23 citations · 51 across the 8 of their papers we have counts for
9 papers
Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fields
Felix Herrmann, Sebastian Zach, Jacopo Banfi +3
Estimating collision probabilities between robots and environmental obstacles or other moving agents is crucial to ensure safety during path planning. This is an important building…
Multiple-Hypothesis Path Planning with Uncertain Object Detections
Brian H. Wang, Beatriz Asfora, Rachel Zheng +3
Path planning in obstacle-dense environments is a key challenge in robotics, and depends on inferring scene attributes and associated uncertainties. We present a multiple-hypothesi…
Learning to Assess Danger from Movies for Cooperative Escape Planning in Hazardous Environments
Vikram Shree, Sarah Allen, Beatriz Asfora +2
There has been a plethora of work towards improving robot perception and navigation, yet their application in hazardous environments, like during a fire or an earthquake, is still…
Is it Worth to Reason about Uncertainty in Occupancy Grid Maps during Path Planning?
Jacopo Banfi, Lindsey Woo, Mark Campbell
This paper investigates the usefulness of reasoning about the uncertain presence of obstacles during path planning, which typically stems from the usage of probabilistic occupancy…
Exploiting Natural Language for Efficient Risk-Aware Multi-robot SaR Planning
Vikram Shree, Beatriz Asfora, Rachel Zheng +3
The ability to develop a high-level understanding of a scene, such as perceiving danger levels, can prove valuable in planning multi-robot search and rescue (SaR) missions. In this…
Detecting and Mapping Trees in Unstructured Environments with a Stereo Camera and Pseudo-Lidar
Brian H. Wang, Carlos Diaz-Ruiz, Jacopo Banfi +1
We present a method for detecting and mapping trees in noisy stereo camera point clouds, using a learned 3-D object detector. Inspired by recent advancements in 3-D object detectio…
Planning Paths Through Unknown Space by Imagining What Lies Therein
Yutao Han, Jacopo Banfi, Mark Campbell
This paper presents a novel framework for planning paths in maps containing unknown spaces, such as from occlusions. Our approach takes as input a semantically-annotated point clou…
Mixed-Integer Linear Programming Models for Multi-Robot Non-Adversarial Search
Beatriz A. Asfora, Jacopo Banfi, Mark Campbell
In this letter, we consider the Multi-Robot Efficient Search Path Planning (MESPP) problem, where a team of robots is deployed in a graph-represented environment to capture a movin…
DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds
Yutao Han, Hubert Lin, Jacopo Banfi +2
Planning in unstructured environments is challenging -- it relies on sensing, perception, scene reconstruction, and reasoning about various uncertainties. We propose DeepSemanticHP…