CN106931970A - Robot security's contexture by self air navigation aid in a kind of dynamic environment - Google Patents
Robot security's contexture by self air navigation aid in a kind of dynamic environment Download PDFInfo
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Abstract
The invention provides robot security's contexture by self air navigation aid in a kind of dynamic environment, belong to robot navigation field；It is characterized in that mainly comprising the following steps：Dynamic barrier representation, representation and hypothesis, ASIPP algorithms, theory analysis and time limit figure represent 5 steps；SIPP is extended to search in real time by combining ARA* (real-time A* programmes) and running weight A* search capabilities with SIPP, the planning speed in extensive environment is considerably improved.In addition to real-time programme is provided, the guarantee theoretical to the integrality and suboptimality of solution cost is additionally provided；In practical operation, as a result the present invention show that the present invention can be to obtain initial solution in 0.05 second and can give the sufficient time reaching optimization lifting solution based on time range having on 50 huge maps of dynamic barrier planning time and parameterize path.
Description
Technical field
Navigated the invention belongs to robot security's contexture by self in robot navigation field, more particularly to a kind of dynamic environment
Method.
Background technology
The either motor vehicle or UAV of autonomous cruise, almost all of robot should all possess in people,
The ability of safety navigation from one place to another in face of the mobile individuality such as pet, automobile.In order to realize this
Function, the position that robot should in the recent period be likely to be to dynamic barrier carry out anticipation and simultaneously produce one section can
The path of collision free.Simultaneously as robot to the prediction of dynamic barrier track in lasting change, therefore machine
People is also required to plan these paths in the very short time.How in dynamic environment with limited time planning reliability
Path seem most important.Dynamic environment constitutes real-time constraint in time-domain planning.If a programme is needed
Want long time to obtain a new route, then robot is very possible and a mobile barrier collides.
Many processing methods to dynamic barrier be by they be modeled under initial launch track with cost high
Short window static-obstacle thing.The highly effective rate of this method, but there is suboptimality；Method also is only to exist
In the case of execution route is planned and get off to consider dynamic barrier (still by them using adjacent barrier avoiding method
Regard static-obstacle thing as), but stagnation and interim card may occurs in local small range；Another is to local programme
Alternative be that using Speed Obstacles thing, Speed Obstacles thing can determine and guide the control collided with moving obstacle,
Although this method is comparatively more accurate, it still can cause local small range problem；
There are certain methods to find path in the planning of comprehensive time-space.Silver ' s HCA* algorithms are for being multiple machines
Device people realizes planning, but is only applicable under little dynamic barrier situation, as the experimental result under SIPP methods,
And time that the algorithm is spent is more long than real-time time.SIPP is a kind of quick prioritization scheme under dynamic environment,
Because the quantity of the time step for each locus is unlimited, therefore the time is drawn in state space
It is relatively relatively slow point to be used as a clear and definite dimension.Time step is compressed into adjacent safe time interval by SIPP
Index.Therefore for each safe time interval and each single state of locus only one of which.
Canbe used on line planned with the time and while need treatment dynamic barrier be very difficult because secondary
The continuous demands of planning force to form strict constraints in the execution cycle.For clear and definite real-time constraints, one
Series method be used for sacrifice its suboptimality carry out guaranteed efficiency.RRT-variants can process more complex machines people
The lower high-dimensional search space of dynamic motion constraint is realized quickly planning.However, these are based on the method for sampling not
Ensure that striven for optimization.RRT* is a kind of method for being whenever based on sampling in the recent period, and this method is in limit
Prioritization scheme is can be derived that under the conditions of system.However, at the end of planning time, this algorithm can not be provided on this
Scheme has many suitable any relevant informations on earth, and on the other hand, for each solution, real-time search strategy exists
Suboptimality compared with prioritization scheme under have the upper bound of numerical value.
The content of the invention
It is an object of the invention to be directed to defect present in above-mentioned technology, it is proposed that robot peace in a kind of dynamic environment
Full contexture by self air navigation aid.Searched by combining ARA* (real-time A* programmes) and running weight A* with SIPP
SIPP is extended to search in real time by Suo Nengli, on real-time SIPP programmes, is taken from event horizon grid
Time range.Significantly improve robot and plan speed in extensive environment；The present invention is except providing planning side in real time
Outside case, the guarantee theoretical to the integrality and suboptimality of solution cost is additionally provided.
To achieve these goals, the technical scheme that the present invention is provided is as follows：In a kind of dynamic environment robot security from
Master program air navigation aid, it is characterised in that comprise the following steps：
Step 1, dynamic barrier representation：Represented with specific method in environment dynamic barrier and it is unified this
Represent；
The ad hoc approach is：Assuming that in the presence of another can in tracking environmental dynamic barrier system, the system deposits
The spheroid of movement locus and radius is viewed as having in a series of dynamic barriers and each barrier, a track is
Be made up of a series of point, and each point have show its constitute and the time state variable, point in these tracks according to
Time sequencing is arranged, and is predicted the movement locus of following a period of time barrier and is carried out by reading the order of these points
Unified representation；
Step 2, representation and hypothesis：Describe how to represent and explain the path planning algorithm of robot in the step；
Step 2.1, describe robot steric configuration in the environment and method for expressing, it is assumed that planning problem is with figure
To represent, each quantity of state s in figure is made up of two parts：The steric configuration vector of robot is denoted as x (s) and mark
Amount safe time interval index is denoted as interval (s)；
Step 2.2, define series of parameters of the robot during search path：Including determining for quantity of state s
Justice, during the search path of robot, each quantity of state s has a variable to be denoted as g (s), this variable be from
The cost that original state is spent to the most famous path of s states；
Heuristic function h (s) is that the cost from s state to dbjective state is estimated, it is assumed that this heuristic function is one
Constant, it means that it is not more than the cost of point of destination and meets triangle inequality；
State changes spent time cost c (s, s '), from s states to the transformation of one of succession state s ' or
Boundary cost is denoted as c (s, s ')；
Step 2.3, show that ASIPP algorithms and SIPP possess identical supposed premise：C (s, s ') it is equivalent to from s states
Action to s ' states performs the time；
Step 3, ASIPP algorithms：ASIPP algorithms are ARA* algorithms and the combination both SIPP algorithms and carry out one
A little extensions and the algorithm of modification, and then propose two algorithms；
Step 3.1, algorithm 1 are the bulk circulations of ARA* algorithms and draw the suboptimality of solution resulting at present
Scope；
The initial ranging mechanism of the initial parameter ε that step 3.1.1, initial display user provide, the value that ε is reduced afterwards (is used
Family can be with the value of self-defined reduction) and rerun search until reach optimal solution (ε=1)；
Step 3.1.2, in each search procedures of step 3.1.1, the cost that INCONS lists can be found is lower
The state in path is filled up, due to they very close to therefore will not be used to extend again；
The state of the lower path of cost described in step 3.1.3, step 3.1.2 needs the quilt in search iteration next time
Extend again, meanwhile, these states for being placed into OPEN lists have an opportunity to be expanded in search iteration next time,
The value of ε ' is finally calculated, the suboptimality scope of the solution for being found at present is drawn.
Step 3.2, algorithm 2 are the weighted A* search mechanisms individually realized under SIPP；
Step 3.2.1, generally in weighted A*, in the state of OPEN lists, there is the state of minimum f values
The f values for having Least-cost in OPEN lists until desired value are expanded, the f values of a state s are g (s) and h (s)
A function, when s is in OPEN lists, play the priority of extended mode s；
When step 3.2.2, a state s expansion process start, all possible mobile primitive of iterative cycles herein, this
A little mobile primitives can be used in the case where not collided with static environment, and variable x ' is after moving m
Residing steric configuration at the end of robot after to steric configuration x (s) of state s；
Step 3.2.3, create minimum and maximum robot can based on it can leave personal distance s it is earliest and
Number of times the latest reaches x ' number of times；
Step 3.2.4, algorithm 2 are to be operated by one etc., and m is then moved again, follow-up as early as possible by one
Time is put on each personal distance of x '；
Step 4, theory analysis：Mainly by proposing two variable come the integrality and optimality of verification algorithm；
Step 4.1, theorem 1：Whenever SIPP algorithms are complete, and at the end of algorithm, it is for dynamic barrier
Thing and static-obstacle thing is hindered to return a safe distance to impact point respectively, with the proviso that such paths are deposited
It is that the figure for giving planning problem is represented；
Step 4.2, theorem 2：At the end of planning time, by the way that whenever the result that SIPP algorithms are returned is spent
The cost of expense will not be bigger than the cost that ε ' times of optimal solution is spent；
Step 5, time limit figure are represented：In programme is in dynamic or uncertain environment, it generally will not be
Depend on the barrier behavior of prediction in following long time, time limit figure be namely based on above-mentioned theory be created that one by
The figure of two states composition：One state is determined by structure and time interval, i.e., high-dimensional state；Another shape
State only determines by steric configuration, i.e. low dimensional state.
It is also conceivable in described step 5 with SIPP and whenever SIPP puts time limit expression way based on peace into
In graphical representation method between the whole district, the SIPP of the corresponding duration for blocking dynamic barrier prediction locus is limited
In time range reaching the purpose no more than time range.
The present invention has the advantages that：
1) present invention is by combining ARA* (real-time A* programmes) and running weight A* search capability handles with SIPP
SIPP is extended to search in real time, on real-time SIPP programmes, time range is taken from event horizon grid；
State after time range is used for being planned for their space coordinates, when only existing robot due to time dimension
Between near, therefore by this way in extensive environment plan speed can be significantly improved.
2) present invention additionally provides the integrality and suboptimum to solution cost in addition to providing real-time programme
Property theoretical guarantee；In practical operation, invention demonstrates a method programme in UAV field real-time capacity,
There is on 50 huge maps of dynamic barrier planning time and parameterizing path, as a result showing that the present invention being capable of base
It is to obtain initial solution in 0.05 second and can give time of abundance making lifting solution in time range
Its being optimal.
Brief description of the drawings
Fig. 1 is the flow chart of robot security's contexture by self air navigation aid in dynamic environment of the present invention.
Specific embodiment
The present invention is described further below in conjunction with the accompanying drawings.
A kind of robot security's contexture by self air navigation aid in dynamic environment, the method is comprised the following steps：Such as accompanying drawing 1
It is shown, step 1, dynamic barrier representation：Represented with specific method in environment dynamic barrier and it is unified this
One represents；Assume to exist in the present invention another can in tracking environmental dynamic barrier system, the system has one
Serial dynamic barrier and each barrier is viewed as having the spheroid of movement locus and radius, a track is by one
The point composition of series, and each point has the state variable for showing that it was constituted with the time, the point in these tracks is according to the time
Order is arranged, and is predicted the movement locus of following a period of time barrier and is unified by reading the order of these points
Represent；
Step 2, representation and hypothesis：Describe how to represent and explain the path planning algorithm of robot in the step；
Step 2.1, describe robot steric configuration in the environment and method for expressing, it is assumed that planning problem is with figure
To represent, each quantity of state s in figure is made up of two parts：The steric configuration vector of robot is denoted as x (s) and mark
Amount safe time interval index is denoted as interval (s)；Continuous safe time interval index refers to without dynamic in a period of time
The time interval of the ad-hoc location of barrier.For example, if only one of which dynamic barrier is by ad-hoc location x (s),
So there was only two safe time intervals herein (before and after barrier passes through).This means only existing two shapes
State has the value of x (s), and a state is interval (s)=0, and another is interval (s)=1.The x (s) given for one
Time step, this is significantly less than many state copies.Edge between state is the short motion of connection correspondence configuration, base
In the expression way of configuration space, these be probably suitable for dynamic motion mobile primitive or have constant curvature
The simple fragment of curve.
Step 2.2, define series of parameters of the robot during search path：Including determining for quantity of state s
Justice, during the search path of robot, each quantity of state s has a variable to be denoted as g (s), this variable be from
The cost that original state is spent to the most famous path of s states；
Heuristic function h (s) is that the cost from s state to dbjective state is estimated, it is assumed that this heuristic function is one
Constant, it means that it is not more than the cost of point of destination and meets triangle inequality；
State changes spent time cost c (s, s '), from s states to the transformation of one of succession state s ' or
Boundary cost is denoted as c (s, s ')；
Step 2.3, show that ASIPP algorithms and SIPP possess identical supposed premise：C (s, s ') it is equivalent to from s states
Action to s ' states performs the time；
Step 3, ASIPP algorithms：ASIPP algorithms are ARA* algorithms and the combination both SIPP algorithms and carry out one
A little extensions and the algorithm of modification；
And then two algorithms are proposed；
Step 3.1, algorithm 1 are the bulk circulations of ARA* algorithms and draw the suboptimality of solution resulting at present
Scope；User provides a value of initial ε and calls ImprovePath () functional based method of algorithm 2 first first,
Followed by ε '=min (ε, g (sgoal)/mins ∈ OPEN ∪ INCONS (g (s)+h (s))), the value of corresponding ε ' is calculated
And current suboptimal design ε ' is issued, wherein, ε ' can draw the suboptimality scope in the solution for being found at present；
If the value of ε is more than 1, then the value of ε is reduced with user-defined value, in circulating herein, by state from INCONS
List be transferred to OPEN lists simultaneously for all OPEN lists in all of s ∈ OPEN according to the value of f (s) more
New stateful priority, and these states for being placed into OPEN lists may be in next iteration by again
Extension, when ε=1, whole loop body terminates.
Step 3.2, ImprovePath (algorithm 2) are a weighted A* search individually realized under SIPP
Mechanism；Generally in weighted A*, there is the state of minimum f values to be used to expand in the state in OPEN lists
Exhibition has the f values of Least-cost until desired value in OPEN lists.The f values of one state s are the one of g (s) and h (s)
Individual function, when s is in OPEN lists, it being capable of preferentially extended mode s, the first all possible shifting of iterative cycles
Dynamic primitive, these movement primitives can be used in the case where not collided with static environment；Variable x ' be into
Residing steric configuration at the end of robot after the mobile m to steric configuration x (s) of state s of row.
Algorithm 2 then create minimum and maximum robot can based on it can leave personal distance s earliest with
Number of times the latest reaches x ' number of times；Then algorithm 2 follow-up is put on the every of x ' by one in the time as early as possible
Individual personal distance, its implementation is to be operated using one etc., and m is then moved again, and (stand-by period can be for 0 therefore
It can be moved suitable for robot at once).
In order to ensure to work as ε>The suboptimum of integrality when 1 is defined, algorithm to each state use two versions, it is optimal and
Suboptimum, if the state of extension is optimal, then for each follow-up (s ' and s "), it can generate two identicals
Version：Optimal variable O (s ') is set as very, and another has O (s ") to be set as vacation；If the state of extension is suboptimum,
So it ensure that more suboptimum states, and suboptimum state is generally placed into OPEN and arranges with f (s ')=g (s ')+ε * h (s ')
In table, and optimum state is placed into OPEN lists with f (s ')=ε * (g (s ')+h (s ')).OPEN lists are saved
Optimal extended order (because f and scalar multiplication) in optimum state, but also there is the optimum state by after to be put into
It is related to sub-optimal state in OPEN lists, because being both extended rather than one.This means when many
Between in the case of, only suboptimum state is expanded, and it is right that such result causes enough optimum states to be extended for
Weighted A* search for desired theories integration and ensure (suboptimality complete and define).
Step 4, theory analysis：Mainly by proposing two variable come the integrality and optimality of verification algorithm；
Step 4.1, theorem 1：Whenever SIPP algorithms are complete, and at the end of algorithm, it is for dynamic barrier
Thing and static-obstacle thing is hindered to return a safe distance to impact point respectively, with the proviso that such paths are deposited
It is that the figure for giving planning problem is represented；
Step 4.2, theorem 2：At the end of planning time, by the way that whenever the result that SIPP algorithms are returned is spent
The cost of expense will not be bigger than the cost that ε ' times of optimal solution is spent；
Step 5, time limit figure are represented：In programme is in dynamic or uncertain environment, it generally will not be
Depend on the barrier behavior of prediction in following long time, time limit figure be namely based on above-mentioned theory be created that one by
The figure of two states composition：One state is determined by structure and time interval, i.e., high-dimensional state；Another shape
State only determines by steric configuration, i.e. low dimensional state.High-dimensional state is appeared near robot, and these states can only
Reached from robotary in given time range.In time limit grid chart, time step is equal with time range
State can be transformed into the not state including the time, i.e. low dimensional state.Therefore, once time step reaches given
Time range, state will become " atemporal ".
It is also conceivable in this step 5 with SIPP and whenever SIPP puts time limit expression way based on safety into
In interval graphical representation method, in the SIPP of the limitation corresponding duration for blocking dynamic barrier prediction locus
Time range is reaching the purpose no more than time range.Therefore, under SIPP and any SIPP, finite time model
The realization enclosed is realized by blocking the duration of the prediction locus of dynamic barrier.
Whenever one of programme it is important the characteristics of be that it can soon obtain initial solution.When us
When changing initial ε values and time range, algorithm obtains the time that initial solution is spent altogether.The ginseng of table 1
Number be by time range SIPP different for 4 kinds after the computing more than 40 times and whenever the planning time of SIPP
Distribution, these results are all to obtain the time that initial solution is spent using optimal SIPP (ε=1).From table 1
In, can be apparent draw whenever SIPP provide planning time two orders of magnitude at least fewer than optimal SIPP.
The result of table 1 shows：In order to be quickly obtained a solution, search for having weight be it is essential,
What is omitted in the present invention is the quantity of extended mode, because this reflection is planning time (the only product of scalar).
If the time needed for programme will be than more than this, the present invention will improve solution using ARA* algorithms
Quality.In this experiment, suboptimality scope ε reduces 0.2 in continuous ARA* algorithm iterations.Once ε
Value reach 1, solution is proved to be optimal.
SIPP the and ASIPP planning times of table 1 are distributed
Presently preferred embodiments of the present invention is the foregoing is only, is not intended to limit the invention, it is all in essence of the invention
Any modification, equivalent and improvement for being made within god and principle etc., should be included in protection scope of the present invention
Within.
Claims (2)
1. robot security's contexture by self air navigation aid in a kind of dynamic environment, it is characterised in that comprise the following steps：
Step 1, dynamic barrier representation：Represented with specific method in environment dynamic barrier and it is unified this
Represent；
The ad hoc approach is：Assuming that in the presence of another can in tracking environmental dynamic barrier system, the system deposits
The spheroid of movement locus and radius is viewed as having in a series of dynamic barriers and each barrier, a track is
Be made up of a series of point, and each point have show its constitute and the time state variable, point in these tracks according to
Time sequencing is arranged, and is predicted the movement locus of following a period of time barrier and is carried out by reading the order of these points
Unified representation；
Step 2, representation and hypothesis：Describe how to represent and explain the path planning algorithm of robot in the step；
Step 2.1, describe robot steric configuration in the environment and method for expressing, it is assumed that planning problem is with figure
To represent, each quantity of state s in figure is made up of two parts：The steric configuration vector of robot is denoted as x (s) and mark
Amount safe time interval index is denoted as interval (s)；
Step 2.2, define series of parameters of the robot during search path：Including determining for quantity of state s
Justice, during the search path of robot, each quantity of state s has a variable to be denoted as g (s), this variable be from
The cost that original state is spent to the most famous path of s states；
Heuristic function h (s) is that the cost from s state to dbjective state is estimated, it is assumed that this heuristic function is one
Constant, it means that it is not more than the cost of point of destination and meets triangle inequality；
State changes spent time cost c (s, s '), from s states to the transformation of one of succession state s ' or
Boundary cost is denoted as c (s, s ')；
Step 2.3, show that ASIPP algorithms and SIPP possess identical supposed premise：C (s, s ') it is equivalent to from s states
Action to s ' states performs the time；
Step 3, ASIPP algorithms：ASIPP algorithms are ARA* algorithms and the combination both SIPP algorithms and carry out one
A little extensions and the algorithm of modification, and then propose two algorithms；
Step 3.1, algorithm 1 are the bulk circulations of ARA* algorithms and draw the suboptimality of solution resulting at present
Scope；
The initial ranging mechanism of the initial parameter ε that step 3.1.1, initial display user provide, the value that ε is reduced afterwards (is used
Family can be with the value of self-defined reduction) and rerun search until reach optimal solution (ε=1)；
Step 3.1.2, in each search procedures of step 3.1.1, the cost that INCONS lists can be found is lower
The state in path is filled up, due to they very close to therefore will not be used to extend again；
The state of the lower path of cost described in step 3.1.3, step 3.1.2 needs the quilt in search iteration next time
Extend again, meanwhile, these states for being placed into OPEN lists have an opportunity to be expanded in search iteration next time,
The value of ε ' is finally calculated, the suboptimality scope of the solution for being found at present is drawn.
Step 3.2, algorithm 2 are the weighted A* search mechanisms individually realized under SIPP；
Step 3.2.1, generally in weighted A*, in the state of OPEN lists, there is the state of minimum f values
The f values for having Least-cost in OPEN lists until desired value are expanded, the f values of a state s are g (s) and h (s)
A function, when s is in OPEN lists, play the priority of extended mode s；
When step 3.2.2, a state s expansion process start, all possible mobile primitive of iterative cycles herein, this
A little mobile primitives can be used in the case where not collided with static environment, and variable x ' is after moving m
Residing steric configuration at the end of robot after to steric configuration x (s) of state s；
Step 3.2.3, create minimum and maximum robot can based on it can leave personal distance s it is earliest and
Number of times the latest reaches x ' number of times；
Step 3.2.4, algorithm 2 are to be operated by one etc., and m is then moved again, follow-up as early as possible by one
Time is put on each personal distance of x '；
Step 4, theory analysis：Mainly by proposing two variable come the integrality and optimality of verification algorithm；
Step 4.1, theorem 1：Whenever SIPP algorithms are complete, and at the end of algorithm, it is for dynamic barrier
Thing and static-obstacle thing is hindered to return a safe distance to impact point respectively, with the proviso that such paths are deposited
It is that the figure for giving planning problem is represented；
Step 4.2, theorem 2：At the end of planning time, by the way that whenever the result that SIPP algorithms are returned is spent
The cost of expense will not be bigger than the cost that ε ' times of optimal solution is spent；
Step 5, time limit figure are represented：In programme is in dynamic or uncertain environment, it generally will not be
Depend on the barrier behavior of prediction in following long time, time limit figure be namely based on above-mentioned theory be created that one by
The figure of two states composition：One state is determined by structure and time interval, i.e., high-dimensional state；Another shape
State only determines by steric configuration, i.e. low dimensional state.
2. robot security's contexture by self air navigation aid in dynamic environment according to claim 1, its feature exists
In：It is also conceivable in described step 5 with SIPP and whenever SIPP puts time limit expression way based on peace into
In graphical representation method between the whole district, the SIPP of the corresponding duration for blocking dynamic barrier prediction locus is limited
In time range reaching the purpose no more than time range.
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