Juan Burgos

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— This paper describes a scalable method for paral-lelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent(More)
— In this work, we describe an approach for model-ing and simulating group behaviors for pursuit-evasion that uses a graph-based representation of the environment and integrates multi-agent simulation with roadmap-based path planning. Our approach can be applied to more realistic scenarios than are typically studied in most previous work, including agents(More)
Multi-Agent Systems involve the cooperation and coordination of groups of agents in an environment while they perform tasks. Previous work has provided simulated heterogeneous agents that engage in specific behavior driven actions with a priori assumptions with respect to agent knowledge of the environment. These experiments however did not have a(More)
We present an approach to the pursuit-evasion problem which is applicable to complex, multi-level environments. Studying each aspect of this problem in 3D structured environments is a distinct extension over many previous approaches. We also utilize our roadmap-based approach to multi-agent behavior when tracking agents of interest. Results are presented in(More)
Motion planning is defined as the problem of finding a valid path taking a robot (or any movable object) from a given start configuration to a goal configuration in an environment. While motion planning has its roots in robotics, it now finds application in many other areas of scientic computing such as protein folding, drug design, virtual prototyping,(More)
Motion planning is the problem of computing valid paths through an environment. However, because computing exact solutions is intractable, sampling-based algorithms, such as Probabilistic RoadMaps (PRMs), have gained popularity. PRMs compute an approximate mapping of the planning space by sacrificing completeness in favor of efficiency. However, these(More)
Problems that require a traversal on a collection of data can be solved following a decrease-and-conquer algorithm strategy. Reductor is a behavioral pattern that describes an object-oriented design relying on higher-order facilities to solve this sort of problem. Intent Identify, encapsulate and organize the variant and invariant parts (both behavior and(More)
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