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The Open Motion Planning Library
TLDR
The open motion planning library is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms, and it can be conveniently interfaced with other software components.
View on IEEE
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
TLDR
Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
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Practical robust localization over large-scale 802.11 wireless networks
TLDR
The system is sufficiently robust to enable a variety of location-aware applications without requiring special-purpose hardware or complicated training and calibration procedures, and can be adapted to work with previously unknown user hardware.
View on ACM
Path planning using lazy PRM
TLDR
The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planning and hence minimize the running time of the planner.
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Robotics-Based Location Sensing Using Wireless Ethernet
TLDR
By applying recent advances in probabilistic inference of position and sensor fusion from noisy signals, it is shown that the RF emissions from base stations as measured by off-the-shelf wireless Ethernet cards are sufficiently rich in information to permit a mobile device to reliably track its location.
View on ACM
Analysis of probabilistic roadmaps for path planning
TLDR
An analysis of a recent path planning method which uses probabilistic roadmaps, which has proven very successful in practice, but the theoretical understanding of its performance is still limited.
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Kinodynamic Motion Planning by Interior-Exterior Cell Exploration
TLDR
Applications of motion planning have also expanded to fields such as graphics and computational biology, and the field that addresses planning for complex robots with kinematic and dynamic constraints is addressed.
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Randomized preprocessing of configuration for fast path planning
TLDR
This paper presents a new approach to path planning for robots with many degrees of freedom (DOF) operating in known static environments that is particularly attractive for many-DOF robots which have to perform many successive point-to-point motions in the same environment.
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Probabilistic Roadmaps for Robot Path Planning
TLDR
The planner is able to find paths involving robots with 10 dof in a fraction of a second after relatively short times for preprocessing, and theoretical analysis of the PRM algorithm provides bounds on the number of roadmap nodes needed for solving planning problems in spaces with certain geometric properties.
Sampling-based motion planning with temporal goals
TLDR
This paper presents a geometry-based, multi-layered synergistic approach to solve motion planning problems for mobile robots involving temporal goals, and presents a technique to construct the discrete abstraction using the geometry of the obstacles and the propositions defined over the workspace.
View on IEEE
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