Zobrazeno 1 - 10
of 211
pro vyhledávání: '"Perez, Tristan"'
Machine vision is an important sensing technology used in mobile robotic systems. Advancing the autonomy of such systems requires accurate characterisation of sensor uncertainty. Vision includes intrinsic uncertainty due to the camera sensor and extr
Externí odkaz:
http://arxiv.org/abs/2010.06871
In this paper, we consider the problem of computing parameters of an objective function for a discrete-time optimal control problem from state and control trajectories with active control constraints. We propose a novel method of inverse optimal cont
Externí odkaz:
http://arxiv.org/abs/2005.06153
This paper presents a novel collision avoidance strategy for unmanned aircraft detect and avoid that requires only information about the relative bearing angle between an aircraft and hazard. It is shown that this bearing-only strategy can be conceiv
Externí odkaz:
http://arxiv.org/abs/2004.09744
Using robots to harvest sweet peppers in protected cropping environments has remained unsolved despite considerable effort by the research community over several decades. In this paper, we present the robotic harvester, Harvey, designed for sweet pep
Externí odkaz:
http://arxiv.org/abs/1810.11920
Autor:
Reyes-Báez, Rodolfo, Donaire, Alejandro, van der Schaft, Arjan, Jayawardhana, Bayu, Perez, Tristan
In this work we propose a family of trajectory tracking controllers for marine craft in the port-Hamiltonian (pH) framework using virtual differential passivity based control (v-dPBC). Two pH models of marine craft are considered, one in a body frame
Externí odkaz:
http://arxiv.org/abs/1803.07938
Publikováno v:
D. Hall, F. Dayoub, T. Perez, and C. McCool, "A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management," Computers and Electronics in Agriculture, Vol. 148, pp. 107-120, May 2018
In this work we demonstrate a rapidly deployable weed classification system that uses visual data to enable autonomous precision weeding without making prior assumptions about which weed species are present in a given field. Previous work in this are
Externí odkaz:
http://arxiv.org/abs/1801.08613
Robotic harvesting of crops has the potential to disrupt current agricultural practices. A key element to enabling robotic harvesting is to safely remove the crop from the plant which often involves locating and cutting the peduncle, the part of the
Externí odkaz:
http://arxiv.org/abs/1709.10275
In this paper, we present the lessons learnt during the development of a new robotic harvester (Harvey) that can autonomously harvest sweet pepper (capsicum) in protected cropping environments. Robotic harvesting offers an attractive potential soluti
Externí odkaz:
http://arxiv.org/abs/1706.06203
Publikováno v:
IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 872-879, April 2017. doi: 10.1109/LRA.2017.2655622
In this letter, we present a new robotic harvester (Harvey) that can autonomously harvest sweet pepper in protected cropping environments. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvest
Externí odkaz:
http://arxiv.org/abs/1706.02023