CN112859851B - Multi-legged robot control system and multi-legged robot - Google Patents
Multi-legged robot control system and multi-legged robot Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
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- B62D—MOTOR VEHICLES; TRAILERS
- B62D57/00—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track
- B62D57/02—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track with ground-engaging propulsion means, e.g. walking members
- B62D57/032—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track with ground-engaging propulsion means, e.g. walking members with alternately or sequentially lifted supporting base and legs; with alternately or sequentially lifted feet or skid
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The application relates to a multi-legged robot control system and a multi-legged robot. The multi-legged robot control system includes: the environment detection device is used for detecting road surface data; a control apparatus configured to include a plurality of finite state machines: the task state machine is used for acquiring a task instruction and sending task information according to the task instruction; the task instruction comprises a movement speed instruction and/or a target point instruction; the environment state machine is used for analyzing the road surface environment information according to the road surface data detected by the environment detection device in real time; and the planning state machine is used for generating planning information according to the task information after receiving the task information, wherein the planning information comprises a movement route planned according to the task instruction and a movement gait planned according to the road surface environment information. This polypod robot control system can make polypod robot realize independently steadily moving under different environment or the superimposed complex environment of multiple environment, improves polypod robot's autonomic degree.
Description
Technical Field
The application relates to the technical field of robots, in particular to a multi-legged robot control system and a multi-legged robot.
Background
With the development of the robot technology, the traditional wheeled robot and the tracked robot have large limitations in use scenes, the traditional wheeled robot has poor obstacle crossing capability, poor terrain adaptability and low turning efficiency, or has large turning outer radius, is easy to slip and is not stable enough, so the foot type robot technology appears, the use scenes of the foot type robot are more complicated, and the application field is more extensive. The traditional tracked robot has high requirements on the terrain, cannot be applied to the terrain with large height difference, and is not as flexible and convenient as a foot-type robot. The foot robot can almost adapt to various complex terrains, can cross obstacles, and has good freedom degree, flexible action, free and stable. Legged robots include humanoid biped robots and multi-legged robots that mimic insects or other animals. The multi-legged robot has strong walking capability in an unstructured environment and can run in ground environments such as flat ground, slopes, gravel ground, grasslands, mountainous regions and the like. In order to enable the multi-legged robot to operate in a complex environment, high requirements are placed on intelligent perception, autonomous movement capability and a protection mechanism of the multi-legged robot.
Most of the actions of the conventional multi-legged robot in the operation process of the whole robot need manual control and intervention so as to ensure the stability and safety of operation and have lower degree of autonomy.
Disclosure of Invention
In view of the above, it is desirable to provide a control system for a multi-legged robot and a multi-legged robot capable of improving the degree of autonomy.
A multi-legged robot control system comprising:
the environment detection device is used for detecting road surface data;
a control apparatus configured to include a plurality of finite state machines:
the task state machine is used for acquiring a task instruction and sending task information according to the task instruction; the task instruction comprises a movement speed instruction and/or a target point instruction;
the environment state machine is used for analyzing the road surface environment information according to the road surface data detected by the environment detection device in real time;
and the planning state machine is used for generating planning information according to the task information after receiving the task information, wherein the planning information comprises a movement route planned according to the task instruction and a movement gait planned according to the road surface environment information.
Above-mentioned polypod robot control system and polypod robot, detect the road surface data of polypod robot environment and feed back to controlling means through environment detection device, controlling means is configured to including task state machine, environment state machine and planning state machine, make controlling means can acquire the task instruction, analysis road surface environment information, and according to the task instruction of acquireing and road surface data automatic planning movement route and motion gait, thereby enable polypod robot to realize independently steadily moving under different environment or the superimposed complex environment of multiple environment, improve the autonomic degree of polypod robot.
In one embodiment, the control device comprises a communication module for establishing communication with a remote control device to transmit data; the remote control device is used for sending the task instruction.
The communication module can be used for carrying out bidirectional data transmission with the remote control device and receiving task instructions sent by the remote control device.
In one embodiment, the planning state machine selectable locomotor gait comprises: standing, slow static walking, diagonal jogging, diagonal walking, and diagonal jogging.
In one embodiment, the types of the road surface environment information include a flat ground environment, a slope environment, a sand ground environment, a stone road environment, a grass environment, a stair environment, and a crowd environment.
In one embodiment, the control apparatus is further configured to include:
and the controller state machine is used for selecting a control strategy according to the planning information.
The controller state machine can select different control strategies according to planning information generated by the planning state machine, calculate motor driving control parameters on each leg of the multi-legged robot, and further control the multi-legged robot to move according to the planning information.
In one embodiment, the controller state machine selectable control strategies include: an impedance controller, a balance controller, a dynamic controller and a model predictive controller.
In one embodiment, the control system of the multi-legged robot further comprises:
the state detection device is used for detecting the self state information of the multi-legged robot;
the control apparatus is further configured to include:
and the hardware state machine is used for acquiring the self state information and switching the hardware working mode according to the self state information.
The corresponding hardware working modes can be switched according to the self state information by using the hardware state machine, and the related hardware such as the motor of the multi-legged robot is controlled to work in the selected hardware working mode, so that the stability and the safety of the multi-legged robot are ensured.
In one embodiment, the selectable operating modes of the hardware state machine include: damping mode, command waiting mode and running mode.
In one embodiment, the hardware state machine is used for switching to a damping mode and sending an abnormal signal to the controller state machine when judging that the state is abnormal according to the state information of the hardware state machine;
the controller state machine is also used for switching to the impedance controller when the abnormal signal is received so as to keep the multi-legged robot in a difficult-to-operate state.
An multi-legged robot comprising a multi-legged robot control system as in any of the above embodiments.
Above-mentioned polypod robot through utilizing polypod robot control system, can realize independently stably moving under different environment or the superimposed complex environment of multiple environment, improves polypod robot's autonomic degree.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the conventional technologies of the present application, the drawings used in the descriptions of the embodiments or the conventional technologies will be briefly introduced below, it is obvious that the drawings in the following descriptions are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the structure of a multi-legged robot in one embodiment;
FIG. 2 is a block diagram of a portion of the structure of the multi-legged robot in one embodiment;
FIG. 3 is one of the block diagrams of the control system of the multi-legged robot in one embodiment;
FIG. 4 is a second block diagram of the control system of the multi-legged robot in one embodiment;
FIG. 5 is a third block diagram of the control system of the multi-legged robot in one embodiment;
FIG. 6 is a block diagram of the control system of the multi-legged robot in one embodiment;
fig. 7 is a schematic diagram of the application of the multi-legged robot in one embodiment.
Description of reference numerals:
100. a body; 200. a leg; 210. driving a motor; 300. a multi-legged robot control system; 310. an environment detection device; 320. a control device; 321. a task state machine; 322. planning a state machine; 323. an environmental state machine; 324. a communication module; 325. a controller state machine; 326. a hardware state machine; 330. and a state detection device.
Detailed Description
To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Embodiments of the present application are given in the accompanying drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises/comprising," "includes" or "including," etc., specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof. Also, as used in this specification, the term "and/or" includes any and all combinations of the associated listed items.
As shown in fig. 1, in one embodiment, the multi-legged robot includes a main body 100 and a plurality of legs 200, wherein a power supply, a control system, etc. are installed on the main body 100, each leg 200 has a plurality of motor drives 210, and the motor drives 210 installed on each leg 200 operate under the control of the control system to drive the multi-legged robot to move.
In one embodiment, as shown in fig. 2 and 3, there is provided a multi-legged robot control system 300, comprising:
an environment detection device 310 for detecting road surface data;
a control device 320 configured to include a plurality of finite state machines:
a Task state machine 321 (Task FSM) configured to obtain a Task instruction and send Task information according to the Task instruction; the task instruction comprises a movement speed instruction and/or a target point instruction;
an environment state machine 323 (environment FSM) for analyzing road surface environment information according to road surface data detected in real time by the environment detecting device 310;
and a planning state machine 322 (planning FSM) for generating planning information according to the task information after receiving the task information, wherein the planning information includes a movement route planned according to the task instruction and a movement gait planned according to the road surface environment information.
The environment detection device 310 can detect the road data of the current environment of the multi-legged robot in real time, specifically, the environment detection device 310 can include devices such as a laser radar and a camera, and the road obstacle condition can be identified by the laser radar, and the road image can be collected by the camera.
The state machine is a control center which is composed of a state register and a combinational logic circuit, can carry out state transition according to a preset state according to a control signal, coordinates related signal actions and completes specific operations. A Finite State Machine (FSM) is a mathematical model that represents the behavior of a Finite number of states and transitions and actions between these states. The control device 320 is configured to include a plurality of finite state machines capable of coordinating the movements of the legs 200 of the multi-legged robot according to a preset operation logic, so as to realize reliable movements in complex environments, and enable the multi-legged robot to work in more complex scenes, such as performing search and rescue tasks by itself, entering a high-intensity radiation environment to perform mechanical detection tasks, and the like.
Specifically, the control device 320 includes a Task state machine 321 (Task FSM), an environment state machine 323 (environment FSM), and a planning state machine 322 (planning FSM). The Task state machine 321 (Task FSM) is a highest-level module in the multi-legged robot control system 300, and is mainly used to process Task instructions, where the Task instructions may include remote control movement (Vel coordination), demonstration (Stance Demo, goal Demo), sensing data acquisition, autonomous navigation movement, and the like. The Task state machine 321 (Task FSM) converts these Task instructions of the user into Task information, the Task instructions include a movement speed instruction and/or a goal point instruction, and the Task state machine 321 (Task FSM) converts the Task instructions into Task information capable of being processed by the planning state machine 322 (planning FSM) and sends the Task information to the planning state machine 322 (planning FSM). The environment state machine 323 (environment FSM) can analyze the road surface environment information according to the road surface data detected by the environment detection device 310 and send the road surface environment information to the planning state machine 322 (planning FSM), for example, the environment state machine 323 (environment FSM) can identify the road surface image collected by the camera through a preset algorithm to judge the type of the road surface; the environment state machine 323 (environment FSM) can also determine the type of road surface based on the obstacle situation identified by the lidar. When receiving the Task information (motion speed command and/or target point command) sent by the Task state machine 321 (Task FSM), the planning state machine 322 (planning FSM) autonomously selects a corresponding motion gait and a corresponding motion route (planning a foot drop point for each movement) according to the Task information and the road environment information analyzed by the environment state machine 323 (environment FSM), thereby ensuring that the multi-legged robot keeps stable motion. In one embodiment, the planning state machine 322 (planning FSM) includes a gait generator for determining the order and frequency of the swing legs 200 of the multi-legged robot. For example, in a riprap, in order to ensure that the multi-legged robot can stably move, a slow static walking gait is selected, so that the multi-legged robot is ensured to have at least three legs 200 standing on the ground all the time in the moving process, and only one leg 200 is in action at most. In one embodiment, the planning state machine 322 (planning FSM) further includes a navigation module for performing autonomous navigation according to a target point and a preset map to plan a movement route.
The multi-legged robot control system 300 detects road surface data of the environment where the multi-legged robot is located through the environment detection device 310 and feeds the road surface data back to the control device 320, and the control device 320 is configured to include a Task state machine 321 (Task FSM), an environment state machine 323 (environmental FSM), and a planning state machine 322 (planning FSM), so that the control device 320 can acquire a Task instruction and analyze road surface environment information, and automatically plan a movement route and a movement gait according to the acquired Task instruction and the road surface data, thereby realizing autonomous stable movement in different environments or complex environments with multiple environments superimposed, and improving the autonomous degree of the multi-legged robot.
As shown in fig. 4, in one embodiment, the control device 320 includes a communication module 324, the communication module 324 is used for establishing communication with the remote control device to transmit data; the remote control device is used for sending task instructions.
The user sends a task command to the control device 320 of the multi-legged robot through the remote control device, which sends a task command to the control module by establishing a connection with the communication module 324 in the control device 320. In one embodiment, the control device 320 can also send data, such as positioning, pictures taken by the multi-legged robot, etc., via the communication module 324 and the remote control device.
In one embodiment, the planning state machine 322 (planning FSM) selectable motion gait includes: standing, slow static walking, diagonal jogging, diagonal walking, and diagonal jogging.
According to the acquired task information, the planning state machine 322 (planning FSM) can select different movement gaits, for example, before receiving a task or after completing the task, in a task instruction waiting phase, at which time the planning state machine 322 (planning FSM) selects a standing movement gait, that is, each leg 200 stands on the ground. In an uneven road environment, the planning state machine 322 (planning FSM) may select a slow static walking motion gait, i.e., at least three legs 200 stand on the ground and at most only one leg 200 swings. If a slow movement command is received in a relatively flat road environment, the planning state machine 322 (planning FSM) may select a diagonal slow walking gait, that is, the gaits of the legs 200 in the diagonal positions are consistent, and the swing frequency is low, which is described by taking a four-legged robot as an example: the left front leg 200 and the right rear leg 200 keep the same gait, the right front leg 200 and the left rear leg 200 keep the same gait, the left front leg 200 and the right rear leg 200 and the right front leg 200 and the left rear leg 200 alternately change the gait, namely the gait is in the state of swinging or standing at the same time, the alternating change of the gait, namely the left front leg 200 and the right rear leg 200 are in the swinging state, the right front leg 200 and the left rear leg 200 are in the standing state; the left front leg 200 and the right rear leg 200 are in standing, and the right front leg 200 and the left rear leg 200 are in swinging. If a normal speed movement command is received in a relatively flat road environment, the planning state machine 322 (planning FSM) can select a diagonal walking movement gait, that is, the gait of the leg 200 in the diagonal position is kept consistent, and the swing frequency is slightly higher. If a rapid movement command is received in a relatively flat road environment, the planning state machine 322 (planning FSM) can select a diagonal sprint movement gait, that is, the gait of the leg 200 at the diagonal position is kept consistent, and the swing frequency is high.
In one embodiment, the motion speed corresponding to the standing gait is 0m/s, the motion speed corresponding to the slow static walking gait is 0-0.3m/s, the motion speed corresponding to the diagonal slow walking gait is 0.1m/s-0.5m/s, the motion speed corresponding to the diagonal walking gait is 0.4-0.8m/s, and the motion speed corresponding to the diagonal sprint gait is 0.8-1.5m/s.
In one embodiment, the types of the road surface environment information include a flat ground environment, a slope environment, a sand ground environment, a stone road environment, a grass environment, a stair environment, and a crowd environment.
The environment state machine 323 (environment FSM) analyzes the road surface data detected by the environment detection device 310, determines the road surface environment information of the area where the multi-legged robot is currently walking, and sends the information to the planning state machine 322 (planning FSM). In one embodiment, the environment state machine 323 (environment FSM) may adopt a neural network algorithm to identify the image collected by the environment detection device 310, determine the road environment information, and further, optimize the identification result by matching the obstacle identification condition of the laser radar on the basis of the result of the algorithm identification, thereby improving the identification accuracy, ensuring that the multi-legged robot can select a proper movement gait, and ensuring the movement stability thereof.
As shown in fig. 5, in one embodiment, the control device 320 is further configured to include:
a controller state machine 325 (controller FSM) for selecting a control strategy based on the planning information.
The controller state machine 325 (controller FSM) selects different control strategies according to the planning information generated by the planning state machine 322 (planning FSM), calculates the control parameters of the motor drives 210 on each leg 200 of the multi-legged robot, controls the motor drives 210 to work, and further drives the multi-legged robot to move according to the planning information.
In one embodiment, the controller state machine 325 (controller FSM) may select a control strategy comprising: an impedance controller, a balance controller, a dynamic controller and a model predictive controller.
An impedance controller, namely a PD controller (probabilistic-Derivative controller), independently uses a PD linear inverse control law for each joint, can ensure progressive stability, and is easy to control; the balance controller is a balance controller, and can enable the multi-legged robot to walk statically at a low speed, so that the multi-legged robot can stop in the walking action without falling down; the dynamic controller, namely a Dynamics controller, can realize dynamic control on the multi-legged robot and realize diagonal movement gait; the Model Predictive controller (MPC controller) can deal with disturbance under rugged terrain, and ensures that the multi-legged robot walks more stably and reliably under complex terrain. Each control strategy preset by the controller state machine 325 (controller FSM) may be corresponding to a movement gait preset by the planning state machine 322 (planning FSM), and after the movement gait is determined, the controller state machine 325 (controller FSM) may determine the control strategy to control.
As shown in fig. 6, in one embodiment, the multi-legged robot further comprises:
a state detection means 330 for detecting the self-state information of the multi-legged robot;
the control device 320 is further configured to include:
and a hardware state machine 326 (hardware FSM) for acquiring the self state information and switching the hardware working mode according to the self state information.
The state detecting means 330 is provided on the body 100 of the multi-legged robot and/or the legs 200 of the multi-legged robot, and detects the self-state information of the multi-legged robot, including whether the operation states of the respective members of the multi-legged robot are normal, whether the multi-legged robot turns on its side, and the like. After acquiring the self-state information of the multi-legged robot detected by the state detection device 330, the hardware state machine 326 (hardware FSM) switches the corresponding hardware working mode according to the self-state information, and controls the relevant hardware such as the motor of the multi-legged robot to work in the selected hardware working mode, so as to ensure the stability and safety of the multi-legged robot.
In one embodiment, the hardware state machine 326 (hardware FSM) selectable operating modes include: damping mode, command waiting mode and running mode.
In the damping mode, hardware such as a motor of the multi-legged robot is in a damping state, so that the multi-legged robot keeps a state of difficult movement and can be protected from being damaged; in the command waiting mode, hardware such as a multi-legged robot motor and the like is in a state of waiting for a control command, stands by for executing the command, and can immediately respond to the command after receiving a task command; in the running mode, hardware such as a motor drives each joint to move in a matched mode through different driving modes, corresponding gait and speed are executed, and the multi-legged robot can stably move.
In one embodiment, the hardware state machine 326 (hardware FSM) is configured to switch to the damping mode and send an exception signal to the controller state machine 325 (controller FSM) when determining that the state is abnormal according to the state information of the hardware state machine 326 (hardware FSM);
the controller state machine 325 (controller FSM) is also used to switch to the impedance controller upon receiving an exception signal to maintain the multi-legged robot in a difficult-to-operate state.
The hardware state machine 326 (hardware FSM) judges the hardware working state of the multi-legged robot or when the multi-legged robot turns on the side according to the self state information, the state is considered to be abnormal, at the moment, in order to protect the multi-legged robot from being damaged, the hardware state machine is switched to a damping mode, meanwhile, an abnormal signal is sent to the controller state machine 325 (controller FSM), when the controller state machine 325 (controller FSM) receives the abnormal signal, a control strategy is switched to the impedance controller, and the damping mode of the hardware state machine 326 (hardware FSM) is matched to guarantee that the multi-legged robot cannot act and damage is avoided.
In one embodiment, there is also provided a multi-legged robot including the multi-legged robot control system 300 according to any one of the above embodiments.
The control device 320 in the multi-legged robot control system 300 includes a planning state machine 322 (planning FSM), a control state machine, a hardware state machine 326 (hardware FSM), an environmental state machine 323 (environment FSM), and a Task state machine 321 (Task FSM). The finite state machines can be switched with each other according to different conditions, so that an optimal state is selected to complete the task instruction sent by the user. The selection of the finite state machine enables the multi-legged robot to operate safely and reliably under different environments independently without manual control and assistance, and improves the independence, stability and safety of the multi-legged robot in operation under various environments. The control device 320 is configured with a plurality of finite state machines in a modularized design, so that the complexity of the control system is reduced, the maintenance aspects such as the function increase and modification of the control system are convenient, and the error is not easy to occur. In one embodiment, if a control method is added, the control method can be easily added to the multi-legged robot control system 300 only by writing the control method into a state machine. The Task state machine 321 (Task FSM) module, the planning state machine 322 (planning FSM), the hardware state machine 326 (hardware FSM), the environment state machine 323 (environment FSM), and the controller state machine 325 (controller FSM) can also increase, decrease, and modify the related states of the control method, so that those skilled in the art can design the multi-legged robot control system 300 according to the needs of different application scenarios, so that the multi-legged robot can realize movements with high autonomy, high stability, and high security in different application scenarios.
In one embodiment, the multi-legged robot further comprises a remote control device, through which a user can send task instructions to the multi-legged robot control system 300.
In one embodiment, the control device 320 of the multi-legged robot control system 300 is also capable of interacting with a server via a network to obtain task instructions sent by the server; in one embodiment, the multi-legged robotic control system 300 can upload data to a server, such as environmental data collected by the environmental detection device 310.
As shown in fig. 7, the task of the multi-legged robot receiving the inspection required to pass through a lawn and stairs to the factory is explained as an example. The state machine of the multi-legged robot in the process is used for completing the task of autonomous control by the following processes:
1. the user issues a task instruction for the multi-legged robot to a specified factory building to perform sensor data acquisition through the remote control device, such as data of instruments and meters in the factory. At this time, the Task state machine 321 (Task FSM) sets the state to a sensor data collection state, and at this time, the multi-legged robot control system 300 can trigger the existing database, calculate the path that the multi-legged robot needs to travel, the position of the plant that needs to arrive, and the sensor data on the station that needs to be checked.
2. At this point the Task state machine 321 (Task FSM) passes the Task information to the planning state machine 322 (planning FSM) and the robot is ready to start motion. At this time, the environment state machine 323 (environment FSM) starts to work, and senses the real-time environment of the robot by installing a plurality of sensors such as a visual camera and a laser radar on the robot. When the multi-legged robot moves forward, the environmental state machine 323 needs to go through a lawn and a continuous step according to the analysis of the road surface data detected by the environment detection device 310. At this time, the environment state machine 323 (environment FSM) transmits the information to the planning state machine 322 (planning FSM), and the planning state machine 322 automatically reduces the exercise speed to 0.4m/s according to the preset exercise gait and exercise speed selection logic, and selects the diagonally slow-walking exercise gait, so as to pass through the lawn more stably without slipping or even falling. When the multi-legged robot comes to continuous steps through the grassland, the robot easily turns over due to the step climbing, at the moment, a planning state machine 322 (planning FSM) reduces the speed of the multi-legged robot to 0.3m/s, and selects a gait of slow static walking, wherein the gait of slow static walking is a static gait, at least 3 legs 200 stand on the ground at each moment, and the other legs 200 swing forwards to step up the steps one by one.
3. During the motion of the multi-legged robot, the controller state machine 325 (controller FSM) and the hardware state machine 326 (hardware FSM) also work simultaneously, such as when passing on grass, the multi-legged robot needs to have stronger disturbance resistance due to the rugged ground, and the controller state machine 325 (controller FSM) is needed to start the Model Predictive Controller (MPC) to control.
4. After the robot passes through the grass and the steps and enters the factory, the environmental state machine 323 (environmental FSM) analyzes the road surface data fed back by the environmental detection device 310 to be a smooth road, and the requirement on the disturbance resistance of the multi-legged robot is low, so the controller state machine 325 (controller FSM) automatically switches the control strategy to a balance controller (ballast controller).
5. When the multi-legged robot is in operation, the hardware state machine 326 (hardware FSM) is operated in real time, and is used for acquiring self state information fed back by the state detection device 330, the state of the body of the multi-legged robot is analyzed, when a device works in an abnormal state or the multi-legged robot is in a side-turning state, the multi-legged robot should be switched to a protection mode, the hardware state machine 326 (hardware FSM) sets the state to a damping mode, and meanwhile, the controller state machine 325 (controller FSM) is switched to an impedance controller (PD controller), and the multi-legged robot is in a difficult-to-move state so as to protect the multi-legged robot from being damaged. When the multi-legged robot normally operates and processes tasks, the operation mode is switched. When the task is completed, the mode is switched to an instruction waiting mode, and a new task instruction can be waited at the moment.
The finite state machine configured by the control device 320 continuously performs autonomous switching according to the task instruction and environment and the state of the multi-legged robot according to the above procedures, thereby controlling the multi-legged robot to autonomously complete the task given by the user.
Those skilled in the art will appreciate that implementing each of the above-described embodiments of the multi-legged robot control system can be accomplished by instructing the associated hardware by a computer program, which can be stored in a non-volatile computer-readable storage medium, and which, when executed, can comprise the processes of the above-described embodiments of the multi-legged robot control system. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
In the description herein, references to "some embodiments," "other embodiments," "desired embodiments," or the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, a schematic description of the above terminology may not necessarily refer to the same embodiment or example.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (7)
1. A control system for a multi-legged robot, comprising:
the environment detection device is used for detecting road surface data;
a control apparatus configured to include a plurality of finite state machines:
the task state machine is used for acquiring a task instruction and sending task information according to the task instruction; the task instruction comprises a movement speed instruction and/or a target point instruction;
the environment state machine is used for analyzing the road surface environment information according to the road surface data detected by the environment detection device in real time;
the planning state machine is used for generating planning information according to the task information after receiving the task information, and the planning information comprises a movement route planned according to the task instruction and a movement gait planned according to the road surface environment information; the selectable motion gait of the planning state machine comprises: standing, slow static walking, diagonal jogging, diagonal walking and diagonal jogging;
the control apparatus is further configured to include: a controller state machine for selecting a control strategy according to the planning information; the controller state machine selectable control strategies include: the system comprises an impedance controller, a balance controller, a dynamic controller and a model prediction controller; the impedance controller is used for independently using a PD linear inverse control law for each joint; the model predictive controller is used to cope with disturbances in rough terrain.
2. The multi-legged robotic control system of claim 1, wherein the control means includes a communication module for establishing communication with a remote control device for transmitting data; the remote control device is used for sending the task instruction.
3. The multi-legged robot control system according to claim 1, wherein the types of the road surface environment information include a flat ground environment, a slope environment, a sand ground environment, a stone road environment, a grass environment, a stair environment, and a crowd environment.
4. The multi-legged robot control system according to claim 1, further comprising:
the state detection device is used for detecting the self state information of the multi-legged robot;
the control apparatus is further configured to include:
and the hardware state machine is used for acquiring the self state information and switching the hardware working mode according to the self state information.
5. The multi-legged robot control system of claim 4, wherein the hardware state machine selectable modes of operation include: damping mode, command waiting mode and running mode.
6. The multi-legged robot control system according to claim 5, wherein the hardware state machine is configured to switch to a damping mode and send an abnormal signal to the controller state machine when determining that the state is abnormal according to the state information of the hardware state machine;
the controller state machine is also used for switching to the impedance controller when the abnormal signal is received so as to keep the multi-legged robot in a state of difficult action.
7. A multi-legged robot comprising the multi-legged robot control system according to any one of claims 1 to 6.
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| CN113788085B (en) * | 2021-10-12 | 2022-12-30 | 深圳鹏行智能研究有限公司 | Safety protection method and foot type robot |
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