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Path Planning with Adaptive Dimensionality
A planning algorithm intended to speed up path planning for high-dimensional state-spaces such as robotic arms that can be substantially faster than some of the state-of-the-art planning algorithms optimized for those tasks. Expand
Path planning for non-circular micro aerial vehicles in constrained environments
This work uses an anytime planner based on A* that performs a graph search on a four-dimensional (4-D) (x,y,z, heading) lattice that allows for the generation of close-to-optimal trajectories based on a set of precomputed motion primitives along with the capability to provide trajectories in real-time allowing for on-the-fly re-planning as new sensor data is received. Expand
Combining global and local planning with guarantees on completeness
This paper presents an approach to solving the global and local path planning problem into a single search using a combined 2-D and higher dimensional state-space. Expand
The University of Pennsylvania MAGIC 2010 multi-robot unmanned vehicle system
A multi-vehicle robot team, consisting of intelligent sensor and disrupter unmanned ground vehicles that can survey, map, recognize, and respond to threats in a dynamic urban environment with minimal human guidance is constructed. Expand
Planning for multi-robot exploration with multiple objective utility functions
This paper presents an expansion to frontier based approaches allowing for the incorporation of multiple objective utility functions that allow adjustment of the exploration priorities both for the individual robots and the group as a whole. Expand
State lattice with controllers: Augmenting lattice-based path planning with controller-based motion primitives
A formal description of the method of constructing the search graph in these cases as well as presenting real-world and simulated testing data showing the practical application of this approach are provided. Expand
Planning for a ground-air robotic system with collaborative localization
The approach is to combine a recently developed state lattice planner using controller-based motion primitives (SLC) with planning using adaptive dimensionality (PAD), which allows for robust navigation using a wide variety of sensors including in areas with no or limited high-quality localization information. Expand
Planning for a Small Team of Heterogeneous Robots: from Collaborative Exploration to Collaborative Localization
A series of planning algorithms that incorporate multi-robot collaboration into the planning process and can generate trajectories that are not feasible to execute if planning occurred on an individual robot basis are developed. Expand
University of Pennsylvania MAGIC 2010 Final Report
Abstract : In this report, we describe the technical approach and algorithms that have been used by the Univ. of Pennsylvania in the MAGIC 2010 competition. We have constructed and deployed aExpand