Titlebook: Algorithmic Foundations of Robotics XI; Selected Contributio H. Levent Akin,Nancy M. Amato,A. Frank Stappen Book 2015 Springer Internationa

不能平静

Efficient Sampling-Based Approaches to Optimal Path Planning in Complex Cost Spaces,ity guarantees. Results presented on several classes of problems show that they converge faster than RRT* toward the optimal path, especially when the topology of the search space is complex and/or when its dimensionality is high.

Cytology

引起痛苦

echnic

Trackability with Imprecise Localization,e to prove upper and lower bounds for the worst-case tracking performance, both with or without obstacles. We also provide simulation results on real and synthetic data to illustrate trackability under imprecise localization.

GEON

Sparse Methods for Efficient Asymptotically Optimal Kinodynamic Planning,such desirable guarantees for a wide set of systems under a reasonable set of assumptions. Simulations for a variety of benchmarks, including physically simulated ones, confirm the argued properties of the approach.

reserve

The Feasible Transition Graph: Encoding Topology and Manipulation Constraints for Multirobot Push-P satisfied for the robots to manipulate the object. From this data structure, we readily derive a complete planner to coordinate such motion. Finally, we show how to construct the FTG in some sample environments and discuss future adaptations to general environments.

土产

缩短

Efficient Multi-robot Motion Planning for Unlabeled Discs in Simple Polygons,ved in . time, assuming that the start and target positions are at least some minimal distance from each other. This is in sharp contrast to the standard (labeled) and more general multi-robot motion planning problem for discs moving in a simple polygon, which is known to be strongly .-hard.

BAIT

FFRob: An Efficient Heuristic for Task and Motion Planning,e FastForward (FF) planner, to motion planning, and to compute it efficiently. We use a multi-query roadmap structure that can be conditionalized to model different placements of movable objects. The resulting tightly integrated planner is simple and performs efficiently in a collection of tasks involving manipulation of many objects.

离开就切除

Kinodynamic RRTs with Fixed Time Step and Best-Input Extension Are Not Probabilistically Complete,. It has been shown that for finite input sets an RRT using a fixed step size with a randomly selected input is probabilistically complete. However, this variant is uncommon since it is less efficient. We prove that the most common variant of choosing the best input in combination with a fixed time step is not probabilistically complete.