CN112925320B - Biped robot gait energy consumption assessment method based on centroid model - Google Patents
Biped robot gait energy consumption assessment method based on centroid model Download PDFInfo
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- G05D1/02—Control of position or course in two dimensions
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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
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
Abstract
The invention relates to a biped robot gait energy consumption assessment method based on a centroid model, which comprises the steps of firstly establishing the centroid model according to the distribution position of the mass M of the biped robot on a body, describing the motion track of the biped robot by using the centroid model, secondly establishing a joint actuator load torque equation by the Cartesian product of a space position vector and a centroid gravity vector in the motion process of the biped robot, and obtaining the axial load torque tau (n) of a joint actuator based on the axis direction of the joint actuator, and finally establishing an energy consumption index function E according to the angular speed omega (n) and the axial load torque tau (n) of the joint actuator of the biped robot in the motion process of the biped robot. The invention overcomes the problem that the instantaneous power loss of each joint actuator of the biped robot is difficult to measure, and the parameters which are easy to obtain in the motion planning algorithm of the biped robot are used for constructing the index function for describing the energy consumption in the motion process of the robot, so that the advantages and disadvantages of different algorithms can be quickly and accurately evaluated.
Description
Technical Field
The invention relates to the field of biped robot motion design, in particular to a biped robot gait energy consumption assessment method based on a centroid model.
Background
The energy consumption generated in the motion process of the biped robot comprises motion loss for driving the joint actuator to operate and non-motion energy consumption for the work of the sensor and the controller. In general, the kinematic losses are the major component of the system energy consumption, and their value is the integral of all the joint actuator instantaneous power vectors over time, and the non-kinematic energy consumption generally appears as a linear function of time.
The biped robot is generally composed of a plurality of joint actuator driving connecting rods, and the accurate measurement of the instantaneous power of the joint actuator is not easy. Considering that the instantaneous power of the joint actuator is equal to the sum of the output power of the joint actuator and the additional loss, the output power of the joint actuator as a main component can be regarded as the product of the output torque of the joint actuator and the angular velocity of the joint actuator, and when the biped robot moves, the output torque of the motor is used for overcoming the resistance of the load torque to the rotating shaft of the actuator.
Under general conditions, the angular speed and the load torque of the joint actuator in the motion planning process of the biped robot are easy to obtain, if an energy consumption function of the motion planning algorithm of the biped robot is constructed on the basis of the angular speed and the load torque of the joint actuator, the energy consumption function can be quickly calculated by applying a computer simulation technology in the algorithm optimization process, and the method is suitable for evaluating the advantages and the disadvantages of different motion planning algorithms.
Disclosure of Invention
The invention provides a biped robot gait energy consumption assessment method based on a centroid model for assessing the gait energy consumption of the conventional biped robot, and the energy consumption assessment of a biped robot gait planning algorithm is realized by establishing an energy consumption index function E according to the angular velocity omega (n) and the axial load torque tau (n) of a joint actuator of the biped robot.
The invention provides a biped robot gait energy consumption assessment method based on a centroid model, which comprises the following steps:
step 1: establishing a mass center model according to the distribution position of the mass M of the biped robot on the body, and describing the motion trail of the biped robot by using the mass center model;
step 2: constructing a joint actuator load torque equation through a Cartesian product of a space position vector and a mass center gravity vector in the motion process of the biped robot, and obtaining an axial load torque tau (n) of the joint actuator based on the axis direction of the joint actuator;
and step 3: and in a gait cycle N, establishing an energy consumption index function E according to the angular velocity omega (N) and the axial load torque tau (N) of the joint actuator of the biped robot, wherein the energy consumption index function E is used for evaluating the gait energy consumption of the biped robot.
Further, the step 1 specifically includes:
step 1.1: according to the distribution position of the mass M of the biped robot on the body, a mass center model is established through a formula (1):
wherein m isjRepresents the jth centroid of the biped robot, j being 1,2, …, K;
step 1.2: according to the mass center m of the biped robotjAnd the position of the sampling point at the n moment in the motion process of the biped robotRespectively constructing a centroid vector m and a centroid position matrix Rm(n) represented by formulas (2) and (3):
m=[m1 m2…mK] (2);
further, the step 2 specifically includes:
step 2.1: on the premise of no loss of generality, the biped robot is composed of connecting rods driven by L joint actuators, and the position of the ith joint actuator in the motion process of the biped robot isThe ith joint actuator load torqueAs expressed by equation (4):
wherein the content of the first and second substances,representing a spatial position vector between the ith joint actuator position and the jth centroid position,is the jth centroid gravity vector,g=9.81m/s2,support coefficient of link driven by ith joint actuator to jth mass centerIndicating full support, coefficient of supportRepresenting shared support, support coefficientRepresenting no support, the joint actuator load torque is represented as the sum of the cartesian products of the spatial position vector and the centroid gravity vector;
step 2.2: the axial unit vector of the ith joint actuator in the motion of the biped robot isBased on equation (4), the axial load torque τ of the ith joint actuatori(n) is expressed as formula (5):
further, the step 3 specifically includes:
step 3.1: under the premise of no loss of generality, in the motion process of the biped robot, the corresponding angles of the ith joint actuator at the sampling point n and n-1 are q respectivelyi(n) and qi(n-1), the angular velocity ω of the ith joint actuatori(n) is expressed as formula (6):
wherein, tsSampling period of robot motion control system;
step 3.2: in the motion process of the biped robot, an angular velocity vector omega (n) and an axial load torque vector tau (n) of a joint actuator are respectively expressed as follows:
ω(n)=[ω1(n) ω2(n)…ωL(n)] (7);
τ(n)=[τ1(n) τ2(n)…τL(n)] (8);
step 3.3: when the gait cycle of the biped robot motion planning is N, establishing an energy consumption index function E by the angular speed omega (N) and the axial load torque tau (N) of the joint actuator, as represented by the formula (9):
wherein s (n) diag(s)1(n),s2(n),…sL(n)) is an L-order diagonal matrix for representing the directional relationship between the angular velocity vector ω (n) and the axial load torque τ (n), if si(n) 0 represents τi(n) and ωi(n) in the same direction, i.e. gravity forces the joint actuator to move if si(n) 1 represents τi(n) and ωi(n) opposite directions, i.e., the joint actuator moves against gravity.
Through the technical scheme, the invention has the beneficial effects that:
the method describes the motion track of the biped robot through a centroid model, further obtains the axial load torque tau (n) of the joint actuator of the biped robot and the angular speed omega (n) of the joint actuator of the biped robot, establishes an energy consumption index function E based on the angular speed omega (n) and the axial load torque tau (n) of the joint actuator of the biped robot through stress analysis, calculates the gait track energy consumption of each sampling point of the biped robot by using the energy consumption index function E, and finally realizes the evaluation of the gait energy consumption of the biped robot.
According to the method, the gait energy consumption of the biped robot of the centroid model is evaluated by establishing the energy consumption index function E, the problem that the instantaneous power consumption of each joint actuator of the biped robot is difficult to measure is solved, the analysis is convenient to be carried out by combining the gait, so that the accuracy of the energy consumption evaluation of the biped robot of the centroid model is greatly improved, and a reliable foundation is provided for the gait algorithm optimization of the biped robot of the centroid model.
Drawings
FIG. 1 is a flow chart of a gait energy consumption evaluation method of a biped robot based on a centroid model.
FIG. 2 is a diagram of a humanoid robot used by a verification algorithm of the gait energy consumption evaluation method of the biped robot based on the centroid model.
Fig. 3 is a variable schematic diagram of the biped robot energy consumption evaluation method of the biped robot gait energy consumption evaluation method based on the centroid model.
Fig. 4 is a motion simulation diagram of the biped robot of the gait energy consumption evaluation method of the biped robot based on the centroid model.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a gait energy consumption evaluation method for a biped robot based on a centroid model, the method includes the following steps:
step 1: establishing a mass center model according to the distribution position of the mass M of the biped robot on the body, and describing the motion trail of the biped robot by using the mass center model;
step 2: constructing a joint actuator load torque equation through a Cartesian product of a space position vector and a mass center gravity vector in the motion process of the biped robot, and obtaining an axial load torque tau (n) of the joint actuator based on the axis direction of the joint actuator;
and step 3: and in a gait cycle N, establishing an energy consumption index function E according to the angular velocity omega (N) and the axial load torque tau (N) of the joint actuator of the biped robot, wherein the energy consumption index function E is used for evaluating the gait energy consumption of the biped robot.
The method is used for describing the motion trail of the biped robot through the position of a joint actuator and the position of a mass center in the motion of the biped robot so as to determine the energy consumption in the motion of the biped robot, and further establishing an energy consumption index function E according to the angular speed omega (n) and the axial load torque tau (n) of the joint actuator of the biped robot;
the error caused by directly measuring the instantaneous power of the joint actuator by using a sensor is avoided, and the energy consumption value of each sampling point in the motion of the biped robot is convenient to grasp and determine, so that an energy consumption evaluation model of the biped robot is constructed;
example 2
On the basis of the embodiment 1, the embodiment of the present invention is different from the embodiment in that the method optimizes the step 1 and establishes the biped robot of the K centroid model, specifically:
step 1.1: according to the distribution position of the mass M of the biped robot on the body, a mass center model is established through a formula (1):
wherein m isjRepresents the jth centroid of the biped robot, j being 1,2, …, K;
step 1.2: according to the mass center m of the biped robotjAnd the position of the sampling point at the n moment in the motion process of the biped robotRespectively constructing a centroid vector m and a centroid position matrix Rm(n) represented by formulas (2) and (3):
m=[m1 m2…mK] (2);
example 3
On the basis of the above embodiment 2, the difference between the embodiment of the present invention and the above embodiment is that, as shown in fig. 3, in order to obtain the axial load torque vector τ (n) of the joint actuator, the step 2 is optimized, specifically:
step 2.1: on the premise of no loss of generality, the biped robot is composed of connecting rods driven by L joint actuators, and the position of the ith joint actuator in the motion process of the biped robot isThe ith joint actuator load torqueAs expressed by equation (4):
wherein the content of the first and second substances,representing a spatial position vector between the ith joint actuator position and the jth centroid position,is the jth centroid gravity vector,g=9.81m/s2,support coefficient of link driven by ith joint actuator to jth mass centerIndicating full support, coefficient of supportRepresenting shared support, support coefficientRepresenting no support, the joint actuator load torque is represented as the sum of the cartesian products of the spatial position vector and the centroid gravity vector;
step 2.2: the axial unit vector of the ith joint actuator in the motion of the biped robot isBased on equation (4), the axial load torque τ of the ith joint actuatori(n) is expressed as formula (5):
example 4
On the basis of the above embodiments, the difference between the embodiment of the present invention and the above embodiments is that, in order to obtain the angular velocity vector ω (n) of the joint actuator, and create the consumption index function E based on the angular velocity vector ω (n) of the joint actuator, step 3 is optimized, specifically:
step 3.1: under the premise of no loss of generality, in the motion process of the biped robot, the corresponding angles of the ith joint actuator at the sampling point n and n-1 are q respectivelyi(n) and qi(n-1), the angular velocity ω of the ith joint actuatori(n) is expressed as formula (6):
wherein, tsSampling period of robot motion control system;
step 3.2: in the motion process of the biped robot, an angular velocity vector omega (n) and an axial load torque vector tau (n) of a joint actuator are respectively expressed as follows:
ω(n)=[ω1(n) ω2(n)…ωL(n)] (7);
τ(n)=[τ1(n) τ2(n)…τL(n)] (8);
step 3.3: when the gait cycle of the biped robot motion planning is N, establishing an energy consumption index function E by the angular speed omega (N) and the axial load torque tau (N) of the joint actuator, as represented by the formula (9):
wherein s (n) diag(s)1(n),s2(n),…sL(n)) is an L-order diagonal matrix for representing the directional relationship between the angular velocity vector ω (n) and the axial load torque τ (n), if si(n) 0 represents τi(n) and ωi(n) in the same direction, i.e. gravity forces the joint actuator to move if si(n) 1 represents τi(n) and ωi(n) opposite directions, i.e. the joint actuator overcomes the weightThe force moves.
The following experiments were conducted to demonstrate the effects of the present invention
As shown in figure 2, the biped (humanoid) robot is constructed to include a body centroid M according to the distribution position of the mass M on the body1Mass center m of two legs2And m3Mass center m of both feet4And m5The five centroid model of (1). The legs of the biped robot are of a multi-link structure with 10 links, and respectively comprise hip joint rolling control q1And q is2Hip joint pitch control q3And q is4Knee joint pitch angle q5And q is6Ankle joint pitch angle q7And q is8And ankle joint roll angle q9And q is10;
Further as shown in fig. 3, the centroid position of the biped robotJoint actuator positionAnd joint actuator axial direction
Wherein g is 9.81m/s2The barycenter vector m of the biped robot is (m)1 m2…m5]The values of (a) are shown in table 1:
TABLE 1 center of mass of biped robot
In order to express the rationality of the energy consumption evaluation method, in the motion planning of the biped robot, the step length s is 10cm, the gait cycle N is 16, and the sampling period t is selectedsA set of gait data of 0.1s, wherein the center of mass position R is in the motion process of the biped robotmThe x-direction and y-direction components of (n) are shown in table 2:
TABLE 2 biped robot in motionCentroid position Rm(n) (unit: cm)
Further, the position R of the joint actuator during the movementa(n) the X-direction component is shown in Table 3, and the Joint actuator position RaThe y-direction component of (n) is shown in Table 4:
TABLE 3 Joint actuator position R in biped robot motionaX component of (n) (unit: cm)
TABLE 4 Joint executor position R in biped robot motion processaY component of (n) (unit: cm)
Further, the joint actuator load torque τ (n) during motion is shown in table 5:
TABLE 5 axial load of joint actuator during biped robot motion Joint actuator load torque τ (n) (unit: Ncm)
Further, the angular velocity ω (n) of the joint actuator during motion is shown in table 6:
TABLE 6 angular velocity ω (n) (unit: 0.01rad/s) of the joint actuator in the biped robot motion process
Through the data shown in tables 1-6, based on the method, the motion energy consumption of the biped robot in the gait cycle N is calculated, and then the gait energy consumption evaluation of the biped robot is realized, as shown in fig. 4, the biped robot is subjected to motion simulation, and finally the energy consumption index function E of the biped robot is 11.35J.
The above-described embodiments are merely preferred embodiments of the present invention, and not intended to limit the scope of the invention, so that equivalent changes or modifications in the structure, features and principles described in the present invention should be included in the claims of the present invention.
Claims (1)
1. A gait energy consumption assessment method of a biped robot based on a centroid model is characterized by comprising the following steps:
step 1: establishing a mass center model according to the distribution position of the mass M of the biped robot on the body, and describing the motion trail of the biped robot by using the mass center model;
the step 1 specifically comprises:
step 1.1: according to the distribution position of the mass M of the biped robot on the body, a mass center model is established through a formula (1):
wherein m isjRepresents the jth centroid of the biped robot, j being 1,2, …, K;
step 1.2: according to the quality of the biped robotHeart mjAnd the position of the sampling point at the n moment in the motion process of the biped robotRespectively constructing a centroid vector m and a centroid position matrix Rm(n) represented by formulas (2) and (3):
m=[m1 m2…mK] (2);
step 2: constructing a joint actuator load torque equation through a Cartesian product of a space position vector and a mass center gravity vector in the motion process of the biped robot, and obtaining an axial load torque tau (n) of the joint actuator based on the axis direction of the joint actuator;
the step 2 specifically comprises:
step 2.1: on the premise of no loss of generality, the biped robot is composed of connecting rods driven by L joint actuators, and the position of the ith joint actuator in the motion process of the biped robot isThe ith joint actuator load torqueAs expressed by equation (4):
wherein the content of the first and second substances,representing a spatial position vector between the ith joint actuator position and the jth centroid position,is the jth centroid gravity vector, support coefficient of link driven by ith joint actuator to jth mass centerIndicating full support, coefficient of supportRepresenting shared support, support coefficientRepresenting no support, the joint actuator load torque is represented as the sum of the cartesian products of the spatial position vector and the centroid gravity vector;
step 2.2: the axial unit vector of the ith joint actuator in the motion of the biped robot isBased on equation (4), the axial load torque τ of the ith joint actuatori(n) is expressed as formula (5):
and step 3: the method comprises the following steps that in a gait cycle N, an energy consumption index function E is established according to the angular speed omega (N) and the axial load torque tau (N) of a joint actuator of the biped robot, and the energy consumption index function E is used for evaluating gait energy consumption of the biped robot;
the step 3 specifically includes:
step 3.1: under the premise of no loss of generality, in the motion process of the biped robot, the corresponding angles of the ith joint actuator at the sampling point n and n-1 are q respectivelyi(n) and qi(n-1), the angular velocity ω of the ith joint actuatori(n) is expressed as formula (6):
wherein, tsSampling period of robot motion control system;
step 3.2: in the motion process of the biped robot, an angular velocity vector omega (n) and an axial load torque vector tau (n) of a joint actuator are respectively expressed as follows:
ω(n)=[ω1(n) ω2(n)…ωL(n)] (7);
τ(n)=[τ1(n) τ2(n)…τL(n)] (8);
step 3.3: when the gait cycle of the biped robot motion planning is N, establishing an energy consumption index function E by the angular speed omega (N) and the axial load torque tau (N) of the joint actuator, as represented by the formula (9):
wherein s (n) diag(s)1(n),s2(n),…sL(n)) is an L-order diagonal matrix for representing the directional relationship between the angular velocity vector ω (n) and the axial load torque τ (n), if si(n) 0 represents τi(n) and ωi(n) in the same direction, i.e. gravity forces the joint actuator to move if si(n) 1 represents τi(n) and ωi(n) opposite directions, i.e., the joint actuator moves against gravity.
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101847009A (en) * | 2010-05-28 | 2010-09-29 | 广东工业大学 | Biped robot gait energy efficiency optimization method |
CN108345211A (en) * | 2017-01-23 | 2018-07-31 | 深圳市祈飞科技有限公司 | Biped anthropomorphic robot and its non-linear gait planning method and control method |
CN111438694A (en) * | 2020-05-21 | 2020-07-24 | 中国计量大学 | Biped robot diagonal gait planning method based on double generation functions |
EP3695783A1 (en) * | 2019-02-15 | 2020-08-19 | Origin Wireless, Inc. | Method, apparatus, and system for wireless gait recognition |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108710298B (en) * | 2018-05-23 | 2021-05-11 | 中国海洋大学 | Low-energy-consumption attitude analysis method based on foot type bionic robot in standing state |
CN108920863B (en) * | 2018-07-20 | 2021-02-09 | 湖南大学 | Method for establishing energy consumption estimation model of robot servo system |
CN112222703B (en) * | 2020-09-30 | 2022-11-04 | 上海船舶工艺研究所(中国船舶集团有限公司第十一研究所) | Energy consumption optimal trajectory planning method for welding robot |
CN113110630A (en) * | 2021-04-23 | 2021-07-13 | 重庆大学 | Energy-saving integrated parameter optimization method for battery replacement robot lifting system |
-
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- 2021-01-25 CN CN202110100346.9A patent/CN112925320B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101847009A (en) * | 2010-05-28 | 2010-09-29 | 广东工业大学 | Biped robot gait energy efficiency optimization method |
CN108345211A (en) * | 2017-01-23 | 2018-07-31 | 深圳市祈飞科技有限公司 | Biped anthropomorphic robot and its non-linear gait planning method and control method |
EP3695783A1 (en) * | 2019-02-15 | 2020-08-19 | Origin Wireless, Inc. | Method, apparatus, and system for wireless gait recognition |
CN111438694A (en) * | 2020-05-21 | 2020-07-24 | 中国计量大学 | Biped robot diagonal gait planning method based on double generation functions |
Non-Patent Citations (6)
Title |
---|
A grid gradient approximation method of energy-efficient gait planning for biped robots;Lu Zhiqiang,Hou Yuanbing,Chai Xiuli,Meng Yun;《International Journal of Advanced Robotic Systems》;20210331;正文全文 * |
Mechanical design optimization for a five-link walking bipedal robot;Ramil Khusainov;《IFAC-Papers On Line》;20210414;第 8953-8958 页 * |
Srinivasan, M ; Ruina, A.Computer optimization of a minimal biped model discovers walking and running.《Nature》.2006, * |
具有半球形足端的六足机器人步态生成和能耗优化研究;陈诚;《中国优秀博硕士学位论文全文数据库(博士)信息科技辑》;20121115;第I140-26页 * |
双足机器人节能步态规划算法;卢志强,侯媛彬,孟芸,周福娜;《西安科技大学学报》;20210531;第540-548页 * |
基于能效优化的两足机器人步态控制方法;王丽杨,刘治,曾小杰,章云;《控制理论与应用》;20110531;第667-674页 * |
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