Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Banfi, Jacopo"'
Autor:
Herrmann, Felix, Zach, Sebastian, Banfi, Jacopo, Peters, Jan, Chalvatzaki, Georgia, Tateo, Davide
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 block of modern planning algorithms in many application scenarios suc
Externí odkaz:
http://arxiv.org/abs/2409.04306
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-hypothesis path planner designed to navigate complex environments using obstacl
Externí odkaz:
http://arxiv.org/abs/2308.07420
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 at a nascent stage. We hypothesize two key challenges here: first, it
Externí odkaz:
http://arxiv.org/abs/2207.13791
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 grid maps for representing the environment when mapping via a noisy se
Externí odkaz:
http://arxiv.org/abs/2205.14251
Publikováno v:
IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3152-3159, April 2021
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 work, we propose to uniquely leverage natural language descriptions f
Externí odkaz:
http://arxiv.org/abs/2104.03809
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 detection using a pseudo-lidar representation for stereo data, we train a Poin
Externí odkaz:
http://arxiv.org/abs/2103.15967
Publikováno v:
IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6805-6812, Oct. 2020
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 moving target within a given deadline. We prove this problem to be NP-hard,
Externí odkaz:
http://arxiv.org/abs/2011.12480
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 cloud, and leverages an image inpainting neural network to generate a reas
Externí odkaz:
http://arxiv.org/abs/2011.07316
Planning in unstructured environments is challenging -- it relies on sensing, perception, scene reconstruction, and reasoning about various uncertainties. We propose DeepSemanticHPPC, a novel uncertainty-aware hypothesis-based planner for unstructure
Externí odkaz:
http://arxiv.org/abs/2003.03464
Publikováno v:
In Robotics and Autonomous Systems September 2019 119:221-230
