CN106054829A - Domestic water carriage service robot system and motion method thereof - Google Patents

Abstract

The present invention discloses a domestic water carriage service robot system and a motion method thereof, belonging to the field of the domestic service robot. The system comprises an instruction receiving device, a master control system, a visual system, a navigation location system, an execution mechanism and an operation device for being remote controlled. The master control system is connected with the instruction receiving device, the visual system and the navigation location system, and the execution mechanism is connected with the navigation location system, wherein the instruction receiving device is configured to receive a control instruction; the master control system is configured to process the control instruction, plan and dispatch a task and control each system; the visual system and the navigation location system are configured for indoor navigation location and the identification and positioning of the task target; and the execution mechanism is configured to grab and dispose the task target. The domestic water carriage service robot system and the motion method thereof can complete a whole water carriage task after receiving a user's water drinking control instruction so as to provide better service for users.

Description

Household water delivery service robot system and its action method

technical field

The invention relates to the field of home service robots, in particular to a home water delivery service robot system and an action method thereof.

Background technique

During the "Twelfth Five-Year Plan" period, China's population aging accelerated, and the population aging situation became more severe, gradually showing three new characteristics of aging, aging, and accelerated development of empty nesters. The acceleration of population aging will inevitably lead to prominent health problems of the elderly population, and the care and nursing of the elderly, disabled and sick elderly should be paid more attention to by the society.

With the continuous advancement of intelligent robot technology in recent years, the home service robot industry has also developed rapidly. At present, all kinds of home service robots have relatively complete functions in the family, but they cannot provide better services for patients or the elderly, nor can they complete a certain task autonomously without human intervention.

Contents of the invention

The technical problem to be solved by the present invention is to provide a household water delivery service robot system and its action method, which can complete a complete water delivery task after receiving the user's drinking water control instruction.

In order to solve the problems of the technologies described above, the present invention provides technical solutions as follows:

A domestic water delivery service robot system, including an instruction receiving device, a main control system, a vision system, a navigation and positioning system, an executive mechanism, and an operating device for remote control, the main control system is connected to the instruction receiving device, the vision system, and The system is connected with the navigation and positioning system, and the actuator is connected with the navigation and positioning system, wherein:

The instruction accepting device is used for accepting control instructions;

The main control system is used for the processing of the control instructions, the planning and scheduling of tasks, and the control of each system;

The vision system and navigation and positioning system are used for indoor navigation and positioning and identification and positioning of task targets;

The actuator is used for grabbing and placing task objects.

Further, the instruction accepting device has a feedback process after receiving the control instruction, that is, the process of confirming the control instruction.

Further, the main control system has the function of accepting time reservations, and the actuator includes a multi-degree-of-freedom robot arm and a robot arm arranged at the end of the robot arm.

Further, the navigation and positioning system is provided with a radio frequency identification device for initially locating the mission target.

Further, the home water delivery service robot system also includes a power management system and a display device connected to the main control system. The power management system provides energy for each system, motion and actuator. Charging mode; the display device is used to display the control instructions and tasks.

Further, the household water delivery service robot system also includes an automatic water heating and quantitative water taking device, and the automatic water heating and quantitative water taking device includes a water heating device, a cooling device, a water heat preservation device, a weighing device and a laser liquid level measuring device , the cooling device is respectively connected with the water heating device and the water heat preservation device, the weighing device is arranged below the place where the water cup is placed, and the laser liquid level measuring device is arranged at the water outlet.

The action method of the above-mentioned household water delivery service robot system includes:

Step 1: The instruction accepting device receives the user's drinking instruction from the operating device, and completes the confirmation of the drinking instruction through a feedback link;

Step 2: The main control system processes the water drinking instruction, and completes the planning of drinking water tasks;

Step 3: The navigation and positioning system navigates and locates the water cup and the automatic water heating and quantitative water taking device, and then the main control system completes the tasks between the home water delivery service robot and the water cup and between the water cup and the automatic water boiling and quantitative water taking device. Path planning between water intake devices;

Step 4: After the radio frequency identification device detects the initial positioning of the water cup, the vision system further performs identification and positioning of the water cup;

Step 5: According to the planned drinking water task, the path between the home water delivery service robot and the water cup, the water cup and the automatic water boiling and quantitative water taking device, and the further identification and positioning of the water cup by the vision system, the execution Under the control of the main control system, the mechanism grabs the water cup and places the water cup at the water outlet of the automatic water boiling and quantitative water taking device to complete the precise and quantitative water taking;

Step 6: After fetching water, the navigation and positioning system navigates and locates the path between the home water delivery service robot and the user, and then the main control system completes the path planning between the home water delivery service robot and the user, and the home water delivery service robot The service robot delivers water to the user under the control of the main control system;

Step 7: After the drinking water task is completed, the home water delivery service robot places the water cup at the original position under the control of the main control system, and completes a control instruction.

Further, the path planning in step 3 and step 6 includes:

Step a: the vision system collects indoor environment information in real time and performs preprocessing;

Step b: using an edge detection algorithm to determine the edges of obstacles and safe areas on the preprocessed indoor environment information;

Step c: using the principle of edge extension to determine the feature points of obstacles in the visual image;

Step d: Use the improved repulsion field model to determine the safe area for the home water delivery service robot to drive.

Further, step a includes:

Step a1: Convert the collected indoor environment information into an HSV image:

Let: Max=max(R, G, B); Min=min(R, G, B);

When Max≠Min:

When Max=Min, that is when R=G=B,

$\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 255</span>}\end{array}\right.<span\; class="notranslate">\; ;</span>$

Step a2: Then use median filtering to remove image noise:

$\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span>& <span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; T</span>}\\ \mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span>& \mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; e</span>}\end{array}\right.$

in, is the output corresponding to w(j, k) after filtering, w(j, k) is the gray value of the image, A represents the point set of image pixels, L represents the number of point set A, T is the threshold value, w(x, y) represents the pixel value corresponding to any point set (x, y) in A.

Further, the improved repulsion field model is:

${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}& \mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\end{array}\right.$

Wherein: U _rep (q) is the repulsion field function, _{k r} is the repulsion field gain coefficient, ρ is the distance from the household water delivery service robot to the obstacle feature point in the visual image, and ρ is the influence of the repulsion field of the indoor obstacle scope.

The present invention has the following beneficial effects:

Compared with the prior art, the instruction receiving device of the household water delivery service robot system and its action method of the present invention can accurately receive the task control instruction sent by the user through the operating device, and process the control instruction through the main control system, Task planning and scheduling, navigation and positioning system and vision system can realize autonomous indoor navigation and positioning, find the task target, and the executive agency can realize the capture and return of the task target under the control of the main control system, which effectively solves the current problem. The needs of household water delivery services can better serve users, etc.

Description of drawings

Fig. 1 is the schematic diagram of the household water delivery service robot system of the present invention;

Fig. 2 is the schematic diagram of the automatic water heating and quantitative water intake device of the household water delivery service robot system of the present invention;

3 is a schematic diagram of the action method of the household water delivery service robot system of the present invention;

detailed description

In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

On the one hand, the present invention provides a household water delivery service robot system 1, as shown in Fig. The remote-controlled operating device 11 and the main control system 13 are respectively connected with the command receiving device 12, the vision system 14 and the navigation and positioning system 15, and the actuator 17 is connected with the navigation and positioning system 15, wherein:

The instruction accepting device 12 is used for accepting the control instruction;

The main control system 13 is used for the processing of control instructions, the planning and scheduling of tasks, and the control of various systems;

Vision system 14 and navigation and positioning system 15 are used for indoor navigation and positioning and identification and positioning of task targets;

The actuator 17 is used for grabbing and placing task objects.

The instruction receiving device of the household water delivery service robot system and its action method of the present invention can accurately receive the task control instructions sent by the user through the operating device, and process the control instructions, plan and dispatch tasks, and navigate through the main control system. The positioning system and vision system can realize autonomous indoor navigation and positioning, search for task targets, and the executive agency can realize the capture and return of task targets under the control of the main control system, effectively solving the current needs of household water delivery services, and can Better service users, etc.

Further, the instruction accepting device 12 can accept various control instructions, for example, it can accept control instructions issued by a brain-computer interface system, voice, gesture, etc.

Preferably, the instruction accepting device 12 may have a feedback process after receiving the control instruction 19, that is, a process of confirming the control instruction. The confirmation of the control command is a task confirmation link for the household water delivery service robot to ensure the accuracy of the task. To confirm, after the accepted control instructions and tasks are displayed on the display device 18, the user's "squint" to the left can indicate that the household water delivery service robot recognizes the task correctly, confirm; The service robot recognizes the wrong task and cancels it; when the user recognizes the control command by voice, the household water delivery service robot accepts the voice task and repeats the task for confirmation. Confirmation can be made through the voice prompt "Correct"; if the recognition task is wrong, the user can cancel it through the display device 18, or through the voice prompt "Cancel".

As an improvement of the present invention, the main control system 13 preferably has the function of accepting time reservations, and the actuator 17 may include a multi-degree-of-freedom robot arm and a robot arm disposed at the end of the robot arm. The time reservation function of the main control system 13 can regularly provide or remind the user of hot water or medicine, so as to prevent the user from forgetting to drink water or take medicine on time;

Further, the indoor navigation and positioning system 15 may be provided with a radio frequency identification device 16 for initially locating the task target. The indoor navigation and positioning system 15 can perform indoor navigation with the assistance of the vision system 14, and the radio frequency identification device 16 can realize the identification and positioning of the task target by the household water delivery service robot system 1, as well as accurate visual identification and positioning.

Preferably, the home water delivery service robot system 1 can also include a power management system (not shown) and a display device 18 connected to the main control system 13, the power management system can provide energy for each system, movement and actuator 17, and the power management system The system is in an autonomous charging mode; the display device 18 is used to display control instructions and mission objectives. The power management system has low-voltage protection, alarm and other functions. Low-voltage protection can be performed when low voltage is low, and the alarm can be used to indicate that the battery is too low, stop the task of the home water delivery service robot, and walk to the "charging room" for autonomous charging to ensure home water delivery service. The robot stops and cannot move indoors; the display device 18 can assist in feedback of control commands and confirmation of tasks.

As another improvement of the present invention, the household water delivery service robot system 1 can also include an automatic water heating and quantitative water taking device 19, and the automatic water heating and quantitative water taking device 19 can include a water heating device 191, a cooling device 192, and a water heat preservation device 193 , weighing device 194 and laser liquid level measuring device 195, cooling device 192 is respectively connected with water heating device 191 and water heat preservation device 193, weighing device 194 is arranged below the place where the water cup is placed, and laser liquid level measuring device 195 is arranged at the outlet At the mouth of the water.

The water heating device 191 automatically turns on the function of boiling water when the water temperature is lower than the set temperature or when cold water is automatically injected after the hot water is taken out. The water supplied by the device 193 is always boiled water; the cooling device 192 is mainly used to cool down the boiling water just injected, and when hot water is injected into the water heat preservation device 193, the cooling device 192 is automatically turned on to cool the hot water to the set temperature , when the water in the water heat preservation device 193 is lower than the set temperature, the heat preservation function is automatically turned on to ensure that the water is always at a suitable temperature when taking water, so that when the home water delivery service robot is taking water, it is guaranteed that the water taken out is at a constant temperature and is suitable Yes, you can drink it directly.

The water cup adopts a water cup of uniform weight and shape to ensure that the weight and height of the water cup are consistent with the distance from the laser liquid level measuring device 195; the weighing device 194 is used to measure the weight of the water injected into the water cup to ensure that the weight of each injection of water is equal , to prevent water from overflowing; weighing device 194 and laser liquid level measuring device 195 both prevent water from overflowing, and have the same priority, that is, as long as one of weighing device 194 and laser liquid level measuring device 195 reaches the set value first, both Turn off the water filling function and stop water filling to prevent water from overflowing.

On the other hand, an action method for the household water delivery service robot system 1 is provided, including:

Step 1: The command receiving device 12 receives the drinking water command 21 issued by the user from the operating device 11, and completes the confirmation of the drinking water command through the feedback link, ensuring the accuracy of the received drinking water command;

Step 2: The main control system 13 processes the drinking water command 21 and completes the planning of the drinking water task. planning of water missions;

Step 3: The navigation and positioning system 15 and the vision system 14 navigate and locate the water cup and the automatic water heating and quantitative water taking device 19, and then the main control system 13 completes the home water delivery service robot and the water cup, as well as the water cup and the automatic water boiling and quantitative water taking. Path planning between devices 19 to ensure that the home water delivery service robot can accurately judge the position of the water cup and the automatic water boiling and quantitative water taking device 19;

Step 4: After the radio frequency identification detection device 16 detects the initial positioning of the water cup, the visual system 14 further performs identification and positioning of the water cup. The recognition and positioning of the water cup in this step is for the actuator 17 to accurately grasp the water cup;

Step 5: According to the planned drinking water task, the path between the home water delivery service robot and the water cup, the water cup and the automatic water boiling and quantitative water taking device 19, and the further identification and positioning of the water cup by the vision system 14, the actuator 17 is in the main Grab the water cup under the control of the control system 13 and place the water cup at the water outlet of the automatic water boiling and quantitative water taking device 19 to complete the accurate and quantitative water taking;

Step 6: After fetching water, the navigation and positioning system 15 navigates and locates the path between the household water delivery service robot and the user, and completes the path planning between the household water delivery service robot and the user. The water is sent to the user under the control of the control system 13;

Step 7: After the task of drinking water is completed, the home water delivery service robot places the water cup at the original position under the control of the main control system 13 to complete a control instruction.

After the user uses the operating device 11 to issue a drinking water task command, the various systems and actuators of the household water delivery service robot system coordinate with each other to jointly complete the water delivery task. The home water delivery service robot system can also realize functions such as regular delivery of medicines.

Preferably, the path planning in steps 3 and 6 may include:

Step a: the vision system 14 collects the indoor environment information in real time and performs preprocessing;

Step b: Using the edge detection algorithm to determine the edge of the obstacle and the safe area on the preprocessed indoor environment information, the edge detection algorithm can detect the edge curve of the obstacle, and can distinguish the obstacle from the safe area;

Step c: using the principle of edge extension to determine the feature points of obstacles in the visual image;

Step d: Use the improved repulsive force field model to determine the safe area for the home water delivery service robot to drive. The improved repulsive force field model can solve the problems of local minima and unreachable target points in the artificial potential field method, and ensure the home water delivery service The accuracy of the safe area where the robot travels.

Further, step a may also include:

Step a1: Convert the collected indoor environment information into an HSV image:

Let: Max=max(R, G, B); Min=min(R, G, B);

When Max≠Min:

When Max=Min, that is when R=G=B,

$\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 255</span>}\end{array}\right.<span\; class="notranslate">\; ;</span>$

Converting the collected indoor environment information into an HSV image can make it easier to process and adjust its saturation and brightness.

Step a2: Then use median filtering to remove image noise:

$\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span>& <span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; T</span>}\\ \mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span>& \mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; e</span>}\end{array}\right.$

in, is the output corresponding to w(j, k) after filtering, w(j, k) is the gray value of the image, A represents the point set of image pixels, L represents the number of point set A, T is the threshold value, w(x, y) represents the pixel value corresponding to any point set (x, y) in A. The median filtering method is simple in operation and fast in speed. It can protect the edge of the image well and restore the image well while removing the image noise.

Preferably, the improved repulsion field model is:

${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}& \mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\end{array}\right.$

Wherein: U _rep (q) is the repulsion field function, _{k r} is the repulsion field gain coefficient, ρ is the distance from the household water delivery service robot to the obstacle feature point in the visual image, and ρ is the influence of the repulsion field of the indoor obstacle scope.

The above description is a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications can also be made. It should be regarded as the protection scope of the present invention.

Claims (10)

1. A household water delivery service robot system, characterized in that it comprises an instruction accepting device, a main control system, a vision system, a navigation and positioning system, an executive mechanism and an operating device for remote control, and the main control system is connected with the said main control system respectively. The instruction accepting device, the vision system and the navigation and positioning system are connected, and the actuator is connected with the navigation and positioning system, wherein: The instruction accepting device is used for accepting control instructions; The main control system is used for the processing of the control instructions, the planning and scheduling of tasks, and the control of each system; The vision system and navigation and positioning system are used for indoor navigation and positioning and identification and positioning of task targets; The actuator is used for grabbing and placing task objects. 2 . The household water delivery service robot system according to claim 1 , wherein the instruction receiving device has a feedback process after receiving the control instruction, that is, a process of confirming the control instruction. 3. The household water delivery service robot system according to claim 1, wherein the main control system has the function of accepting time reservations, and the actuator includes a multi-degree-of-freedom mechanical arm and is arranged at the end of the mechanical arm manipulator. 4 . The household water delivery service robot system according to claim 1 , wherein a radio frequency identification device for initially locating the task target is arranged in the navigation and positioning system. 5. The household water delivery service robot system according to any one of claims 1 to 4, characterized in that, the household water delivery service robot system further comprises a power management system and a display device connected to the main control system, the The power management system provides energy for each system, motion and actuator, and the power management system adopts an autonomous charging mode; the display device is used to display the control instructions and tasks. 6. The household water delivery service robot system according to claim 5, characterized in that, the household water delivery service robot system also includes an automatic water heating and quantitative water intake device, and the automatic water heating and quantitative water intake device includes a water heating device, Cooling device, water heat preservation device, weighing device and laser liquid level measuring device, the cooling device is respectively connected with the water heating device and the water heat preservation device, the weighing device is arranged below the place where the water cup is placed, the laser liquid level The measuring device is arranged at the water outlet. 7. The action method of the household water delivery service robot system according to claim 6, characterized in that it comprises: Step 1: The instruction accepting device receives the user's drinking instruction from the operating device, and completes the confirmation of the drinking instruction through a feedback link; Step 2: The main control system processes the water drinking instruction, and completes the planning of drinking water tasks; Step 3: The navigation and positioning system navigates and locates the water cup and the automatic water heating and quantitative water taking device, and then the main control system completes the tasks between the home water delivery service robot and the water cup and between the water cup and the automatic water boiling and quantitative water taking device. Path planning between water intake devices; Step 4: After the radio frequency identification device detects the initial positioning of the water cup, the vision system further performs identification and positioning of the water cup; Step 5: According to the planned drinking water task, the path between the home water delivery service robot and the water cup, the water cup and the automatic water boiling and quantitative water taking device, and the further identification and positioning of the water cup by the vision system, the execution Under the control of the main control system, the mechanism grabs the water cup and places the water cup at the water outlet of the automatic water boiling and quantitative water taking device to complete the precise and quantitative water taking; Step 6: After fetching water, the navigation and positioning system navigates and locates the path between the home water delivery service robot and the user, and then the main control system completes the path planning between the home water delivery service robot and the user, and the home water delivery service robot The service robot delivers water to the user under the control of the main control system; Step 7: After the drinking water task is completed, the home water delivery service robot places the water cup at the original position under the control of the main control system, and completes a control instruction. 8. The action method according to claim 7, characterized in that, the path planning in step 3 and step 6 includes: Step a: the vision system collects indoor environment information in real time and performs preprocessing; Step b: using an edge detection algorithm to determine the edges of obstacles and safe areas on the preprocessed indoor environment information; Step c: using the principle of edge extension to determine the feature points of obstacles in the visual image; Step d: Use the improved repulsion field model to determine the safe area for the home water delivery service robot to drive. 9. The action method according to claim 8, wherein step a comprises: Step a1: Convert the collected indoor environment information into an HSV image: Let: Max=max(R, G, B); Min=min(R, G, B); When Max≠Min: definition: but: in: When Max=Min, that is when R=G=B, $\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 255</span>}\end{array}\right.<span\; class="notranslate">\; ;</span>$ Step a2: Then use median filtering to remove image noise: $\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span>& <span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; A</span>}}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; T</span>}\\ \mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>& \mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; e</span>}\end{array}\right.$ in, is the output corresponding to w(j, k) after filtering, w(j, k) is the gray value of the image, A represents the point set of image pixels, L represents the number of point set A, T is the threshold value, w(x, y) represents the pixel value corresponding to any point set (x, y) in A. 10. The action method according to claim 8, characterized in that, the improved repulsion field model is: ${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}& \mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; \&rho;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}\end{array}\right.$ Wherein: U _rep (q) is the repulsion field function, _{k r} is the repulsion field gain coefficient, ρ is the distance from the household water delivery service robot to the obstacle feature point in the visual image, and ρ is the influence of the repulsion field of the indoor obstacle scope.