CN115307636A - Intelligent wheelchair autonomous navigation method - Google Patents
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Abstract
The invention discloses an intelligent wheelchair autonomous navigation method, which comprises a global path planning part and a local path planning part; an A-x algorithm is used as a global path planning algorithm, and a Time Elastic Band (TEB) algorithm is used as a local path planning algorithm, so that an intelligent wheelchair system for realizing autonomous navigation in a specific occasion is designed, and the intelligent wheelchair system makes a certain contribution to solving the problems of difficult actions and lack of care of the old and the patient in a hospital and humanized services of intelligent medical treatment and the hospital.
Description
Technical Field
The invention belongs to the technical field of navigation, and particularly relates to an intelligent wheelchair autonomous navigation method.
Background
The wheelchair is widely applied to hospitals as a travel tool and a medical apparatus, and provides convenience for outpatients or inpatients with mobility inconvenience, old and weak. At present, the necessary facility construction for the wheelchair in the hospital construction of China still stays on the traditional manual wheelchair, and the wheelchair has the defects of low intelligence, poor safety and the like, so that the safer and more intelligent wheelchair construction is very important in the public place of the hospital. However, the research and development technology of the current intelligent wheelchair is not mature in the aspect of navigation intellectualization, and particularly, a poor technology is not provided in the aspect of realizing path planning and navigation in a specific scene of a hospital, so that the design of a navigation system of the intelligent wheelchair in the specific scene of the hospital is very important.
At present, the technology used by the intelligent wheelchair is the fusion of a brain-computer interface technology and an SLAM technology; the method comprises the steps that Beidou navigation positioning is adopted as global path planning information, and accurate walking is achieved by combining with user operation to solve the problem of hospital consultation guiding; the human-in-the-loop framework realizes real-time path modification and the like. The researches realize the autonomous navigation of the wheelchair to a certain extent, but the problems that the detection precision of a two-dimensional radar is insufficient, the Beidou positioning information has errors, the navigation function is reduced in a complex environment, the pertinence is not strong enough and the like exist. Therefore, the intelligent wheelchair self-service navigation system is designed mainly aiming at the problems of reduced navigation function, poor obstacle avoidance effect and poor stability of path planning safety in specific scenes (hospitals, nursing homes and the like), and is capable of exploring and optimizing the prior art to a certain extent and being more intelligent.
The current path planning algorithm mainly comprises two categories of global path planning and local path planning, wherein the global path planning is mainly used for processing the condition that the environment is completely known, and the local path planning refers to the real-time planning of a path for avoiding obstacles only by sensing the surrounding environment information through a sensor of the local path planning and combining the state information of the local path planning under the unknown environment. A common global path planning algorithm is divided into: the heuristic search algorithm is a college search algorithm containing heuristic information, and the heuristic search algorithm is mainly used for setting an heuristic function by using experience knowledge of a specific scene to improve the search efficiency and represents an A-algorithm; the intelligent search algorithm includes genetic algorithm, simulated annealing algorithm, ant colony algorithm, fish colony algorithm, etc. The local path planning algorithm is commonly used in the prior art, such as an Artificial Potential Field (APF) method and a Dynamic Window Approach (DWA). However, the above algorithms have advantages and disadvantages in terms of effectiveness, but the simulation and application in the specific scene of a hospital are lack of experimental support.
According to the mechanical characteristics of the intelligent wheelchair and the limit of the surrounding environment on the speed of the wheelchair, a DWA algorithm forms a dynamic window to sample the speed, simulates the movement track of a certain speed in a period of time in the future, scores the movement tracks of all speeds through a given evaluation function, and selects the speed corresponding to the highest score track in a track set as the speed of the intelligent wheelchair at the next moment. The DWA algorithm considers fewer factors in each calculation, only has the limitations of speed and acceleration, and has small track space and short sampling time, so the calculation of the DWA algorithm is simpler. However, just because of the small consideration factor of the DWA algorithm, the algorithm has many obvious disadvantages, which are embodied in two aspects of poor obstacle avoidance effect and poor foresight. The poor obstacle avoidance effect means that the steering radius cannot be set by the DWA algorithm, so that collision is likely to occur; a prospective deficit refers to the vulnerability of the DWA algorithm to fall into a locally optimal solution. Therefore, in consideration of the unique application scene of the intelligent wheelchair in a hospital, the intelligent wheelchair has the characteristics of large human flow, high uncertainty of activity state and many burst factors, and the defect of poor obstacle avoidance effect can be exposed by adopting the DWA algorithm as a local path planning algorithm.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an intelligent wheelchair autonomous navigation method, which comprises a global path planning part and a local path planning part; an A-x algorithm is used as a global path planning algorithm, and a Time Elastic Band (TEB) algorithm is used as a local path planning algorithm, so that an intelligent wheelchair system for realizing autonomous navigation in a specific occasion is designed, and the intelligent wheelchair system makes a certain contribution to solving the problems of difficult actions and lack of care of the old and the patient in a hospital and humanized services of intelligent medical treatment and the hospital.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1: the intelligent wheelchair autonomous navigation method comprises a global path planning part and a local path planning part;
and 2, step: the global path planning adopts an A algorithm;
the a algorithm is a heuristic search algorithm, compared to the blind search algorithm-breadth-first search and depth-first search, by adding an evaluation function f (n):
f(n)＝g(n)+h(n)
making the search proceed towards a fixed direction; in f (n), g (n) represents the cost paid by searching the current node, a heuristic function h (n) represents the estimated cost from the current node to the target node, and the heuristic function h (n) and the target node are subjected to weighted evaluation to obtain an evaluation function; dividing the search nodes into two types, namely traversed nodes and non-traversed nodes;
and step 3: local path planning adopts an improved TEB algorithm;
step 3-1: the TEB algorithm optimizes the result of the global path planning, and the pose of the intelligent wheelchair is represented by three variables on a two-dimensional plane:
X i ＝(x i ,y i ,β i )
in the formula, x i ，y i Corresponding to the position of the intelligent wheelchair in the map coordinate system, beta i Corresponding to the postures of the intelligent wheelchair in the map coordinate system, the time elapsed between every two adjacent postures is delta T; the mathematical description of the objective function is:
wherein f (B) represents an objective function after synthesizing various constraint conditions, f k (B) Representing constraint functions of terms, r k As weights of the constraints, B * An optimal TEB trajectory;
step 3-2: improving a TEB algorithm, and optimizing the security by adopting a distance grading strategy;
the distance grading strategy takes the center of the robot and the distance between the robot and the obstacle as a radius d; when planning a path, dividing an existing area into 3 levels of areas according to the distance between the existing area and an obstacle by using a radius d: that is, the distance between the robot and the obstacle is divided into a collision area, a probability collision area and a safety area from near to far, and two distance parameters r are corresponding to 1 And r 2 ，r 1 <r 2 ；
When d is<＝r 1 When the navigation plan is generated, the area is taken as a collision area and does not appear in a path planned by navigation;
when r is 1 <d<＝r 2 Taking the area as a probability collision area, and taking a route passing through the area as an alternative route instead of a preferred route in navigation design;
when d is>r 2 When the route is selected, the route passing through the area is preferentially considered as a preferred route;
if the safety area or the route conflict does not exist, the probability collision area is considered as a secondary safety route, otherwise, the safety route is considered to be unavailable, and the robot stands still to wait for the obstacle to get away;
step 3-3: introducing an acceleration constraint to limit the change rate of the acceleration within a range; adding acceleration constraint to the hypergraph, and setting the acceleration change rate to be j 0 Definition of j 0 The acceleration constraints satisfied are as follows:
wherein j 0lin And j 0rot Are respectively j 0 The corresponding linear and angular acceleration rates,a 0lin 、a 0rot respectively represent a 0 Corresponding linear and angular accelerations, a 1lin 、a 1rot Respectively represent a 1 Corresponding linear and angular accelerations,. DELTA.T 0 、△T 1 、△T 2 Respectively representing the time intervals between adjacent poses;
limiting the variation range of linear acceleration and angular acceleration by combining acceleration constraint, and applying the constraint in the initial state, the intermediate state and the end state of local path planning;
step 3-4: optimizing a plurality of navigations by adopting a TEB + VO algorithm combination;
the VO algorithm eliminates the speed possibly occurring in the collision area, and selects the optimal speed not in the collision area; considering multiple moving objects, the VO algorithm changes the absolute motion of multiple objects to the relative motion of one object.
Preferably, the heuristic function h (n) is set to one of a manhattan distance, an euclidean distance, or a chebyshev distance.
Preferably, the heuristic function h (n) is set to a euclidean distance.
The invention has the following beneficial effects:
the invention optimizes the TEB algorithm from the three aspects of safety, stability and multi-navigation, so that the autonomous navigation system is safer and more stable and has better obstacle avoidance effect in the scene of a hospital, and an experience and thought are provided for solving the problem of construction of intelligent wheelchair facilities of the hospital.
Drawings
Fig. 1 is a flow chart of the algorithm a of the present invention.
FIG. 2 is a schematic diagram of the TEB algorithm of the present invention.
FIG. 3 shows the specific values of the parameters of the TEB algorithm of the present invention.
FIG. 4 is a schematic diagram of an experiment performed using an Rviz tool for mapping in an embodiment of the present invention.
FIG. 5 is a diagram illustrating a speed variation curve under a dynamic environment according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the drawings.
The invention aims to overcome the defects of the prior art in a hospital which is a specific complex scene, and designs a set of intelligent wheelchair autonomous navigation system which is suitable for the hospital which is the complex scene.
An intelligent wheelchair autonomous navigation method comprises the following steps:
step 1: the intelligent wheelchair autonomous navigation method comprises a global path planning part and a local path planning part;
and 2, step: the global path planning adopts an A-star algorithm;
as shown in fig. 1, the a-algorithm is a heuristic search algorithm, and compared to a blind search algorithm, i.e., breadth-first search and depth-first search, the a-algorithm is implemented by adding an evaluation function f (n):
f(n)＝g(n)+h(n)
the search is carried out towards a fixed direction, so that the search efficiency is higher and the speed is higher; the valuation function is often composed of some priori knowledge of the solved problem, such as the characteristics of the solution, the distribution rule of the solution, and the like; in f (n), g (n) represents the cost paid by searching the current node, a heuristic function h (n) represents the estimated cost from the current node to the target node, and the heuristic function h (n) and the target node are subjected to weighted evaluation to obtain an evaluation function; dividing the search nodes into two types, namely traversed nodes and non-traversed nodes;
and step 3: as shown in fig. 2, the local path planning adopts an improved TEB algorithm;
step 3-1: the TEB algorithm optimizes the result of the global path planning, and three variables are used for representing the pose of the intelligent wheelchair on a two-dimensional plane:
X i ＝(x i ,y i ,β i )
in the formula, x i ，y i Corresponding to the position of the intelligent wheelchair in the map coordinate system, beta i Corresponding to the postures of the intelligent wheelchair in the map coordinate system, the time elapsed between every two adjacent postures is delta T; the mathematical description of the objective function is:
wherein f (B) represents an objective function after synthesizing various constraint conditions, f k (B) Representing constraint functions of each item, r k As weights of the constraints, B * An optimal TEB trajectory;
step 3-2: improving a TEB algorithm, and optimizing safety by adopting a distance grading strategy;
the distance grading strategy takes the center of the robot and the distance between the robot and the obstacle as a radius d; when planning a path, dividing an existing area into 3 levels of areas according to the distance between the existing area and an obstacle by using a radius d: the distance between the robot and the barrier is divided into a collision area, a probability collision area and a safety area from near to far, and the two distance parameters r correspond to 1 And r 2 ，r 1 <r 2 ；
When d is<＝r 1 When the navigation plan is generated, the area is taken as a collision area and does not appear in a path planned by navigation;
when r is 1 <d<＝r 2 Taking the area as a probability collision area, and taking a route passing through the area as an alternative route instead of a preferred route in navigation design;
when d is>r 2 When the route is selected, the route passing through the area is preferentially considered as a preferred route;
if the safety area or the route conflict does not exist, the probability collision area is considered as a secondary safety route, otherwise, the safety route is considered to be unavailable, and the robot stands still to wait for the obstacle to get away;
step 3-3: introducing an acceleration constraint to limit the change rate of the acceleration within a range; adding acceleration constraint to the hypergraph, and setting the acceleration change rate to be j 0 Definition of j 0 Satisfy addition ofThe speed constraints are as follows:
wherein j 0lin And j 0rot Are respectively j 0 A corresponding linear acceleration rate and angular acceleration rate;
limiting the variation range of linear acceleration and angular acceleration by combining acceleration constraint, and applying the constraint in the initial state, the intermediate state and the end state of local path planning;
step 3-4: optimizing a plurality of navigations by adopting a TEB + VO algorithm combination;
the core of the VO algorithm is to eliminate the speed possibly occurring in the collision area and select the optimal speed not in the collision area; considering multiple moving objects, the VO algorithm changes the absolute motion of multiple objects to the relative motion of one object.
The specific embodiment is as follows:
the automatic navigation of the intelligent wheelchair in a hospital scene mainly relates to two aspects, namely planning a global path, wherein the main task of the part is to realize the selection of an optimal path from a starting point to a terminal point; and secondly, local path planning, which can help the intelligent wheelchair to automatically make a decision and plan the motion in the traveling process, and mainly comprises obstacle avoidance and speed and acceleration selection. Therefore, the global path planning and the local path planning are developed, and the simulation result is combined to perform final field test and experiment in the scene of a simulated hospital.
1. And (3) global path planning:
the algorithm adopted in the navigation design in terms of global path planning is the a-x algorithm. The a-algorithm is a heuristic search algorithm. Compared with the traditional blind search algorithm, namely breadth-first search and depth-first search, the A-algorithm adds an evaluation function:
f(n)＝g(n)+h(n)
the search is carried out towards a certain direction, and the search efficiency is higher and the speed is higher. Often, the valuation function consists of some a priori knowledge of the problem being solved, such as the characteristics of the solution, the distribution law of the solution, and so on. In f (n), g (n) represents the cost which is paid by searching the current node, h (n) represents the estimated cost from the current node to the target node, and the two are subjected to weighted evaluation to obtain the evaluation function. Furthermore, to distinguish whether a certain search node has traversed, nodes are typically divided into two classes, namely traversed nodes and non-traversed nodes and put into two sets named close and open.
Currently, in the field of mobile robots, a heuristic function h (n) is usually set to be one of a manhattan distance, an euclidean distance, or a chebyshev distance according to actual conditions, and a traditional euclidean distance is selected in the embodiment to perform an experiment.
2. Local path planning:
in the selection of local path planning, a Dynamic Window (DWA) algorithm is often applied to a conventional navigation system.
Because the DWA algorithm has the defect of poor obstacle avoidance effect, the DWA algorithm is replaced by a Time Elastic Band (TEB) algorithm, and optimization and improvement are made on the local path planning. The TEB algorithm optimizes the result of the global path planning, and three variables are used for representing the pose of the intelligent wheelchair on a two-dimensional plane:
X i ＝(x i ,y i ,β i )
in the formula, x i ，y i Corresponding to the position of the intelligent wheelchair in the map coordinate system, beta i Corresponding to the postures (namely angles) of the intelligent wheelchair in the map coordinate system, the elapsed time between every two adjacent postures is delta T; the idea of the TEB algorithm is similar to that of the graph optimization, and barrier information, discrete intervals of a planned track, and the approximation of adjacent time, space and the like should be fully considered in the process of moving the intelligent wheelchairAnd combining the time and pose sequences of the robot, and weighting the multi-objective optimization by using the influence degrees under different constraint conditions as weights to obtain the optimal path points to obtain the optimal solution. The mathematical description of the objective function is:
wherein f (B) represents an objective function after synthesizing various constraint conditions, f k (B) Representing constraint functions of terms, r k As weights of the constraints, B * An optimal TEB trajectory;
because the TEB algorithm has the advantages of supporting online real-time optimization, supporting the correction of the minimum steering radius and the heading and the like, the TEB algorithm is more suitable for an intelligent vehicle of an Ackerman model than the DWA algorithm, and can meet the speed requirement required by the design of a navigation system. And the TEB algorithm can exert the advantages of dynamic obstacle avoidance, and the intelligent wheelchair can have the characteristics of excellent obstacle avoidance effect and less time consumption in an indoor complex obstacle environment by combining with the actual occasions of a hospital, so that the selection of the TEB algorithm as a local path planning method is a better choice.
Therefore, the invention is divided into two parts in the aspect of autonomous navigation design: the intelligent wheelchair system for realizing autonomous navigation in a specific occasion is designed by using an A-algorithm as a global path planning algorithm and a TEB algorithm as a local path planning algorithm, so that the intelligent wheelchair system makes a certain contribution to solving the problems that the old and the patient in a hospital are difficult to move and lack of care, and humanized services of intelligent medical treatment and the hospital.
In the experimental process, firstly, site simulation is carried out by using software, and secondly, adjustment and repeated test of algorithm parameters are carried out in a scene of a simulated hospital in a field experiment to obtain a set of complete and relatively excellent autonomous navigation scheme, so that two main effects of realizing stable movement and safe obstacle avoidance can be achieved. And subsequently, some optimization concepts and improvement ideas are provided for an algorithm part in the navigation scheme, wherein the optimization concepts and the improvement ideas comprise safety optimization based on a distance grading strategy, stability optimization based on acceleration constraint and optimization among multiple navigations based on a speed obstacle algorithm (VO).
As shown in fig. 4, the intelligent wheelchair is simulated under a linux platform, a Gazebo physical simulation environment and a two-dimensional cost map are sequentially started in a simulation experiment, target points are respectively set for the intelligent vehicle simulating the intelligent wheelchair by using an Rviz visualization tool, wherein the global path planning algorithm of an original comparison group and the global path planning algorithm of an experiment group which are not improved use an a algorithm, and the global path planning algorithm of the comparison group uses a DWA algorithm as a local path planning algorithm for performing the experiment.
As shown in fig. 5, since the DWA algorithm cannot set the steering radius, the algorithm is optimized after considering a specific application scenario of the intelligent wheelchair, a TEB algorithm is adopted as a local path planning algorithm, field simulation is performed, and various parameters in the TEB algorithm are adjusted. Parameters of the TEB algorithm mainly include the following:
the motion track parameter, objectory, is a kind of parameter mainly aiming at the adjustment and planning of the motion track of the robot;
the kinematic parameters Robot are used for adjusting the control of speed, acceleration and the like in the motion process;
the obstacle parameter Obstacles is used for the position relation between the robot and the obstacle in the movement process;
optimization parameter Optimization is mainly used for optimizing parameter weights;
and (3) simulating a real scene in the field, and dynamically adjusting parameters by utilizing rqt. The intelligent vehicle can flexibly avoid static obstacles in the map obtained by scanning, and shows better performance in local path planning. However, due to the defects of the algorithm, the intelligent vehicle cannot plan a new route for a sudden dynamic obstacle in time and locally, so that the trolley can rotate in situ under partial conditions. More detailed parameter adjustment is carried out for the problem. As shown in fig. 3, the embodiment of the invention discloses specific numerical values of TEB algorithm parameters in an intelligent wheelchair autonomous navigation system in a complex environment.
Claims (3)
1. An intelligent wheelchair autonomous navigation method is characterized by comprising the following steps:
step 1: the intelligent wheelchair autonomous navigation method comprises a global path planning part and a local path planning part;
step 2: the global path planning adopts an A-star algorithm;
the a algorithm is a heuristic search algorithm, compared to the blind search algorithm-breadth-first search and depth-first search, by adding an evaluation function f (n):
f(n)＝g(n)+h(n)
making the search proceed towards a fixed direction; in f (n), g (n) represents the cost paid by searching the current node, a heuristic function h (n) represents the estimated cost from the current node to the target node, and the heuristic function h (n) and the target node are subjected to weighted evaluation to obtain an evaluation function; dividing the search nodes into two types, namely traversed nodes and non-traversed nodes;
and step 3: local path planning adopts an improved TEB algorithm;
step 3-1: the TEB algorithm optimizes the result of the global path planning, and the pose of the intelligent wheelchair is represented by three variables on a two-dimensional plane:
X i ＝(x i ,y i ,β i )
in the formula, x i ，y i Corresponding to the position of the intelligent wheelchair in the map coordinate system, beta i Corresponding to the postures of the intelligent wheelchair in the map coordinate system, the elapsed time between every two adjacent postures is delta T; the mathematical description of the objective function is:
wherein f (B) represents an objective function after synthesizing various constraint conditions, f k (B) Representing constraint functions of terms, r k As weights of the constraints, B * An optimal TEB trajectory;
step 3-2: improving a TEB algorithm, and optimizing the security by adopting a distance grading strategy;
the distance grading strategy takes the center of the robot and the distance between the robot and the obstacle as a radius d; when planning a path, dividing an existing area into 3 levels of areas according to the distance between the existing area and an obstacle by using a radius d: that is, the distance between the robot and the obstacle is divided into a collision area, a probability collision area and a safety area from near to far, and two distance parameters r are corresponding to 1 And r 2 ，r 1 <r 2 ；
When d is<＝r 1 When the navigation plan is generated, the area is taken as a collision area and does not appear in a path planned by navigation;
when r is 1 <d<＝r 2 Taking the area as a probability collision area, and taking a route passing through the area as an alternative route instead of a preferred route in the navigation design;
when d is>r 2 When the route is selected, the route passing through the area is preferentially considered as a preferred route;
if the safety area or the route conflict does not exist, the probability collision area is considered as a secondary safety route, otherwise, the safety route is considered to be unavailable, and the robot stands still to wait for the obstacle to get away;
step 3-3: introducing an acceleration constraint, and limiting the change rate of the acceleration to a range; adding acceleration constraint to the hypergraph, and setting the acceleration change rate as j 0 Definition of j 0 The acceleration constraints satisfied are as follows:
wherein j 0lin And j 0rot Are respectively j 0 Corresponding linear and angular acceleration rates, a 0lin 、a 0rot Respectively represent a 0 Corresponding linear and angular accelerations, a 1lin 、a 1rot Respectively represent a 1 Corresponding linear and angular accelerations,. DELTA.T 0 、△T 1 、△T 2 Respectively representing the time intervals between adjacent poses;
limiting the variation range of linear acceleration and angular acceleration by combining acceleration constraint, and applying the constraint in the initial state, the intermediate state and the end state of local path planning;
step 3-4: optimizing a plurality of navigations by adopting a TEB + VO algorithm combination;
the VO algorithm eliminates the speed possibly occurring in the collision area, and selects the optimal speed not in the collision area; considering multiple moving objects, the VO algorithm changes the absolute motion of multiple objects to the relative motion of one object.
2. The method of claim 1, wherein the heuristic function h (n) is set to one of a manhattan distance, an euclidean distance, or a chebyshev distance.
3. The intelligent wheelchair autonomous navigation method of claim 1, wherein the heuristic function h (n) is set to Euclidean distance.
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