CN115307636B - Autonomous navigation method of intelligent wheelchair - Google Patents
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention discloses an intelligent wheelchair autonomous navigation method, which comprises two parts, namely global path planning and local path planning; an A-type algorithm is used as a global path planning algorithm, and an elastic time Band algorithm (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 a certain contribution is made to the intelligent medical treatment and the humanized service of the hospital for solving the problems that the actions of the old and the patient in the hospital are difficult and the nursing is lacking.
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 walking tool and a medical instrument, and provides convenience for outpatients or inpatients with inconvenient actions, old and weak ages. At present, the necessary facility construction of the wheelchair in the hospital construction of China is still remained on the traditional manual wheelchair, and the wheelchair has the defects of low intelligence, poor safety and the like, so that the input of safer and more intelligent wheelchair construction in the public place of the hospital is very important. However, the current intelligent wheelchair research and development technology is not very mature in the aspect of navigation intelligence, and particularly, the technology for realizing path planning and navigation in a specific scene of a hospital lacks in the aspect of poor technology, so that the design of a navigation system of the intelligent wheelchair in the specific scene of the hospital is particularly important.
At present, the technology used by the intelligent wheelchair is the fusion of a brain-computer interface technology and a SLAM technology; the Beidou navigation positioning is adopted as global path planning information, and accurate walking is realized by combining with user operation to solve the hospital diagnosis guiding problem; the human-in-the-loop framework realizes real-time path modification and the like. The researches realize autonomous navigation of the wheelchair to a certain extent, but also have the problems of insufficient two-dimensional radar detection precision, error of Beidou positioning information, reduced navigation function in a complex environment, insufficient pertinence and the like. Therefore, the intelligent wheelchair self-help navigation system is designed mainly aiming at the problems of reduced navigation function, poor obstacle avoidance effect and poor path planning safety and stability in specific scenes (hospitals, nursing homes and the like), and performs certain exploration and optimization on the prior art.
The current path planning algorithm is mainly divided into two major categories, namely global path planning and local path planning, wherein the global path planning is mainly used for processing the situation that the environment is completely known, and the local path planning refers to the process of planning a path for avoiding an obstacle in real time by sensing surrounding environment information through a sensor of the local path planning and combining state information of the local path planning. The common global path planning algorithm is divided into: heuristic search algorithm, intelligent search algorithm, etc., the heuristic search algorithm is a college search algorithm comprising heuristic information, mainly utilize experience knowledge of specific scene to set up heuristic function to raise search efficiency, it represents A algorithm; the intelligent search algorithm comprises a genetic algorithm, a simulated annealing algorithm, an ant colony algorithm, a fish swarm algorithm and the like. Local path planning algorithms are currently in common use, such as the artificial field method (ARTIFICIAL POTENTIAL FIELD, APF) and the dynamic window method (Dynamic Windows Approach, DWA). However, the algorithms described above are each advantageous in terms of effectiveness, but simulation and application in a specific scene of a hospital lacks experimental support.
According to the mechanical characteristics of the intelligent wheelchair and the limit of surrounding environment on the wheelchair speed, the DWA algorithm forms a dynamic window to sample the speed, simulates the motion trail of a certain speed in a period of time in the future, scores the motion trail of all speeds through a given evaluation function, and selects the speed corresponding to the highest scoring trail in the trail set as the speed of the intelligent wheelchair at the next moment. The DWA algorithm has fewer factors considered in each calculation, has only the limitation of speed and acceleration, has small track space and short sampling time, and is simpler in calculation. However, due to the few factors considered by the DWA algorithm, the algorithm also has a plurality of obvious defects, and particularly realizes the two aspects of poor obstacle avoidance effect and poor prospective. Poor obstacle avoidance means that the DWA algorithm cannot set the steering radius, so collision is likely to occur; a prospective deficiency refers to the ease with which DWA algorithms can be trapped in locally optimal solutions. Therefore, the special application scene of the intelligent wheelchair in hospitals is considered, the intelligent wheelchair has the characteristics of large traffic, high uncertainty of the activity state and many burst factors, and the defect of poor obstacle avoidance effect can be exposed by adopting a 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 two parts, namely global path planning and local path planning; an A-type algorithm is used as a global path planning algorithm, and an elastic time Band algorithm (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 a certain contribution is made to the intelligent medical treatment and the humanized service of the hospital for solving the problems that the actions of the old and the patient in the hospital are difficult and the nursing is lacking.
The technical scheme adopted by the invention for solving the technical problems comprises the following steps:
Step 1: the intelligent wheelchair autonomous navigation method comprises two parts, namely global path planning and local path planning;
Step 2: the global path planning adopts an A-algorithm;
The algorithm a is a heuristic search algorithm, which is performed by adding a rating function f (n) compared to the blind search algorithm, breadth first search and depth first search:
f(n)＝g(n)+h(n)
So that the search proceeds toward a fixed direction; in f (n), g (n) represents the cost paid by searching the current node, the heuristic function h (n) represents the estimated cost from the current node to the target node, and the two are subjected to weighted evaluation to obtain a valuation function; dividing the search nodes into two types, namely traversed nodes and non-traversed nodes;
step 3: the local path planning adopts an improved TEB algorithm;
Step 3-1: the TEB algorithm optimizes the result of global path planning, and three variables are used for representing the pose of the intelligent wheelchair on a two-dimensional plane:
Xi＝(xi,yi,βi)
Wherein x i,yi corresponds to the position of the intelligent wheelchair in the map coordinate system, beta i corresponds to the posture of the intelligent wheelchair in the map coordinate system, and the elapsed time between every two adjacent postures is DeltaT; the mathematical description of the objective function is:
Wherein f (B) represents an objective function after integrating various constraint conditions, f k (B) represents various constraint functions, r k is the weight of various constraint conditions, and B * is the optimal TEB track;
step 3-2: improving the TEB algorithm, and optimizing the safety by adopting a distance grading strategy;
The distance grading strategy uses the center of the robot, and the distance between the robot and the obstacle is the radius d; in the path planning, the existing area is divided into 3 levels of areas according to the distance between the existing area and the obstacle by the radius d: namely, the distance between the robot and the obstacle is divided into a collision zone, a probability collision zone and a safety zone from near to far, and the distance corresponds to two distance parameters r 1 and r 2,r1<r2;
When d < = r 1, this region is taken as the collision zone and does not appear in the path planned by the navigation;
when r 1<d<＝r2, taking the area as a probability collision area, and taking a route passing through the area as an alternative route rather than a preferred route in the navigation design;
When d > r 2, taking the area as a safety area, and giving priority to the route passing through the area as a preferred route in route selection;
If no safety area or route conflict exists, taking the probability collision area as a secondary safety route, otherwise, considering that no safety route is available, and standing the robot for keeping away the obstacle;
Step 3-3: introducing acceleration constraint, and limiting the change rate of acceleration to a range; adding acceleration constraint to the hypergraph, setting the acceleration change rate as j 0, and defining the acceleration constraint condition satisfied by j 0 as follows:
Wherein j 0lin and j 0rot are the linear acceleration change rate and the angular acceleration change rate corresponding to j 0, respectively, a 0lin、a0rot represents the linear acceleration and the angular acceleration corresponding to a 0, a 1lin、a1rot represents the linear acceleration and the angular acceleration corresponding to a 1, respectively, and Δt 0、△T1、△T2 represents the time interval between adjacent poses, respectively;
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 termination state of local path planning;
Step 3-4: optimizing among a plurality of navigation by adopting 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 the multiple objects into the relative motion of one object.
Preferably, the heuristic function h (n) is set to one of a manhattan distance, a euclidean distance, or a chebyshev distance.
Preferably, the heuristic function h (n) is set to euclidean distance.
The beneficial effects of the invention are as follows:
According to the invention, the TEB algorithm is optimized from three angles 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 intelligent wheelchair facility construction of the hospital.
Drawings
Fig. 1 is a flowchart of the algorithm of the present invention.
Fig. 2 is a schematic diagram of the TEB algorithm of the present invention.
Fig. 3 is a specific numerical value of the TEB algorithm parameters of the present invention.
FIG. 4 is a schematic illustration of an experiment using Rviz tools to create a map in accordance with an embodiment of the present invention.
FIG. 5 is a schematic diagram of a velocity profile in a dynamic environment in an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
The invention aims to solve the defect of the prior art in a specific complex scene of a hospital, and designs an intelligent wheelchair autonomous navigation system suitable for the complex scene of the hospital.
An intelligent wheelchair autonomous navigation method comprises the following steps:
Step 1: the intelligent wheelchair autonomous navigation method comprises two parts, namely global path planning and local path planning;
Step 2: the global path planning adopts an A-algorithm;
As shown in fig. 1, the a-algorithm is a heuristic search algorithm, which is performed by adding a rating function f (n) compared to the blind search algorithm, breadth-first search and depth-first search:
f(n)＝g(n)+h(n)
The searching is conducted towards a fixed direction, so that the searching efficiency is higher and the searching speed is faster; the valuation function often consists of some priori knowledge of the problem being solved, such as the characteristics of the solution, the distribution rules of the solution, etc.; in f (n), g (n) represents the cost paid by searching the current node, the heuristic function h (n) represents the estimated cost from the current node to the target node, and the two are subjected to weighted evaluation to obtain a valuation function; dividing the search nodes into two types, namely traversed nodes and non-traversed nodes;
Step 3: as shown in fig. 2, the local path planning employs a modified TEB algorithm;
Step 3-1: the TEB algorithm optimizes the result of global path planning, and three variables are used for representing the pose of the intelligent wheelchair on a two-dimensional plane:
Xi＝(xi,yi,βi)
Wherein x i,yi corresponds to the position of the intelligent wheelchair in the map coordinate system, beta i corresponds to the posture of the intelligent wheelchair in the map coordinate system, and the elapsed time between every two adjacent postures is DeltaT; the mathematical description of the objective function is:
Wherein f (B) represents an objective function after integrating various constraint conditions, f k (B) represents various constraint functions, r k is the weight of various constraint conditions, and B * is the optimal TEB track;
step 3-2: improving the TEB algorithm, and optimizing the safety by adopting a distance grading strategy;
The distance grading strategy uses the center of the robot, and the distance between the robot and the obstacle is the radius d; in the path planning, the existing area is divided into 3 levels of areas according to the distance between the existing area and the obstacle by the radius d: namely, the distance between the robot and the obstacle is divided into a collision zone, a probability collision zone and a safety zone from near to far, and the distance corresponds to two distance parameters r 1 and r 2,r1<r2;
When d < = r 1, this region is taken as the collision zone and does not appear in the path planned by the navigation;
when r 1<d<＝r2, taking the area as a probability collision area, and taking a route passing through the area as an alternative route rather than a preferred route in the navigation design;
When d > r 2, taking the area as a safety area, and giving priority to the route passing through the area as a preferred route in route selection;
If no safety area or route conflict exists, taking the probability collision area as a secondary safety route, otherwise, considering that no safety route is available, and standing the robot for keeping away the obstacle;
Step 3-3: introducing acceleration constraint, and limiting the change rate of acceleration to a range; adding acceleration constraint to the hypergraph, setting the acceleration change rate as j 0, and defining the acceleration constraint condition satisfied by j 0 as follows:
Wherein j 0lin and j 0rot are the corresponding linear acceleration change rate and angular acceleration change rate of j 0, respectively;
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 termination state of local path planning;
Step 3-4: optimizing among a plurality of navigation by adopting TEB+VO algorithm combination;
The core of the VO algorithm is to reject 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 the multiple objects into the relative motion of one object.
Specific examples:
The automatic navigation of the intelligent wheelchair in the hospital scene mainly relates to two aspects, namely the planning of a global path, and the main task of the part is to realize the selection of an optimal path from a starting point to an end point; secondly, the local path planning can help the intelligent wheelchair to automatically make decisions and motion planning in the travelling process, and mainly comprises the avoidance of obstacles and the selection of speed and acceleration. Therefore, the two parts of the global path planning and the local path planning are unfolded, and the simulation result is combined to perform final field test and experiment in the scene of the simulated hospital.
1. Global path planning:
The algorithm adopted in the global path planning aspect in the navigation design is an a-algorithm. The a algorithm is a heuristic search algorithm. Compared to the traditional blind search algorithm, breadth-first and depth-first, the a algorithm works by adding a rating function:
f(n)＝g(n)+h(n)
The searching is conducted towards a certain direction, so that the searching efficiency is higher and the searching speed is higher. The valuation function often consists of some a priori knowledge of the problem being solved, such as the characteristics of the solution, the distribution law of the solution, etc. In f (n), g (n) represents the cost paid by searching the current node, h (n) represents the estimated cost from the current node to the target node, and the estimated cost are weighted and evaluated to obtain the valuation function. Furthermore, to distinguish whether a certain search node has been traversed, the nodes are typically divided into two categories, namely, nodes that have been traversed and nodes that have not been traversed, and put them into two sets named close and open.
Currently, in the field of mobile robots, according to actual situations, the heuristic function h (n) is generally set to one of a manhattan distance, a euclidean distance or a chebyshev distance, and in this embodiment, a conventional euclidean distance is selected for an experiment.
2. Local path planning:
in the selection of local path planning, conventional navigation systems are more often used with dynamic window (Dynamic Windows Approach, DWA) algorithms.
Because the DWA algorithm has the defect of poor obstacle avoidance effect, an elastic time Band algorithm (TIME ELASTIC Band, TEB) is selected to replace the DWA algorithm, and optimization and improvement are performed on local path planning. The TEB algorithm optimizes the result of global path planning, and three variables are used for representing the pose of the intelligent wheelchair on a two-dimensional plane:
Xi＝(xi,yi,βi)
Wherein x i,yi corresponds to the position of the intelligent wheelchair in the map coordinate system, beta i corresponds to the posture (i.e. angle) of the intelligent wheelchair in the map coordinate system, and the time elapsed between every two adjacent postures is DeltaT; the concept of the TEB algorithm is similar to that of the graph optimization, constraints of obstacle information, discrete intervals of a planned track, adjacent time, space and the like are fully considered in the advancing process of the intelligent wheelchair, the time and pose sequences of the robot are combined, and the influence degree under different constraint conditions is used as weight to weight the multi-objective optimization to obtain an optimal path point, so that an optimal solution is obtained. The mathematical description of the objective function is:
Wherein f (B) represents an objective function after integrating various constraint conditions, f k (B) represents various constraint functions, r k is the weight of various constraint conditions, and B * is the optimal TEB track;
Because the TEB algorithm has the advantages of supporting online real-time optimization, supporting the correction of the minimum steering radius and heading, and the like, the method is more suitable for intelligent vehicles of an Ackerman model than the DWA algorithm, and can also meet the speed requirement for the design of a navigation system. And the TEB algorithm can exert the advantages of dynamic obstacle avoidance, and combines with the practical occasions of hospitals, so that the intelligent wheelchair can have the characteristics of excellent obstacle avoidance effect and less time consumption in an indoor complex obstacle environment, and therefore, the TEB algorithm is selected as a better choice for a local path planning method.
Therefore, the invention is divided into two parts in the aspect of autonomous navigation design: the intelligent wheelchair system for realizing autonomous navigation in specific occasions is designed to solve the problems that the actions of the elderly and the patients in hospitals are difficult and lack of nursing, and to make a certain contribution to intelligent medical treatment and humanized service in hospitals.
In the experimental process, firstly, field simulation is carried out by using software, and secondly, algorithm parameters are adjusted and repeatedly tested in a scene of an analog hospital in the field experiment, so that a complete and relatively excellent autonomous navigation scheme is obtained, and two main effects of stable movement and safe obstacle avoidance can be achieved. And some optimization assumptions and improvement ideas are proposed in the algorithm section in the navigation scheme later, including security optimization based on distance grading strategy, stationarity optimization based on acceleration constraint, and optimization between multiple navigations based on speed obstacle algorithm (VO).
As shown in fig. 4, the intelligent wheelchair is simulated under the linux platform, the simulation experiment sequentially starts Gazebo a physical simulation environment and a two-dimensional cost map, and the Rviz visualization tool is utilized to set target points for the intelligent vehicle simulating the intelligent wheelchair, wherein the global path planning algorithms of the original control group and the experimental group which are not improved all use an a-type algorithm, and the DWA algorithm is used as a local path planning algorithm in the control group to perform the experiment.
As shown in fig. 5, since the DWA algorithm cannot set the steering radius, the algorithm is optimized in consideration of a specific application scenario of the intelligent wheelchair, and the TEB algorithm is adopted as a local path planning algorithm and is simulated in the field, so that parameters in the TEB algorithm are adjusted. The parameters of the TEB algorithm mainly include the following:
the motion trail parameter Trajectory is a type of parameter which is mainly aimed at the adjustment and planning of the motion trail of the robot;
The kinematic parameter Robot is used for adjusting the control of speed acceleration and the like in the motion process;
the obstacle parameter Obstacles is aimed at the position relation between the robot and the obstacle in the moving process;
the Optimization parameter Optimization is mainly used for optimizing the parameter weight;
And 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 locally in time for the suddenly-appearing dynamic obstacle, so that the trolley can turn in situ under partial conditions. Finer tuning was performed to address this problem. As shown in fig. 3, the embodiment of the invention discloses specific 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 two parts, namely global path planning and local path planning;
Step 2: the global path planning adopts an A-algorithm;
The algorithm a is a heuristic search algorithm, which is performed by adding a rating function f (n) compared to the blind search algorithm, breadth first search and depth first search:
f(n)＝g(n)+h(n)
So that the search proceeds toward a fixed direction; in f (n), g (n) represents the cost paid by searching the current node, the heuristic function h (n) represents the estimated cost from the current node to the target node, and the two are subjected to weighted evaluation to obtain a valuation function; dividing the search nodes into two types, namely traversed nodes and non-traversed nodes;
step 3: the local path planning adopts an improved TEB algorithm;
Step 3-1: the TEB algorithm optimizes the result of global path planning, and three variables are used for representing the pose of the intelligent wheelchair on a two-dimensional plane:
Xi＝(xi,yi,βi)
Wherein x i,yi corresponds to the position of the intelligent wheelchair in the map coordinate system, beta i corresponds to the posture of the intelligent wheelchair in the map coordinate system, and the elapsed time between every two adjacent postures is DeltaT; the mathematical description of the objective function is:
Wherein f (B) represents an objective function after integrating various constraint conditions, f k (B) represents various constraint functions, r k is the weight of various constraint conditions, and B * is the optimal TEB track;
step 3-2: improving the TEB algorithm, and optimizing the safety by adopting a distance grading strategy;
The distance grading strategy uses the center of the robot, and the distance between the robot and the obstacle is the radius d; in the path planning, the existing area is divided into 3 levels of areas according to the distance between the existing area and the obstacle by the radius d: namely, the distance between the robot and the obstacle is divided into a collision zone, a probability collision zone and a safety zone from near to far, and the distance corresponds to two distance parameters r 1 and r 2,r1<r2;
When d < = r 1, this region is taken as the collision zone and does not appear in the path planned by the navigation;
when r 1<d<＝r2, taking the area as a probability collision area, and taking a route passing through the area as an alternative route rather than a preferred route in the navigation design;
When d > r 2, taking the area as a safety area, and giving priority to the route passing through the area as a preferred route in route selection;
If no safety area or route conflict exists, taking the probability collision area as a secondary safety route, otherwise, considering that no safety route is available, and standing the robot for keeping away the obstacle;
Step 3-3: introducing acceleration constraint, and limiting the change rate of acceleration to a range; adding acceleration constraint to the hypergraph, setting the acceleration change rate as j 0, and defining the acceleration constraint condition satisfied by j 0 as follows:
Wherein j 0lin and j 0rot are the linear acceleration change rate and the angular acceleration change rate corresponding to j 0, respectively, a 0lin、a0rot represents the linear acceleration and the angular acceleration corresponding to a 0, a 1lin、a1rot represents the linear acceleration and the angular acceleration corresponding to a 1, respectively, and Δt 0、△T1、△T2 represents the time interval between adjacent poses, respectively;
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 termination state of local path planning;
Step 3-4: optimizing among a plurality of navigation by adopting 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 the multiple objects into the relative motion of one object.
2. The intelligent wheelchair autonomous navigation method of claim 1, wherein the heuristic function h (n) is set to one of a manhattan distance, a euclidean distance, or a chebyshev distance.
3. The autonomous navigation method of an intelligent wheelchair according to claim 1, wherein the heuristic function h (n) is set as euclidean distance.
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| 动态环境下智能轮椅的路径规划与导航;王丽军;王景川;陈卫东;;上海交通大学学报;20101128(第11期);全文 * |
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