Class AITstar
Defined in File AITstar.h
Inheritance Relationships
Base Type
public ompl::base::Planner(Class Planner)
Class Documentation
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class AITstar : public ompl::base::Planner
Adaptively Informed Trees (AIT*)
AIT* (Adaptively Informed Trees) is an almost-surely asymptotically optimal path planner. It aims to find an initial solution quickly and asymptotically converge to the globally optimal solution.
AIT* views the planning problem as the two subproblems of approximation and search, and approximates the free state space with an increasingly dense, edge-implicit random geometric graph (RGG), similar to BIT* and ABIT*.
AIT* uses an asymmetric bidirectional search that simultaneously estimates and exploits an accurate, adaptive heuristic that is specific to each problem instance. The reverse search (goal to start) does not perform collision detection on the edges and estimates cost-to-go values for all states in the RGG that are processed with the forward search (start to goal) which does perform collision detection. Because AIT* uses LPA* as the reverse search algorithm, the heuristic is admissible in the context of the current RGG and can efficiently be updated when the forward search detects a collision on an edge that is in the reverse search tree (as this indicates a flawed heuristic).
This implementation of AIT* can solve problems with multiple start and goal states and supports adding start and goal states while the planner is running. One can also turn off repairing the reverse search tree upon collision detection, which might be beneficial for problems in which collision detection is computationally inexpensive.
M. P. Strub, J. D. Gammell. “Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics” in Proceedings of the IEEE international conference on robotics and automation (ICRA), Paris, France, 31 May – 4 Jun. 2020.
- Associated publications:
arXiv: arXiv:2002.06589 DOI: ICRA40945.2020.9197338 Video 1: ICRA submission video. Video 2: ICRA presentation video
M. P. Strub, J. D. Gammell. “AIT* and EIT*: Asymmetric bidirectional sampling-based path planning” The International Journal of Robotics Research (IJRR), in revision, 2021.
arXiv: arXiv:2111.01877 Video 1: IJRR trailer Video 2: IJRR extension 1
Public Functions
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explicit AITstar(const ompl::base::SpaceInformationPtr &spaceInformation)
Constructs a AIT*.
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~AITstar() = default
Destructs a AIT*.
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virtual void setup() override
Additional setup that can only be done once a problem definition is set.
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ompl::base::PlannerStatus::StatusType ensureSetup()
Checks whether the planner is successfully setup.
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ompl::base::PlannerStatus::StatusType ensureStartAndGoalStates(const ompl::base::PlannerTerminationCondition &terminationCondition)
Checks whether the problem is successfully setup.
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virtual void clear() override
Clears the algorithm’s internal state.
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virtual ompl::base::PlannerStatus solve(const ompl::base::PlannerTerminationCondition &terminationCondition) override
Solves a motion planning problem.
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virtual void getPlannerData(base::PlannerData &data) const override
Get the planner data.
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void setBatchSize(std::size_t batchSize)
Set the batch size.
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std::size_t getBatchSize() const
Get the batch size.
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void setRewireFactor(double rewireFactor)
Set the rewire factor of the RGG graph.
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double getRewireFactor() const
Get the rewire factor of the RGG graph.
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void trackApproximateSolutions(bool track)
Set whether to track approximate solutions.
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bool areApproximateSolutionsTracked() const
Get whether approximate solutions are tracked.
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void enablePruning(bool prune)
Set whether pruning is enabled or not.
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bool isPruningEnabled() const
Get whether pruning is enabled or not.
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void setUseKNearest(bool useKNearest)
Set whether to use a k-nearest RGG connection model. If false, AIT* uses an r-disc model.
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bool getUseKNearest() const
Get whether to use a k-nearest RGG connection model. If false, AIT* uses an r-disc model.
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void setMaxNumberOfGoals(unsigned int numberOfGoals)
Set the maximum number of goals AIT* will sample from sampleable goal regions.
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unsigned int getMaxNumberOfGoals() const
Get the maximum number of goals AIT* will sample from sampleable goal regions.