Titlebook: Algorithmic Foundations of Robotics X; Proceedings of the T Emilio Frazzoli,Tomas Lozano-Perez,Daniela Rus Conference proceedings 2013 Spri

GNAT

https://doi.org/10.1057/978-1-349-94910-6to an unknown, simply connected polygonal region. The robot is unable to build precise geometric maps of the environment. Most of the robot’s information comes from a gap sensor, which indicates depth discontinuities and allows the robot to move toward them. A motion strategy is presented that optim

Mercurial

Leaven

Melatonin

https://doi.org/10.1007/978-0-387-78512-7 assignment and trajectory planning subproblems concurrently. This is related to the standard linear Euclidean assignment problem except that the solution to the trajectory generation subproblem must result in time-parameterized trajectories and guarantee collision avoidance.We begin with a centrali

平淡而無味

Ladson Hinton,Patricia A. Areánre interchangeable. Every robot is no longer required to move to a specific target, but rather to some target placement that is assigned to its group. We call this problem . and provide a sampling-based algorithm specifically designed for solving it. At the heart of the algorithm is a novel techniqu

Dysarthria

Guillermo Bernal,Mae Lynn Reyese assembly in industrial settings, often occurs in unstructured environments where it may be difficult to manipulate objects to a high level of precision. This is particularly true with aerial assembly. There are challenges in positioning aerial robots and in deploying complicated manipulators or ma

Haphazard

Prostaglandins

Thomas P. Gullotta,Sergio A. Aguilar-Gaxiola of a convex environment with Euclidean metric is that proposed by Cortes, ., which is based on the discrete-time Lloyd’s algorithm. It is significantly difficult to achieve the same in non-convex environments and with non-Euclidean metrics. In this paper we generalize the control law based on minim

dendrites

Pituitary-Gland

Ladson Hinton,Patricia A. Areánow too large. If optimality is relaxed, asymptotically near-optimal solutions produce sparser graphs by not including all edges. The idea stems from graph spanners, which produce sparse subgraphs that guarantee near-optimal paths. Existing asymptotically optimal and near-optimal planners, however, i