962 A [Non-patent literature]

[0006] [Non-Patent Document 1] Y. Ma, et. al., Multi-objective path planning for unmanned surface vehicle with currents effects, ISA Transactions, 2018 Summary of the Invention [Problem to be solved by the invention]

[0007] There is a need for a method that can flexibly plan a movement path and speed that optimizes at least one of multiple conflicting evaluation indexes related to the movement of a moving object while satisfying specified constraints for the other evaluation indexes.

[0008] The present disclosure provides a route planning method, a route planning device, and a program that can solve the above problems. [Means for solving the problem]

[0009] According to one aspect of the present disclosure, a route planning method is a route planning method executed by a computer, the route planning method comprising the steps of: calculating a plurality of movement routes of a moving object from an initial position to a destination; selecting, from the plurality of movement routes, a multi-objective optimal solution for the purpose of achieving requirements related to movement that are in a trade-off relationship with each other; and, referring to a degree of achievement of the requirements in the movement route selected as the multi-objective optimal solution, The operator sets Optimization objectives and constraints for formulating the path planning of the moving object as an optimization problem a constraint for the optimization problem; a constraint for the optimization problem; and planning a moving path and speed of the moving object by solving the optimization problem.

[0010] According to one aspect of the present disclosure, the route planning device includes a means for calculating a plurality of travel routes of a moving object from an initial position to a destination, and selecting a multi-objective optimal solution from among the plurality of travel routes when the objective is to achieve travel-related requirements that are in a trade-off relationship with each other, and a means for selecting a multi-objective optimal solution from the travel route selected as the multi-objective optimal solution based on the degree of achievement of the requirements. The operator sets Optimization objectives and constraints for formulating the path planning of the moving object as an optimization problem a constraint for the optimization problem; a constraint for the optimization problem; and means for solving the optimization problem to plan a route and a speed of the moving body.

[0011] According to one aspect of the present disclosure, the program includes a step of causing a computer to calculate a plurality of movement paths of a moving object from an initial position to a destination, and selecting a multi-objective optimal solution from among the plurality of movement paths when the objective is to achieve requirements related to movement that are in a trade-off relationship with each other; and a step of calculating a degree of achievement of the requirements in the movement path selected as the multi-objective optimal solution based on the degree of achievement of the requirements in the movement path selected as the multi-objective optimal solution. The operator sets Optimization objectives and constraints for formulating the path planning of the moving object as an optimization problem a constraint for the optimization problem; a constraint for the optimization problem;and a step of solving the optimization problem to plan a route and a speed of the moving body. Effect of the Invention

[0012] According to the above-mentioned route planning method, route planning device, and program, it is possible to optimize at least one of a plurality of conflicting requirements and evaluation indexes related to the movement of a moving object, and to plan a movement route and optimal speed that satisfies desired constraints for the other requirements. [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 is a functional block diagram illustrating an example of a route planning device according to an embodiment. [Diagram 2] FIG. 2 is a diagram showing an overall process flow of a route planning method according to the embodiment. [Diagram 3] FIG. 2 is a first diagram illustrating calculation of a multi-objective optimal route according to the embodiment. [Figure 4] FIG. 2 is a second diagram illustrating calculation of a multi-objective optimal route according to the embodiment. [Diagram 5] 5A and 5B are diagrams illustrating an example of an objective function and constraint conditions according to the embodiment. [Figure 6] 10 is a flowchart illustrating an example of a route search process according to the embodiment. [Figure 7] FIG. 4 is a diagram illustrating a route search process according to the embodiment. [Figure 8] FIG. 1 is a diagram illustrating an example of a hardware configuration of a route planning device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] <Embodiment> Hereinafter, a route planning method according to the present disclosure will be described with reference to FIGS. (composition) FIG. 1 is a functional block diagram illustrating an example of a route planning device according to an embodiment. The path planning device 10 is a device that plans a moving path and speed of a moving body 1, such as an unmanned underwater vehicle (UUV), an advanced safety vehicle (ASV), a planetary rover, or an unmanned aerial vehicle (UAV). The moving body 1 is not limited to an autonomous moving body, and may be a moving body that moves by human operation. The path planning device 10 can plan an optimal path and speed considering multiple conflicting requirements related to movement, such as energy consumption required for movement, moving time, moving speed, and moving distance. The conflicting requirements are, for example, that energy consumption and moving time are in a trade-off relationship, and it is often difficult to satisfy both requirements at the same time, such as reducing energy consumption and increasing moving time. In contrast, in this embodiment, multiple evaluation indexes to be considered in the path planning problem are classified into objectives and constraints, and are reduced into a constrained optimization problem, thereby planning an optimal path considering multiple conflicting requirements.

[0015] As shown in FIG. 1, the route planning device 10 includes an input information acquisition unit 11, a route advance calculation unit 12, a route planning unit 13, a storage unit 14, and an output unit 15. The input information acquisition unit 11 is configured using input devices such as a keyboard, a mouse, a touch panel, a button, a signal input interface, etc., and acquires information input using these input devices. The input information acquisition unit 11 acquires information necessary for route planning, such as basic information necessary for route planning, such as position information of the initial position and destination of the moving body 1, map information of the moving range of the moving body 1, information on conditions that affect movement such as wind, tides, and slopes, and the possible moving speed of the moving body 1, as well as information such as an objective function (evaluation function) and constraint conditions that are necessary when formulating and solving a route planning problem as a constrained optimization problem.

[0016] The advance route calculation unit 12 calculates a multi-objective optimal route (a route that optimizes multiple evaluation indexes such as energy consumption and travel time, a Pareto optimal solution) for a route from an initial position to a destination in order to grasp in advance how much energy consumption and travel time are required to move a moving body from an initial position to a destination before planning an optimal route. For this calculation, the technology described in Non-Patent Document 1 can be used. The calculated multi-objective optimal route is used as reference information when making decisions regarding the objectives and constraints of a constrained optimization problem. For example, by referring to the multi-objective optimal route, an operator can grasp the trade-off relationship between multiple evaluation indexes.

[0017] The route planning unit 13 solves a constrained optimization problem with one or more of the multiple evaluation indexes as an objective function and the others as constraint conditions, and plans an optimal route or the like that satisfies the constraints (in the following embodiment, an example of determining a route and an optimal speed will be given). The objective and constraints of the optimization problem are set by an operator who refers to the multi-objective optimal route calculated by the pre-route calculation unit 12.

[0018] The memory unit 14 is configured using a storage device such as an HDD (hard disk drive) or a flash memory, and stores various data such as the information acquired by the input information acquisition unit 11, a program (software) for calculating the route plan, and calculation results. The output unit 15 outputs the calculation result of the travel route to a display device or the moving object 1.

[0019] (operation) FIG. 2 shows the overall process flow of the route planning method according to this embodiment. First, an operator selects whether or not to automatically plan a moving route (step S1). If automatic planning is not required (step S1; No), the operator manually sets the route and speed of the moving object 1 (step S2).

[0020] In the case of automatic planning (step S1; Yes), the route planning device 10 starts planning a route. In this case, an operator sets information required for route planning in the route planning device 10 in advance. The input information acquisition unit 11 records the set information in the storage unit 14. First, the route planning device 10 uses the advance route calculation unit 12 to plan a multi-objective optimal route (step S3). The advance route calculation unit 12 calculates a Pareto optimal solution for energy consumption and travel time to calculate a multi-objective optimal route. A method for calculating a multi-objective optimal route will be described with reference to Figs. 3 and 4.

[0021] FIG. 3 is a first diagram illustrating calculation of a multi-objective optimal route according to the embodiment. The area 30 in FIG. 3 is the moving range of the moving object 1. If the grid at the top left of the moving range 30 is represented as (A, 1) and the grid at the bottom right as (F, 6), the route planning device 10 plans a route for the moving object 1 to move from the initial position (B, 5) to the destination (F, 1). In the calculation of the multi-objective optimal route in step S3, the pre-route calculation unit 12 randomly generates a moving route from the initial position (B, 5) to the destination (F, 1), and calculates the energy consumption and the moving time when the moving object 1 moves at a predetermined speed (the speed is constant) along the generated moving route. The pre-route calculation unit 12 may be set to generate a predetermined number of routes or more. The routes 31 to 34 in FIG. 3 are examples of routes randomly generated by the pre-route calculation unit 12. The pre-route calculation unit 12 calculates the moving time and the energy consumption for each of the routes 31 to 34. Any known method can be used as a method for calculating the moving time and the energy consumption. For example, the energy consumption increases when the moving body 1 moves against the wind or tide or goes up a slope, whereas the energy consumption may be small even if the moving distance is long when the moving body 1 moves with the backing of the wind or tide or goes down a slope. The advance route calculation unit 12 records the calculated energy consumption and travel time for each route in the storage unit 14 in association with the route.

[0022] FIG. 4 is a second diagram illustrating the calculation of a multi-objective optimal route according to the embodiment. The vertical axis of the graph in FIG. 4 indicates the energy consumption required to move from the initial position to the destination, and the horizontal axis indicates the travel time. One circle point in the graph corresponds to one route, and is plotted at a position indicating the energy consumption and travel time when moving along that route. The pre-route calculation unit 12 selects a multi-objective optimal route from among these circle points. A multi-objective optimal route is a route (Pareto optimal solution) in which, when one of multiple evaluation indexes is improved, the other evaluation indexes deteriorate. In FIG. 4, the multi-objective optimal route is indicated by a white circle point, and the others are indicated by a black circle point. For example, in the case of the circle point 41, the "energy consumption" of the evaluation indexes is the smallest. If an attempt is made to improve the "travel time" of the evaluation index, only routes exist in which the "energy consumption" is worse than the point 41 ("when one evaluation index is improved, other evaluation indexes deteriorate"). If a route such as that represented by the star point 40 exists, even if the "travel time" is improved for the circle point 41, there exists a star point 40 where the "energy consumption" does not deteriorate, and in this case, the circle point 41 is no longer a Pareto optimal solution. The pre-route calculation unit 12 selects a circle point having the same properties as the circle point 41 as the multi-objective optimal route.

[0023] Note that, although an example of calculating a multi-objective optimal route for energy consumption and travel time has been given here, this is not limited to this, and a multi-objective optimal route can be calculated for any number of evaluation indexes (e.g., travel time and travel distance).

[0024] The pre-route calculation unit 12 outputs the selected multi-objective optimal route and the values ​​of the evaluation indices (in this example, "energy consumption" and "travel time") to a display device (not shown) through the output unit 15. For example, if routes 31 to 34 in FIG. 3 are multi-objective optimal routes, the display device displays "energy consumption" and "travel time" for each of the routes 31 to 34 together with the image exemplified in FIG. 3. The operator looks at the displayed "energy consumption" and "travel time" to grasp and organize the approximate values ​​of "energy consumption" and "travel time" required for travel from the initial position to the destination and the trade-off relationship between them, consider which evaluation index is to be optimized (objective), which evaluation index is to be used as a constraint, and what the values ​​of the constraint conditions should be, and further decide whether to add constraint conditions including speed (step S4).

[0025] Next, the operator selects whether or not to add constraints to the route plan (step S5). If constraints are not to be added (step S5; No), the operator selects, for example, one of the multi-objective optimal routes calculated in step S3 and sets a desired speed (step S6). This determines the route of the moving body 1.

[0026] When adding a constraint condition (step S5; Yes), the operator sets the constraint condition and objective function in the route planning device 10. The input information acquisition unit 11 records the set information in the storage unit 14. FIG. 5 shows an example of the objective function and constraint condition that are set. FIG. 5 shows a constrained optimization problem in which ranges are set for travel time and speed as constraints, and energy consumption is minimized. Equation (1) in FIG. 5 is the objective function (evaluation function), and represents the minimization of energy consumption. e i (v i ) is the speed of moving object 1 v i By WP i From WP i+1is the energy consumption when moving from WP to WP. WP is a waypoint. A waypoint is a target point to which the moving body 1 moves. By instructing the moving body 1 on the position information of the waypoint, the moving body 1 can move along a desired moving route. Using equation (1), not only is it possible to minimize the total energy consumption, but also to minimize the speed v between each WP at that time. i Equations (2) and (3) in Fig. 5 are constraint conditions, where equation (2) indicates the travel time constraint and equation (3) indicates the speed range constraint. d i WP i From WP i+1 The distance between each WP is expressed as the distance to the destination. i Add a constraint that the total travel time when moving at speed v is less than or equal to the time constraint T, and use equation (3) to i In this way, by dividing the evaluation indexes to be considered when planning a route into constraints and objectives and solving a constrained optimization problem, it is possible to obtain the optimal speed (the speed that minimizes energy consumption) between each WP while satisfying constraints such as the travel time to the destination and the amount of energy consumed.

[0027] When the operator sets constraints and instructs execution of route planning, the route planning device 10 executes calculation of an optimal route and speed that satisfies the constraints (step S7). The route planning unit 13 searches for a route by the method described below, and calculates a route and speed that satisfies the conditions exemplified in Fig. 5. Next, the process of planning the optimal route and speed will be described with reference to Figs. 6 and 7.

[0028] FIG. 6 is a flowchart illustrating an example of a route search process according to the embodiment. FIG. 7 is a diagram illustrating the route search process according to the embodiment. First, the route planning unit 13 generates a plurality of local routes at a distance d from the current WP (step S11). The route planning unit 13 generates straight lines 711-718 that are extended at a constant distance d at regular intervals within a range of ±θ degrees from the current position (current WP 71 in FIG. 7) in the direction of the next WP 72 on a default route (for example, a reference route selected by an operator), and sets these as candidates for the local route. By setting the local routes as straight lines, the generation cost of the local routes can be reduced.

[0029] Next, the route planning unit 13 generates a global route from the grid to which the end positions of each of the generated local route candidates belong to to the destination (step S12). The generated global route is a multi-objective optimal route from the center of the grid including the end positions of the local routes to the destination. The route planning unit 13 generates a multi-objective optimal route with respect to the energy consumption and travel time from the center of the grid in which the end positions of the local routes are located to the destination 74. The method of generating the multi-objective optimal route is the same as the process of step S3 in FIG. 2 described using FIG. 3 and FIG. 4. The route planning unit 13 randomly generates multiple routes from the grid center 73 of the end positions to the destination 74, sets a predetermined travel time within the constraint range of formula (3), and calculates the energy consumption and travel time of each route. Then, the route planning unit 13 selects Pareto-optimal routes 731 to 733 from the multiple generated global routes. By generating a multi-objective optimal route that minimizes both travel time and energy consumption as a global route, it is possible to generate a route that reduces energy consumption but has a long travel distance (e.g., global route 731), a route that is short in travel distance but has high energy consumption (e.g., global route 733), etc. Note that while Fig. 7 only illustrates global routes 731-733 (multi-objective optimal routes) from the grid center 73 to the destination 74, the route planning unit 13 also calculates multi-objective optimal routes from the grid centers to which the terminal positions of the other local routes 713-718 belong to to the destination 74.

[0030] Next, the route planning unit 13 selects a local route with the smallest energy consumption from among candidates of local routes arriving at each grid cell, and connects the selected local route to a multi-objective optimal route among the global routes generated in step S12 (step S13). For example, if the route with the smallest energy consumption from among the local routes 711 to 718 is the local route 711, the route planning unit 13 generates the local route 711 + global route 731, the local route 711 + global route 732, and the local route 711 + global route 733. (The end of the local route 711 is not physically connected to the start of the global routes 731 to 733.)

[0031] Next, the route planning unit 13 calculates an optimal speed that satisfies the constraints of the travel time and the speed range for all routes generated by connecting the local routes and the global routes in step S13 (step S14). That is, the route planning unit 13 calculates a speed that satisfies the formulas (2) and (3) and minimizes the objective function of formula (1). For the speed and travel time from the initial position to the current WP 71, values ​​already calculated (from the initial position to the current WP 71) by the process of this flowchart can be used. For example, for one route generated in step S13, the route planning unit 13 adds the travel time when moving from the current WP 71 to the destination 74 at the optimal speed being searched (speed that satisfies the constraint of formula (3)) to the calculated travel time from the initial position to the current WP 71, and determines whether the value satisfies formula (2), while adjusting the speed and searching for the optimal speed. The route planning unit 13 also calculates the optimal speed for each of the remaining routes generated in step S13, which satisfies equations (2) and (3) and minimizes the objective function of equation (1).

[0032] Next, the route planning unit 13 selects a route that minimizes the energy consumption among the routes that satisfy the constraints calculated in step S14, and sets the position of the next WP (for example, WP75) and the speed when moving from the current WP73 to the next WP75 (step S15). The route planning unit 13 selects a route that minimizes the energy consumption among the routes that satisfy the constraints calculated in step S14, sets the end of the local route on that route as the position of the next WP, and sets the optimal speed calculated for the selected route as the speed when moving from the current WP71 to the next WP75. In this way, the speed that minimizes the energy consumption while satisfying the constraints for each interval between WPs can be obtained (the speed may differ for each interval between WPs). Also, by optimizing the speed on the route, a combination of the route and the speed that minimizes the evaluation function that changes depending on both the route and the speed can be planned.

[0033] Next, the route planning unit 13 calculates the distance between the position of the next WP set in step S15 and the destination 74, and judges whether the calculated distance is within the distance d (step S16). If the distance is within the distance d (step S16; Yes), a route to the vicinity of the destination 74 that satisfies the constraints of equations (2) and (3) and minimizes the energy consumption is planned, and the route planning is terminated. If the calculated distance exceeds the distance d (step S16; No), the next WP set in step S15 is replaced with the current WP (step S17). Then, the route planning unit 13 repeats the process from step S11 for the replaced current WP. In this way, the route planning unit 13 sets WPs one after another and calculates the optimal speed between them (the speed that satisfies equations (2) and (3)). By connecting each set WP, it is possible to calculate a route from the initial position to the destination that satisfies equation (1) and the speed between each WP.

[0034] As explained using the flowchart of FIG. 6, the route planning unit 13 generates linear local route candidates from the current WP toward a predetermined range for the optimization problem with constraints formulated by formulas (1) to (3) in FIG. 5, and generates a multi-objective optimal route from near the end of each local route candidate to the destination. Then, the route planning unit 13 connects the local route from the current WP and the global route from the local route end position to the destination to generate multiple candidate routes from the current position to the destination, obtains the optimal speed for moving on each route, and calculates a combination of the route and speed that minimizes the evaluation function while satisfying the constraints as the optimal route and speed. By generating a route to the destination that connects the local route and the global route, it is possible to consider the total travel time and energy amount to the destination as constraints rather than local ones when optimizing the speed. The route planning process explained in FIG. 2 and FIG. 6 can be used not only when planning a route in advance, but also as a route replanning method during movement.

[0035] As described above, according to this embodiment, a multi-objective optimal route for multiple evaluation indexes is calculated prior to optimal route planning. This makes it possible to analyze the trade-off relationship between multiple conflicting evaluation indexes. Then, the analysis is used for decision making, and the index to be optimized (minimized, maximized, approached to a target value, etc.) among the multiple evaluation indexes is the objective, and for the other indexes, the operator sets constraints by looking at the balance of each evaluation index in the Pareto optimal route, so that the route planning problem can be reduced to a constrained optimization problem. In addition, by appropriately setting the objective function and constraint conditions and solving the formulated constrained optimization problem, a route that meets the requirements more than the Pareto optimal solution searched for by the multi-objective optimal route can be obtained. Note that if an appropriate objective function can be set according to the evaluation index, it is possible to deal with combinations of objectives and constraints of various evaluation indexes. For example, an evaluation function that adds constraints on travel time and travel distance and obtains the speed when energy consumption is minimized (maximized or as close as possible to a predetermined target value, etc.) or an evaluation function that obtains the speed between each WP that minimizes the overall travel time after imposing constraints on energy consumption may be set. In this manner, according to this embodiment, it is possible to flexibly respond to various requirements placed on the travel route and plan the optimal route and speed.

[0036] FIG. 8 is a diagram illustrating an example of a hardware configuration of the route planning device according to the embodiment. The computer 900 includes a CPU 901 , a main memory device 902 , an auxiliary memory device 903 , an input / output interface 904 , and a communication interface 905 . The above-described route planning device 10 is implemented in a computer 900. Each of the above-described functions is stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads the program from the auxiliary storage device 903, loads it in the main storage device 902, and executes the above-described processing in accordance with the program. The CPU 901 also reserves a storage area in the main storage device 902 in accordance with the program. The CPU 901 also reserves a storage area in the auxiliary storage device 903 for storing data being processed in accordance with the program.

[0037] In addition, a program for realizing all or part of the functions of the route planning device 10 may be recorded in a computer-readable recording medium, and the program recorded in the recording medium may be read into a computer system and executed to perform processing by each functional unit. The term "computer system" as used herein includes hardware such as an OS and peripheral devices. In addition, if a WWW system is used, the term "computer system" also includes a homepage providing environment (or display environment). In addition, the term "computer-readable recording medium" refers to a portable medium such as a CD, DVD, or USB, and a storage device such as a hard disk built into a computer system. In addition, when the program is distributed to the computer 900 through a communication line, the computer 900 that receives the program may deploy the program in the main storage device 902 and execute the above processing. In addition, the above program may be for realizing part of the above-mentioned functions, and may further be capable of realizing the above-mentioned functions in combination with a program already recorded in the computer system.

[0038] As described above, several embodiments according to the present disclosure have been described, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and gist of the invention.

[0039] <Additional Notes> The route planning method, route planning device, and program described in each embodiment can be understood, for example, as follows.

[0040] (1) A route planning method according to a first aspect (FIG. 2) is a route planning method executed by a computer 900 (route planning device 10), and includes the steps of: calculating a plurality of movement routes of a moving body 1 from an initial position to a destination; selecting from the plurality of movement routes a multi-objective optimal solution for achieving movement-related requirements that are in a trade-off relationship with each other (S3); setting an optimization objective and constraint conditions for formulating a route plan of the moving body as an optimization problem, with reference to the degree of achievement of the requirements in the movement route selected as the multi-objective optimal solution (S4, S5, S6); and simultaneously planning a movement route and an optimal speed of the moving body by solving the optimization problem (S6).

[0041] According to this embodiment, multiple evaluation indexes to be considered in a path planning problem are classified into objectives and constraints, and are reduced to a constrained optimization problem. For this purpose, first, a multi-objective optimal solution regarding multiple evaluation indexes is selected from multiple paths. This makes it possible to clarify the trade-off relationship between the multiple evaluation indexes, thereby supporting the operator in setting objectives and constraints. Next, the objectives and constraints of the optimization problem are set with reference to the evaluation indexes of the multi-objective optimal solution, and the optimization problem is solved to plan an optimal path. This makes it possible to flexibly plan a movement path that optimizes at least one of the requirements and satisfies the desired constraints for the other requirements when there are multiple conflicting requirements regarding the movement of a moving object.

[0042] (2) A route planning method according to a second aspect is the route planning method of (1), in which the requirements are multiple items among travel time, energy required for travel, travel speed, and travel distance. In route planning for a moving object, restrictions are often placed on travel time, energy required for travel, travel speed, and travel distance. According to the second aspect, a route plan can be created by referring to a multi-objective optimal route that takes these requirements into consideration.

[0043] (3) A route planning method according to a third aspect is a route planning method according to (1) to (2), wherein the objective is to minimize, maximize, or bring at least one of the requirements closer to a predetermined target value, and the constraint condition is a constraint on more than one of the requirements. By flexibly combining multiple requirements to set constraints, selecting the most important requirements, and setting appropriate objectives, it is possible to flexibly plan routes optimized for various objectives.

[0044] (4) A route planning method according to a fourth aspect is a route planning method according to (1) to (2), wherein the objective is to calculate a travel speed that minimizes the energy required for travel, and the constraint conditions are a constraint on a range of the travel speed and a constraint on the travel time. This makes it possible to find not only the route that minimizes energy consumption while satisfying the constraints of travel time and travel speed to the destination, but also the optimal speed. By finding the route and the optimal speed for each route, it is possible to deal with evaluation functions that change depending on both route changes and speed changes.

[0045] (5) A route planning method according to a fifth aspect is a route planning method according to any one of (1) to (4), wherein in the step of planning the travel route, a plurality of local routes are calculated for a predetermined range starting from a position of the moving body at a certain time point, and a plurality of global routes are generated from near an end of the local route to the destination. When assuming movement along the local route and the global route, the local route and the global route are selected when the optimization objective is at its best value, the end of the selected local route is set as a target position of the moving body's destination, and the speed to the end of the local route is set as the speed to the next destination. This process is repeatedly executed while replacing the set target with the position at the certain time point, until the difference between the target position and the destination falls within a predetermined range. This makes it possible to generate not only local routes but also multiple candidate routes from the current position to the destination, and to perform route planning that takes into account the total travel time to the destination and the energy consumption.

[0046] (6) A route planning method according to a sixth aspect is a route planning method according to (5), wherein the global route is a multi-objective optimal solution for achieving multiple requirements, selected from multiple routes from near the end of the local route to the destination. By limiting the subjects of consideration to multi-objective optimal routes, it is possible to suppress the increase in the amount of calculations required for route planning. In addition, by selecting a multi-objective optimal route, it is possible to thoroughly consider optimal routes for multiple requirements, which allows efficient route planning while maintaining accuracy.

[0047] (7) A path planning device according to a seventh aspect includes a means for calculating a plurality of movement paths of a moving object from an initial position to a destination, and selecting from the plurality of movement paths a multi-objective optimal solution when the objective is to achieve movement-related requirements that are in a trade-off relationship with each other, a means for setting an optimization objective and constraint conditions for formulating a path plan of the moving object as an optimization problem, with reference to a degree of achievement of the requirements in the movement path selected as the multi-objective optimal solution, and a means for solving the optimization problem, and simultaneously planning a path and an optimal speed of the moving object.

[0048] (8) A program according to an eighth aspect causes a computer to execute the steps of: calculating a plurality of movement paths of a moving object from an initial position to a destination; and selecting, from among the plurality of movement paths, a multi-objective optimal solution for achieving movement-related requirements that are in a trade-off relationship with each other; setting an optimization objective and constraint conditions for formulating a path plan of the moving object as an optimization problem, based on a degree of achievement of the requirements in the movement path selected as the multi-objective optimal solution; and solving the optimization problem, and simultaneously planning the path and optimal speed of the moving object. [Explanation of symbols]

[0049] 10 Path planning device, 11 Input information acquisition unit, 12 Pre-path calculation unit, 13 Path planning unit, 14 Memory unit, 15 Output unit, 1 Mobile object, 900 Computer, 901 CPU, 902 Main memory unit, 903 Auxiliary memory unit, 904 Input / output interface, 905 Communication interface

Claims

1. 1. A computer-implemented method for path planning, comprising: A step of calculating a plurality of movement paths of the moving object from an initial position to a destination, and selecting a multi-objective optimal solution from among the plurality of movement paths when the objective is to achieve movement-related requirements that are in a trade-off relationship with each other; receiving an optimization objective and constraints for formulating, as an optimization problem, a path plan of the moving object set by an operator with reference to a degree of achievement of the requirements in the moving path selected as the multi-objective optimal solution, setting the received optimization objective as an objective function of the optimization problem, and setting the received constraints as constraint conditions of the optimization problem; planning a moving path and a moving speed of the moving object by solving the optimization problem; The path planning method includes:

2. The requirements are a combination of travel time, energy required for travel, travel speed, and travel distance. The method of claim 1 .

3. The objective of the optimization is to minimize, maximize, or approximate to a predetermined target value at least one of the requirements; The constraint is a constraint on a plurality of the requirements. The route planning method according to claim 1 or 2.

4. The objective of the optimization is to calculate a moving speed that minimizes the energy required for moving, The constraints are a constraint on the range of the moving speed and a constraint on the moving time. The route planning method according to claim 1 or 2.

5. In the step of planning the travel route, a process of calculating a plurality of local routes within a predetermined range starting from a position of the moving body at a certain time point, generating a plurality of global routes from near the ends of the local routes to the destination, selecting a combination of the local route and the global route that will achieve the best value for the optimization objective when it is assumed that the moving body moves along the local route and the global route, setting the end of the selected local route as a target position for the next moving destination of the moving body, and setting the speed to the end of the local route as the speed to the next moving destination, while replacing the set target position with the position at the certain time point, repeatedly executing this process until the difference between the target position and the destination falls within a predetermined range; The route planning method according to any one of claims 1 to 4.

6. The global route is a multi-objective optimal solution selected from among a plurality of routes from near the end of the local route to the destination when the objective is to achieve a plurality of the requirements. The method of claim 5 .

7. A means for calculating a plurality of movement paths of a moving object from an initial position to a destination and selecting a multi-objective optimal solution from among the plurality of movement paths when the objective is to achieve movement-related requirements that are in a trade-off relationship with each other; a means for accepting an optimization objective and constraints for formulating, as an optimization problem, a path plan of the moving object set by an operator with reference to a degree of achievement of the requirements in the moving path selected as the multi-objective optimal solution, setting the accepted optimization objective as an objective function of the optimization problem, and setting the accepted constraints as constraint conditions of the optimization problem; means for solving the optimization problem to plan a route and a speed of the moving object; A path planning device having the above configuration.

8. On the computer, A step of calculating a plurality of movement paths of the moving object from an initial position to a destination, and selecting a multi-objective optimal solution from among the plurality of movement paths when the objective is to achieve movement-related requirements that are in a trade-off relationship with each other; receiving an optimization objective and constraints for formulating, as an optimization problem, a path plan of the moving object set by an operator with reference to a degree of achievement of the requirements in the moving path selected as the multi-objective optimal solution, setting the received optimization objective as an objective function of the optimization problem, and setting the received constraints as constraint conditions of the optimization problem; Solving the optimization problem to plan a route and a speed of the moving object; A program that executes the following.

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