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Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques

Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques Path planning is the problem of finding a continuous sequence of valid states from a start to a goal specification. Popular approaches in robotics include graph-based searches, such as A* [1], and sampling-based planners, such as Rapidly-exploring Random Trees (RRT) [2]. Both graph- and sampling-based approaches [...]

By |2020-02-24T09:41:42+00:00February 24th, 2020|ESP, ORI Blog, Planning|Comments Off on Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques

Risky Planning: Path Planning over Costmaps with a Probabilistically Bounded Speed-Accuracy Tradeoff

Abstract— This paper is about generating plans over uncertain maps quickly. Our approach combines the ALT (A* search, landmarks and the triangle inequality) algorithm and risk heuristics to guide search over probabilistic cost maps. We build on previous work which generates probabilistic cost maps from aerial imagery and use these cost maps to precompute heuristics [...]

By |2019-12-14T13:07:18+00:00October 13th, 2011|MRG Highlights, News Feed, Perception, Planning, Topics|Comments Off on Risky Planning: Path Planning over Costmaps with a Probabilistically Bounded Speed-Accuracy Tradeoff

Planning to Perceive: Exploiting Mobility For Robust Object Detection

Abstract - Consider the task of a mobile robot autonomously navigating through an environment while detecting and mapping objects of interest using a noisy object detector. The robot must reach its destination in a timely manner, but is rewarded for correctly detecting recognizable objects to be added to the map, and penalized for false alarms. However, [...]

By |2016-10-22T19:51:02+01:00October 13th, 2011|News Feed, Perception, Planning, Topics|Comments Off on Planning to Perceive: Exploiting Mobility For Robust Object Detection

Non-parametric Learning for Natural Plan Generation

We present a novel way to learn sampling distributions for sampling-based motion planners by making use of expert data. We learn an estimate (in a non-parametric setting) of sample densities around semantic regions of interest, and incorporate these learned distributions into a sampling-based planner to produce natural plans. Our motivation is that certain aspects of [...]

By |2016-10-22T19:51:02+01:00October 13th, 2010|News Feed, Perception, Planning|Comments Off on Non-parametric Learning for Natural Plan Generation

Planning Most-Likely Paths from Overhead Imagery

In this work, we are concerned with planning paths from overhead imagery. The novelty here lies in taking explicit account of uncertainty in terrain classification and spatial variation in terrain cost. The image is first classified using a multi-class Gaussian Process Classifier which provides probabilities of class membership at each location in the image, which is then combined [...]

By |2019-08-03T14:16:05+01:00October 13th, 2010|MRG Highlights, News Feed, Perception, Planning, Topics|Comments Off on Planning Most-Likely Paths from Overhead Imagery

Teaching a Randomized Planner to plan with Semantic fields

 Abstract—This paper presents a novel way to bias the sampling domain of stochastic planners by learning from example plans. We learn a generative model of a planner as a function of proximity to labeled objects in the workspace. Our motivation is that certain objects in the workspace have a local influence on planning strategies, which [...]

By |2018-06-20T15:28:29+01:00October 13th, 2010|News Feed, Perception, Planning, Topics|Comments Off on Teaching a Randomized Planner to plan with Semantic fields
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