8 accepted papers
Failure of any component in a robotic system during operation is a critical concern, and it is essential to address such incidents promptly. This work investigates a novel technique to recover from failures or changes in the configuration space while avoiding expensive re-computation or re-planning.…
We present an incremental topology-based motion planner that, while planning paths in the configuration space, performs metric gluing on the constructed Vietoris-Rips simplicial complex of each sub-space (voxel). By incrementally capturing topological and geometric information in batches of voxel gr…
We present a topological method for finding coarsely diverse pathways. The use of pre-computed paths for online planning in a dynamic context reduces the overhead of re-planning alternate routes. Our algorithm applied the notion of discrete Morse theory to identify critical points incident on the ob…
Many state-of-art robotics applications require fast and efficient motion planning algorithms. Existing motion planning methods become less effective as the dimensionality of the robot and its workspace increases, especially the computational cost of collision detection routines. In this work, we pr…
Successful robot path planning is challenging in the presence of visual occlusions and moving targets. Classical methods to solve this problem have used visioning and perception algorithms in addition to partially observable markov decision processes to aid in path planning for pursuit-evasion and r…
This paper introduces a probabilistic prediction model with a novel variant of the Markov decision process to improve tracking time and location detection accuracy in an air-to-ground robot surveillance scenario. While most surveillance algorithms focus mainly on controls of an unmanned aerial vehic…
We present a new way of constructing sparse roadmaps using point clouds that approximates and measures the underlying topology of the Cfree space. The main advantage of the constructed roadmap is its homotopy equivalence to the η-offset of the Cfree space. Though only used to plan paths as a regular…
Probabilistic Roadmap Methods (PRMs) solve the motion planing problem by constructing a roadmap (or graph) that models the motion space when feasible local motions exist. PRMs and variants contain several phases during roadmap generation i.e., sampling, connection, and query. Some work has been done…