Class STRIDE
Defined in File STRIDE.h
Nested Relationships
Nested Types
Inheritance Relationships
Base Type
public ompl::base::Planner
(Class Planner)
Class Documentation
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class STRIDE : public ompl::base::Planner
Search Tree with Resolution Independent Density Estimation.
- Short description
STRIDE (Search Tree with Resolution Independent Density Estimation) is a tree-based motion planner that attempts to detect the less explored area of the space through the use of a GNAT nearest-neighbor data structure. It is similar to EST, but unlike the EST implementation in OMPL does not require a projection. However, in case the state space has many dimensions, a projection can be specified and the GNAT can be built using distances in the projected space. This has the advantage over the EST implementation that no grid cell sizes have to be specified.
- External documentation
B. Gipson, M. Moll, and L.E. Kavraki, Resolution independent density estimation for motion planning in high-dimensional spaces, in IEEE Intl. Conf. on Robotics and Automation, pp. 2429-2435, 2013. [PDF]
Public Functions
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STRIDE(const base::SpaceInformationPtr &si, bool useProjectedDistance = false, unsigned int degree = 16, unsigned int minDegree = 12, unsigned int maxDegree = 18, unsigned int maxNumPtsPerLeaf = 6, double estimatedDimension = 0.0)
Constructor.
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~STRIDE() override
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virtual void setup() override
Perform extra configuration steps, if needed. This call will also issue a call to ompl::base::SpaceInformation::setup() if needed. This must be called before solving.
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virtual base::PlannerStatus solve(const base::PlannerTerminationCondition &ptc) override
Function that can solve the motion planning problem. This function can be called multiple times on the same problem, without calling clear() in between. This allows the planner to continue work for more time on an unsolved problem, for example. If this option is used, it is assumed the problem definition is not changed (unpredictable results otherwise). The only change in the problem definition that is accounted for is the addition of starting or goal states (but not changing previously added start/goal states). If clearQuery() is called, the planner may retain prior datastructures generated from a previous query on a new problem definition. The function terminates if the call to ptc returns true.
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virtual void clear() override
Clear all internal datastructures. Planner settings are not affected. Subsequent calls to solve() will ignore all previous work.
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inline void setGoalBias(double goalBias)
In the process of randomly selecting states in the state space to attempt to go towards, the algorithm may in fact choose the actual goal state, if it knows it, with some probability. This probability is a real number between 0.0 and 1.0; its value should usually be around 0.05 and should not be too large. It is probably a good idea to use the default value.
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inline double getGoalBias() const
Get the goal bias the planner is using.
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inline void setUseProjectedDistance(bool useProjectedDistance)
Set whether nearest neighbors are computed based on distances in a projection of the state rather distances in the state space itself.
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inline bool getUseProjectedDistance() const
Return whether nearest neighbors are computed based on distances in a projection of the state rather distances in the state space itself.
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inline void setDegree(unsigned int degree)
Set desired degree of a node in the GNAT.
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inline unsigned int getDegree() const
Get desired degree of a node in the GNAT.
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inline void setMinDegree(unsigned int minDegree)
Set minimum degree of a node in the GNAT.
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inline unsigned int getMinDegree() const
Get minimum degree of a node in the GNAT.
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inline void setMaxDegree(unsigned int maxDegree)
Set maximum degree of a node in the GNAT.
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inline unsigned int getMaxDegree() const
Set maximum degree of a node in the GNAT.
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inline void setMaxNumPtsPerLeaf(unsigned int maxNumPtsPerLeaf)
Set maximum number of elements stored in a leaf node of the GNAT.
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inline unsigned int getMaxNumPtsPerLeaf() const
Get maximum number of elements stored in a leaf node of the GNAT.
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inline void setEstimatedDimension(double estimatedDimension)
Set estimated dimension of the free space, which is needed to compute the sampling weight for a node in the GNAT.
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inline double getEstimatedDimension() const
Get estimated dimension of the free space, which is needed to compute the sampling weight for a node in the GNAT.
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inline void setRange(double distance)
Set the range the planner is supposed to use.
This parameter greatly influences the runtime of the algorithm. It represents the maximum length of a motion to be added in the tree of motions.
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inline double getRange() const
Get the range the planner is using.
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inline void setMinValidPathFraction(double fraction)
When extending a motion, the planner can decide to keep the first valid part of it, even if invalid states are found, as long as the valid part represents a sufficiently large fraction from the original motion. This function sets the minimum acceptable fraction (between 0 and 1).
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inline double getMinValidPathFraction() const
Get the value of the fraction set by setMinValidPathFraction()
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inline void setProjectionEvaluator(const base::ProjectionEvaluatorPtr &projectionEvaluator)
Set the projection evaluator. This class is able to compute the projection of a given state.
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inline void setProjectionEvaluator(const std::string &name)
Set the projection evaluator (select one from the ones registered with the state space).
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inline const base::ProjectionEvaluatorPtr &getProjectionEvaluator() const
Get the projection evaluator.
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virtual void getPlannerData(base::PlannerData &data) const override
Get information about the current run of the motion planner. Repeated calls to this function will update data (only additions are made). This is useful to see what changed in the exploration datastructure, between calls to solve(), for example (without calling clear() in between).
Protected Functions
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void freeMemory()
Free the memory allocated by this planner.
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void setupTree()
Initialize GNAT data structure.
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inline double distanceFunction(const Motion *a, const Motion *b) const
Compute distance between motions (actually distance between contained states)
Protected Attributes
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base::ValidStateSamplerPtr sampler_
Valid state sampler.
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base::ProjectionEvaluatorPtr projectionEvaluator_
This algorithm can optionally use a projection to guide the exploration.
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boost::scoped_ptr<NearestNeighborsGNAT<Motion*>> tree_
The exploration tree constructed by this algorithm.
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double goalBias_ = {.05}
The fraction of time the goal is picked as the state to expand towards (if such a state is available)
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double maxDistance_ = {0.}
The maximum length of a motion to be added to a tree.
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bool useProjectedDistance_
Whether to use distance in the projection (instead of distance in the state space) for the GNAT.
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unsigned int degree_
Desired degree of an internal node in the GNAT.
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unsigned int minDegree_
Minimum degree of an internal node in the GNAT.
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unsigned int maxDegree_
Maximum degree of an internal node in the GNAT.
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unsigned int maxNumPtsPerLeaf_
Maximum number of points stored in a leaf node in the GNAT.
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double estimatedDimension_
Estimate of the local dimensionality of the free space around a state.
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double minValidPathFraction_ = {.2}
When extending a motion, the planner can decide to keep the first valid part of it, even if invalid states are found, as long as the valid part represents a sufficiently large fraction from the original motion. This is used only when extendWhileValid_ is true.
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class Motion
The definition of a motion.
Public Functions
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Motion() = default
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inline Motion(const base::SpaceInformationPtr &si)
Constructor that allocates memory for the state.
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~Motion() = default
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Motion() = default