AIT* and EIT*: Asymmetric bidirectional sampling-based path planning

  title={AIT* and EIT*: Asymmetric bidirectional sampling-based path planning},
  author={Marlin P. Strub and Jonathan D. Gammell},
Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their search with problem-specific knowledge expressed as heuristics and can be orders of magnitude more efficient than uninformed algorithms. Heuristics are most effective when they are both accurate and computationally inexpensive to evaluate, but these are often conflicting characteristics. This makes the selection of appropriate… 

BiAIT*: Symmetrical Bidirectional Optimal Path Planning with Adaptive Heuristic

This article extends AIT* from the asymmetric biddirectional search to the symmetrical bidirectional search, namely BiAIT*.

Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning

The TMIT* algorithm is presented, an optimal TMP algorithm that combines results from makespan-optimal task planning and almost-surely asymptotically optimal motion planning that allows it to solve problems quickly and then converge towards the optimal solution with additional computational time.

BITKOMO: Combining Sampling and Optimization for Fast Convergence in Optimal Motion Planning

A new planner is introduced, BITKOMO, which integrates the asymptotically optimal Batch Informed Trees (BIT*) planner with the K-Order Markov Optimization (KomO) trajectory optimization framework and outperforms BIT* in terms of convergence to the optimal solution.

Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort

EIRM* uses an asymmetric bidirectional search to identify existing paths that may help solve an individual planning query and then uses this information to order its search and reduce computational effort, which allows it to find initial solutions up to an order ofmagnitude faster than state-of-the-art planning algorithms on the tested abstract and robotic multiquery planning problems.

The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning

The Surface Edge Explorer is presented, a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures that can attain better surface coverage in less computational time and sensor travel distance than evaluated volumetric approaches on both small- and large-scale scenes.

Autonomous Aerial Mapping and its Applications for Emergency Response

  • Rowan BorderJ. Gammell
  • Mathematics
    2022 Workshop on Cyber Physical Systems for Emergency Response (CPS-ER)
  • 2022
Navigating unknown structures (e.g., buildings or caves) can be a dangerous and challenging task for emergency responders. This risk can be reduced by capturing detailed 3D maps of unseen