Planning

//Planning

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 [...]

Risky Planning: Path Planning over Costmaps with a Probabilistically Bounded Speed-Accuracy Tradeoff 2016-10-22T19:51:02+00:00

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, [...]

Planning to Perceive: Exploiting Mobility For Robust Object Detection 2016-10-22T19:51:02+00:00

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 [...]

Non-parametric Learning for Natural Plan Generation 2016-10-22T19:51:02+00:00

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 [...]

Planning Most-Likely Paths from Overhead Imagery 2016-10-22T19:51:02+00:00

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 [...]

Teaching a Randomized Planner to plan with Semantic fields 2018-06-20T15:28:29+00:00