Guidance and control system

Abstract

The present invention provides a guidance control system that can guide targets using guided mobility more effectively or efficiently. [Solution] The present invention comprises an autonomously drivable guided mobility vehicle 2, a surrounding monitoring device 3 that detects the surrounding state of the guided mobility vehicle 2, and a controller 4 that controls the operation of the guided mobility vehicle 2 so that the guided object reaches a set target action state. The controller 4 calculates the relationship, including the time change in the position of the guided object and the guided mobility vehicle, based on the detection results of the surrounding monitoring device 3, and sets the control amount of the guided mobility vehicle 2 based on this relationship to achieve the target action state.

Description

[Technical Field]

[0001] This invention relates to a guidance control system for controlling a guided mobility device that guides a target object. [Background technology]

[0002] Recently, with the decline in the working population, the demand for autonomous mobility has increased, and development is underway regarding the control of mobility. As a prior art, for example, Japanese Patent Publication No. 2009-123045 discloses a device that calculates the shape of the dangerous area that arises around a mobile robot as a result of the robot's movement and displays it visually. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2009-123045 [Overview of the project] [Problems that the invention aims to solve]

[0004] However, conventional technology does not take into account the relationship between the movement of the mobile robot and the movement of the object being guided (such as a person). Therefore, there is room for improvement in conventional control devices in terms of effective or efficient guidance according to the movement of the object being guided.

[0005] The object of the present invention is to provide a guidance control system that can guide an object using a guided mobility device more effectively or efficiently. [Means for solving the problem]

[0006] The guidance control system of the present invention comprises an autonomously drivable guided mobility device, a surrounding monitoring device for detecting the surrounding state of the guided mobility device, and a controller for controlling the operation of the guided mobility device so that a guided target, which is a person, animal, or other autonomously drivable mobility device, reaches a set target behavior state. The controller calculates a relationship, including the time change in the position of the guided target and the guided mobility device, based on the detection results of the surrounding monitoring device. Based on the relationship, the controller sets the control amount of the guided mobility device to achieve the target behavior state. [Effects of the Invention]

[0007] According to the present invention, the guided mobility is controlled based on a relationship that includes changes in the positional relationship between the guided mobility and the guided object. By considering this relationship, the controller can cause the guided mobility to perform actions that are effective or efficient in relation to the guidance state of the target. [Brief explanation of the drawing]

[0008] [Figure 1] This is a diagram (conceptual bottom view) of the induction control system of this embodiment. [Figure 2] This is a conceptual diagram of the guided mobility of this embodiment. [Figure 3] This is a flowchart illustrating an example of the control flow in this embodiment. [Figure 4] This is an explanatory diagram illustrating the impact of this embodiment. [Figure 5] This is an explanatory diagram illustrating the definition of ΔTTCP in this embodiment. [Figure 6] This is an explanatory diagram illustrating the relationship between the degree of influence (controllability index) and ΔTTCP in this embodiment. [Modes for carrying out the invention]

[0009] Hereinafter, an induction control system 1, which is one embodiment of the present invention, will be described in detail with reference to the figures. In addition to the embodiments described below, the present invention can be implemented in various forms with various modifications and improvements based on the knowledge of those skilled in the art.

[0010] As shown in Figure 1, the guidance control system 1 of this embodiment comprises a guided mobility 2, a surrounding monitoring device 3, and a controller 4. The guided mobility 2 is an autonomously drivable mobility device. The means of movement for the guided mobility 2 may be, for example, wheels, propellers, caterpillars, or robotic legs. In other words, the guided mobility 2 may be, for example, a vehicle-type robot that moves by wheels (e.g., a vehicle), a flying robot that moves by propellers (e.g., a drone), an animal-type robot (e.g., a quadruped robot), or a humanoid robot (e.g., a bipedal robot), etc.

[0011] The guided mobility 2 of this embodiment is a vehicle-type robot comprising a main body 21, three wheels 22, 23, and 24, drive actuators 25 and 26 for driving the front wheels 22 and 23, a brake actuator 27, and a position detection device 28. The main body 21 may be provided with arms, a head, and / or a display screen, etc.

[0012] The main body 21 is the housing portion of the inductive mobility 2. The main body 21 is equipped with wheels 22-24, drive actuators 25, 26, brake actuator 27, position detection device 28, surrounding monitoring device 3, and controller 4. The front wheels 22 and 23 are drive wheels and are configured to be independently driveable by their respective drive actuators 25 and 26. The drive actuators 25 and 26 are electric motors (e.g., in-wheel motors).

[0013] The brake actuator 27 is a device provided on each of the front wheels 22 and 23, and applies frictional braking force independently to each of the front wheels 22 and 23. The controller 4 can generate, as braking force, regenerative braking force by the drive actuators 25 and 26 and frictional braking force by the brake actuator 27.

[0014] The rear wheel 24 is a driven wheel, and is arranged on a virtual straight line passing through the midpoint (the middle position of the tread) between the front wheels 22 and 23 and extending in the front-rear direction. The rear wheel 24 is configured to be freely steerable. The induction mobility 2 can perform left-right turning and on-the-spot turning (for example, even ultra-on-the-spot turning) by changing the rotation speed ratio of the front wheels 22 and 23.

[0015] The position detection device 28 is a device that detects the position of the induction mobility 2. The position detection device 28 is, for example, an antenna and a receiver of GNSS (Global Navigation Satellite System). The position information of the induction mobility 2 is transmitted to the controller 4. Note that the position detection device 28 may be a device using a beacon. Further, the induction mobility 2 may include a steering actuator that steers the front wheels 22 and 23 and / or the rear wheel 24 based on the control of the controller 4.

[0016] The peripheral monitoring device 3 is a device that detects the peripheral state of the induction mobility 2. The peripheral monitoring device 3 is composed of, for example, a LiDAR (Light Detection and Ranging, or Laser Imaging Detection and Ranging), a radar, and / or a camera, etc. The peripheral monitoring device 3 can also be said to be a device that detects information on objects around the induction mobility 2. The peripheral monitoring device 3 of the present embodiment includes a plurality of LiDARs that sense different directions.

[0017] The controller 4 is a computer or an electronic control unit (ECU) having one or more processors and one or more memories. The controller 4 controls each of the actuators 25, 26, and 27 of the induction mobility 2. The controller 4 has the function of an autonomous driving ECU (automatic driving ECU) that executes the autonomous driving of the induction mobility 2. The controller 4 calculates the target route of the induction mobility 2 based on map data, the destination of the induction mobility 2, the position (current location) of the induction mobility 2, and the like. Further, the controller 4 can finely adjust the target route, such as avoiding obstacles, based on the detection results of the peripheral monitoring device 3 and the map data.

[0018] The controller 4 executes normal control when no induction target is detected, and executes induction control when an induction target is detected. The normal control is a control for autonomous driving at a moving speed based on a predetermined rule along the target route. The induction control is a control that makes changes to the operation of the normal control as necessary.

[0019] In the induction control, the controller 4 mainly executes a relational operation process, an action determination process, an influence degree operation process, and a control amount operation process. In other words, as shown in FIG. 2, the induction control system 1 includes a relational operation unit 41 that executes a relational operation process, an action determination unit 42 that executes an action determination process, an influence degree operation unit 43 that executes an influence degree operation process, and a control amount operation unit 44 that executes a control amount operation process.

[0020] (Relational operation process) The controller 4 controls the operation of the induction mobility 2 so that an induction target, which is a person, an animal, or another autonomously drivable mobility, reaches the set target action state. That is, the controller 4 has an induction function in addition to the autonomous driving function. The induction target is set in the controller 4 in advance. In the controller 4 of the present embodiment, a person (pedestrian) is set as the induction target. The controller 4 recognizes the detected person as the induction target based on, for example, the detection results of the peripheral monitoring device 3.

[0021] The controller 4 calculates the position, speed, and direction of travel of the guided object based on the detection results of the surrounding monitoring device 3. For example, the controller 4 identifies the guided object from the detection results of the surrounding monitoring device 3 based on the registered shape information (feature information) of the guided object and acquires the position of the guided object as time-series data. The controller 4 can acquire its own position information from the position detection device 28. The controller 4 can determine the speed of travel of the guided mobility 2 based on the control values ​​of each actuator 22, 23, and 27. The controller 4 can calculate the relative position and relative speed of the guided object with respect to the guided mobility 2. The controller 4 can recognize the direction of travel of the guided object and the direction of travel of the guided mobility 2.

[0022] In this way, the controller 4 calculates a relationship that includes the time change in the positions of the guided object and the guided mobility 2 (which can also be described as the time change in the relative position of the guided object with respect to the guided mobility 2) based on the detection results of the surrounding monitoring device. The relationship (relationship information) in this embodiment includes the relative position and relative velocity of the guided object with respect to the guided mobility 2, the direction of travel of the guided object, and the direction of travel of the guided mobility 2.

[0023] (Action decision processing) Controller 4 has a target behavior state for the guided object set as a guidance target for the guided object. Based on the calculated relationship, Controller 4 determines (estimates) whether the result of the guided object's action matches the target behavior state. Controller 4 can recognize changes in the relative position between the guided object and the guided mobility 2 in relation to each other. Controller 4 can predict changes in the relative position over time, for example, by assuming that the relative speed is constant.

[0024] As an example, let's consider a case where the target behavior state is set as follows: "When the guided object and guided mobility 2 are moving in directions that intersect each other (hereinafter also referred to as the 'intersecting movement state'), guided mobility 2 takes the lead over the guided object." In this mobility-leading case, the target behavior state for the guided object is for the person to take action to allow guided mobility 2 to go first in the intersecting movement state. In this case, the target behavior state is achieved by, for example, making the person slow down or stop, or making the person decide to "follow the mobility," thereby allowing guided mobility 2 to go first in the intersecting movement state.

[0025] Controller 4 determines, based on the relationship, whether the current situation is a crossing state. If Controller 4 determines that the current situation is a crossing state, it determines (predicts) which will take the lead at the crossing, based on the relationship, for example, the relative speed between the guided object and guided mobility 2 (hereinafter simply referred to as "both"). If the determination result is "guided object takes the lead," Controller 4 determines that it does not match the target behavior state, and if the determination result is "guided mobility 2 takes the lead," it determines that it matches the target behavior state.

[0026] (Influence calculation process) The controller 4 calculates the degree to which the operation of the guided mobility 2 has an influence on the guided object, based on the relationship. For example, the controller 4 stores predetermined rules (e.g., maps, databases, and / or calculation formulas) that represent the relationship between the relationship and the degree of influence. As an example, the rule is set based on the theory (hypothesis) that "in a crossing-movement state, the greater the distance between the two, the greater the influence (i.e., degree of influence) that changes in the operation of the guided mobility 2 (e.g., changes in speed) have on the operation of the guided object."

[0027] One theory suggests that when a person anticipates crossing paths with a distant mobility object while continuing to move, and then perceives that distant mobility object accelerating, they tend to think in terms of letting the distant mobility object pass first. According to this theory, a person is more likely to decide to follow a mobility object if it accelerates in the distance than if it accelerates in the near mobility object. Based on this theory, rules could be established where the influence of such rules increases with the distance between the two objects in a crossing state.

[0028] As another example, the rules are set based on the theory that "in a crossing motion, the higher the probability of contact between the two, the greater the influence (i.e., degree of influence) of changes in the movement of the guided mobility 2 (e.g., changes in speed) on the movement of the guided object." One theory is that the higher the probability of a person coming into contact with the guided mobility 2 through movement, the more they are influenced by the acceleration of the guided mobility 2 and tend to move ahead of the guided mobility 2. These theories are based on the results of experiments and simulations on the movement of the person being guided. The controller 4 may calculate the degree of influence numerically or at a stepped level (e.g., high, medium, low) based on the relationship and rules.

[0029] Controller 4 is configured to perform an influence calculation process if the result of the action judgment process is "not a match". In other words, if Controller 4 determines that the result of the guided action does not match the target behavior state, it calculates the influence. If Controller 4 determines that they "match", it does not calculate the influence and performs normal control. Note that Controller 4 may calculate the influence regardless of the result of the action judgment process, or regardless of whether the action judgment process is performed or not. The calculated influence is used in the control quantity calculation process.

[0030] (Control variable calculation processing) When the above determination state of "mismatch" is reached, the controller 4 sets the control amount of the guided mobility 2 based on the degree of influence to achieve the target behavioral state. For example, if the degree of influence is below a predetermined value, the amount of change in the control amount is made greater than when the degree of influence is greater than the predetermined value. If the degree of influence is greater than the predetermined value, the amount of change in the control amount is made smaller than when the degree of influence is below the predetermined value. When the degree of influence is high, even a small change in the guided mobility 2 (e.g., acceleration or deceleration) can be effective in guiding the guided object to the target behavioral state.

[0031] In the above example of a mobility-first case, and in a state where it is determined that there is a "mismatch," the controller 4 selects one of the following based on the relationship, degree of impact, and / or safety criteria: "accelerate the guided mobility 2 (acceleration control)," "change the target behavior state (target change control)," and "execute safety control (safety control)."

[0032] Controller 4 selects safety control when the distance to the target is very small (distance < safe distance threshold) or when the probability of contact is very high (probability of contact > safe contact threshold). In safety control, Controller 4 maintains or decreases the control amount without increasing it. It can also be said that Controller 4 changes the target behavior state from "target follows" to "target leads" based on the relationship and safety criteria (various thresholds). Controller 4 may also perform stopping or avoidance as a safety control. Controller 4 determines whether or not to perform safety control based on the relationship and safety criteria.

[0033] Controller 4 performs target change control based on the relationship and degree of influence, thereby changing the target behavior state. If the determination state is "not matching", the relative distance is small, and the degree of influence is below a predetermined threshold, Controller 4 stops control toward achieving the current target behavior state and maintains or reduces the control amount. In other words, Controller 4 changes the target behavior state if the determination state is "not matching" and the degree of influence is below a predetermined threshold. In this case, Controller 4 changes the target behavior state from "guided object is following" to "guided object is leading". When the target behavior state is "guided object is leading", guidance control such as deceleration of guided mobility 2 is performed.

[0034] Controller 4 selects acceleration control if the current situation does not fall under safety control or target change control. In acceleration control, Controller 4 increases the amount of change in the controlled variable (acceleration amount) as the impact is smaller. Controller 4 also checks for safety after acceleration (a state where the possibility of contact between the two is extremely low) and changes the controlled variable within the range where safety is ensured. The amount of change in the controlled variable is set based on the impact and safety.

[0035] Controller 4, in acceleration control, reduces the amount of acceleration as the degree of influence increases. For example, multiple acceleration levels (e.g., small acceleration, large acceleration) may be set, and the amount of acceleration may be determined according to the degree of influence. In this case, if the degree of influence is below the acceleration threshold, the large acceleration may be selected, and if the degree of influence is greater than the acceleration threshold, the small acceleration may be selected. When the distance between the two is large, the degree of influence is large, and the target can be guided to the target behavior state even with a relatively small amount of acceleration.

[0036] If a person (the target of guidance) sees the acceleration of guided mobility 2, which is likely to intersect with them in the future, there is a reasonable possibility that they will change their behavior, for example, by slowing down to allow guided mobility 2 to go ahead. When a person slows down, a change occurs in the relationship between the two, and the result of the action decision processing may also change. In this way, if the result of the action decision processing changes from "does not match" to "matches" due to a change in the controlled quantity (acceleration), the controlled quantity is maintained.

[0037] The controller 4 may store one acceleration amount and, based on its impact, decide whether to perform acceleration control (control amount change control) based on that acceleration amount, or target change control (or safety control). In such a case, the impact amount serves as an indicator for deciding whether to change the control amount to guide the target into the target behavior state, or to change the target behavior state.

[0038] To summarize the control flow related to the calculation of the degree of influence, as shown in Figure 3, the controller 4 calculates the relationship between the guided object and the guided mobility 2 based on the detection results of the surrounding monitoring device 3 (S1). Based on the calculated relationship, the controller 4 predicts the outcome of the guided object's action (S2). The controller 4 determines whether the guided object will perform the desired action, that is, whether the predicted action of the guided object matches the target action state (S3).

[0039] If they match (S3: Yes), controller 4 maintains the controlled quantity (S6). If they do not match (S3: No), controller 4 calculates the degree of influence based on the relationship (S4). Controller 4 changes the controlled quantity of guided mobility 2 based on the degree of influence in order to guide the guided object to the target behavior state (S5). The relationship changes moment by moment, and guidance control is performed based on the relationship at that time.

[0040] According to this embodiment, the control quantity is set based on the relationship between the guided object and the guided mobility 2, and the target behavior state. This allows for more effective or efficient control of the guidance to the target behavior state, taking into account the positional relationship between the two. Specifically, the controller 4 calculates the degree of influence based on the relationship and sets the control quantity based on the degree of influence, thereby enabling the guided mobility 2 to perform more effective guidance actions. According to this embodiment, an effective control quantity for guidance can be set based on the relationship.

[0041] (An example of modeling) In a situation where an AMR (Autonomous Mobile Robot), an example of guided mobility 2, and a person are moving in a way that crosses paths, the decision of whether the person should go ahead or behind the AMR can be modeled as shown in equation (1). In equation (1), B means the person is going behind (Behind), A means the person is going ahead (Ahead), and U means the person is undecided.

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[0042] By performing a Taylor expansion of the model in equation (1) with respect to the controlled variable, the magnitude of the influence the controlled variable has on human judgment (emotion index) can be quantitatively expressed, as shown in equation (2). v1 represents the speed of the AMR. The first term on the right-hand side of equation (2) (the term for v1) represents the "degree of influence (magnitude of influence)". C s It is a constant.

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[0043] The magnitude of the influence (sensitivity index) expressed in the first term on the right-hand side of equation (2) changes based on the relationship between the AMR and the person. This model was determined based on actual human movement and decision-making experiments. In this experiment, two people wearing motion capture devices started simultaneously and walked in a cross-shaped pattern from different starting positions to ending positions, with each pedestrian (Ped.0 and Ped.1) recording their decisions at each stage using their respective control devices (3360 trials in total). The decision options were: leading (A), following (B), and undecided (U). In this experiment, at least the position and decision results of each pedestrian were recorded at predetermined time intervals.

[0044] As an example of experimental results, Figure 4 shows the distribution of the judgment sensitivity index (also known as the degree of influence) for Ped.0's "following" behavior. Ped.1, the influencing Ped.0, is walking along the y-axis from the origin. Although the color differences are not shown in Figure 4, the degree of influence is small in the area close to Ped.1 (bottom left) and increases as you move upwards or to the right.

[0045] As shown in Figure 4, when Ped.1 accelerates from the origin, the probability that Ped.0, which is in region A (the area where the distance between the two is greater than a predetermined value), will judge it as "backward" increases. This is because the walking speed of Ped.1 increases, Δ TTCP This is because the value of increased, and the judgment shifted from "leading" or "undecided" to "following." Δ TTCP It is defined by equations (3), (4), and (5) (see Figure 5). TTCP can be said to be the Time To Crossing Point. The difference between the two crossing times Δ TTCP The smaller the value, the higher the probability of contact between the two.

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[0046] On the other hand, when Ped.0 is in Region B (an area where the distance between the two is less than or equal to a predetermined value), the influence of Ped.1's acceleration is small, and there is a high probability that Ped.0's judgment will not change. In other words, this indicates that when Ped.0 is in Region B, it is difficult to induce (change in judgment) Ped.0 through changes in Ped.1's velocity. The greater the distance between the two, the more difficult it is for a person to influence Δ TTCP It is also thought that this makes it easier to recognize changes.

[0047] Here, even if Ped.1 is replaced by AMR, it is presumed that a similar judgment will be made by Ped.0 (human). Thus, according to FIG. 4, it can be seen that for a person far away, accelerating the AMR is effective in making the person judge that "the AMR goes ahead (the person goes behind)". Also, for a person nearby, it can be seen that the influence due to the change in the operation of the AMR is small and it is difficult to change the judgment.

[0048] Also, the human behavior model based on human judgment can be expressed as in equations (6), (7), and (8). That is, by having the AMR act with a control amount according to the relationship, it is possible to induce human judgment and induce human behavior. P x is the position of the person in the X-axis direction, and P y is the position of the person in the Y-axis direction. v0 is the moving speed of the person, and v1 is the moving speed of the AMR. φ represents the moving direction of the person, and αΔt represents the speed change of the person. It can be said that the subscript "0" in this example is the person and "1" is the AMR.

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[0049] Also, in the above behavior model, the human behavior inductivity (controllability) can be defined as in equations (9), (10), (11), and (12). Equation (9) means taking the first term on the right side of equation (6) as A k and equation (10) means taking the second term on the right side of equation (6) as B kThis means that... Equation (11) represents the controllability Gramian W in a finite-time discrete system. L represents the number of steps (the number of steps required for controllability analysis). Equation (12) represents the evaluation of the magnitude of the eigenvalues ​​of the controllability Gramian W by finding the trace value or determinant of the controllability Gramian W. In other words, the left-hand side J of equation (12) represents the degree of influence of AMR on the human state (behavior) to the input v1. The left-hand side J can also be called the controllability or inducibility index. In equation (12), P represents a person and cg represents the Gramian.

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[0050] As shown in Figure 6, this influence (controllability) J corresponds to Δ, which represents the possibility of contact between humans and AMRs. TTCP The following correlation was confirmed: Δ TTCP The smaller the probability of contact (the greater the likelihood of contact), the greater the influence (controllability) J. In other words, the greater the likelihood of contact, the greater the influence, and the smaller the likelihood of contact, the smaller the influence.

[0051] Controller 4 calculates the degree of influence based on the relationship between the person and the AMP (relative position, relative velocity, direction of movement), and calculates the control variable based on the degree of influence. The above-mentioned rules (calculation formulas) can be applied to the calculation of the degree of influence. However, for control purposes, the above-mentioned calculation formulas may not be used, and "rules regarding relationships and degrees of influence" may be set based on a model, simulation results, or theory.

[0052] Regarding the relationship between relationships and influence, rules (maps, databases, etc.) that simply produce an output (influence, control variable) for an input (relationship) may be set based on theories such as "the greater the distance between the two, the greater the influence (first rule)" and / or "the greater the possibility of contact between the two, the greater the influence (second rule)." The first and second rules may be set independently or together. The overall influence may be "large" when the distance between the two and the possibility of contact are large, "medium" when the distance between the two are large and the possibility of contact is small, "small" when the distance between the two are small and the possibility of contact is large, and "minimal" when the distance between the two are small and the possibility of contact is small.

[0053] Thus, if the controller 4 recognizes a person as a target for guidance based on the detection results of the surrounding monitoring device 3, and recognizes that the guided mobility 2 and the target are moving in directions that intersect each other based on their relationship, the influence may be increased as the distance between the guided mobility 2 and the target increases. Furthermore, if the controller 4 recognizes that the guided mobility 2 and the target are moving in directions that intersect each other based on their relationship, it may calculate the probability of contact corresponding to the possibility of the guided mobility 2 and the target coming into contact, and the influence may be increased as the probability of contact increases.

[0054] (others) The present invention is not limited to the embodiments described above. The control quantities of the guided mobility 2 calculated based on the relationship are not limited to the speed of the guided mobility 2, but may also relate to the direction of movement (orientation), angular velocity, operation of various parts such as arms, and the presentation of a display screen. The controller 4 may change the relationship based on the relationship, for example by changing the direction of movement of the guided mobility 2, flashing or lighting up lights, emitting sounds or voices, activating hands to give signals, announcing "please go ahead" on the screen or by voice, or changing the facial expression of the robot, and perform guidance control toward the target behavior state.

[0055] Furthermore, the target of guidance may be an animal or other mobile object (e.g., an autonomously driven mobility device). If it is an animal, a control amount may be set based on the relationship to determine an effective action for guiding the animal (e.g., a theoretically effective action). If it is another mobile object, a control amount may be set based on the relationship to determine an action amount according to a pre-set action pattern of the other mobile object (e.g., stopping if there is a high probability of contact).

[0056] The guidance control system 1 may include a controller that performs autonomous driving and a controller that causes the guided mobility 2 to perform guidance functions. In addition, at least some of the functions of the controller 4 may be realized by a computer located outside the guided mobility 2 and configured to communicate with the guided mobility 2. [Explanation of Symbols]

[0057] 1... Guidance control system, 2... Guidance mobility, 3... Surroundings monitoring device, 4... Controller.

Claims

1. Autonomous guided mobility, A surrounding monitoring device for detecting the surrounding conditions of the aforementioned guided mobility, A controller that controls the operation of a guided mobility device, such as a person, animal, or other autonomously drivable mobility device, so that the guided device reaches a set target behavioral state. Equipped with, The aforementioned controller, Based on the detection results of the surrounding monitoring device, the relationship including the time change in the position of the guided target and the guided mobility is calculated. Based on the aforementioned relationship, the control amount of the guided mobility is set to achieve the aforementioned target behavioral state. Guidance and control system.

2. The aforementioned controller, Based on the aforementioned relationship, the degree to which the operation of the guided mobility has an effect on the guided object is calculated. Based on the degree of influence, the amount of control of the guided mobility is set to achieve the target behavioral state. The induction control system according to claim 1.

3. The aforementioned controller, Based on the aforementioned relationship, it is determined whether the result of the guided behavior matches the target behavioral state. If it is determined that the result of the behavior of the guided subject does not match the target behavioral state, the degree of influence is calculated. The induction control system according to claim 2.

4. The aforementioned controller, Based on the detection results of the surrounding monitoring device, the person is recognized as the target for guidance. Based on the aforementioned relationship, when it is recognized that the guided mobility and the guided object are moving in directions that intersect each other, the greater the distance between the guided mobility and the guided object, the greater the degree of influence. Based on the aforementioned degree of influence, a control amount related to the speed of the guided mobility is set. The induction control system according to claim 2 or 3.

5. The aforementioned controller, Based on the detection results of the surrounding monitoring device, the person is recognized as the target for guidance. Based on the aforementioned relationship, if it is recognized that the guided mobility and the guided object are moving in directions that intersect each other, the probability of contact corresponding to the possibility of the guided mobility and the guided object coming into contact is calculated, The greater the likelihood of contact, the greater the impact. Based on the aforementioned degree of influence, a control amount related to the speed of the guided mobility is set. The induction control system according to claim 2 or 3.

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