Zobrazeno 1 - 10
of 165
pro vyhledávání: '"Grizzle, Jessy W"'
This work explores an innovative algorithm designed to enhance the mobility of underactuated bipedal robots across challenging terrains, especially when navigating through spaces with constrained opportunities for foot support, like steps or stairs.
Externí odkaz:
http://arxiv.org/abs/2403.02486
This paper presents a novel approach to fall prediction for bipedal robots, specifically targeting the detection of potential falls while standing caused by abrupt, incipient, and intermittent faults. Leveraging a 1D convolutional neural network (CNN
Externí odkaz:
http://arxiv.org/abs/2309.14546
A new control paradigm using angular momentum and foot placement as state variables in the linear inverted pendulum model has expanded the realm of possibilities for the control of bipedal robots. This new paradigm, known as the ALIP model, has shown
Externí odkaz:
http://arxiv.org/abs/2307.02448
For bipedal humanoid robots to successfully operate in the real world, they must be competent at simultaneously executing multiple motion tasks while reacting to unforeseen external disturbances in real-time. We propose Kinodynamic Fabrics as an appr
Externí odkaz:
http://arxiv.org/abs/2303.04279
This paper presents a reactive planning system that allows a Cassie-series bipedal robot to avoid multiple non-overlapping obstacles via a single, continuously differentiable control barrier function (CBF). The overall system detects an individual ob
Externí odkaz:
http://arxiv.org/abs/2301.01906
This work reports on developing a deep inverse reinforcement learning method for legged robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive sensory data. Existing works use robot-agnostic exteroceptive envir
Externí odkaz:
http://arxiv.org/abs/2207.03034
Multi-objective or multi-destination path planning is crucial for mobile robotics applications such as mobility as a service, robotics inspection, and electric vehicle charging for long trips. This work proposes an anytime iterative system to concurr
Externí odkaz:
http://arxiv.org/abs/2205.14853
Targets are essential in problems such as object tracking in cluttered or textureless environments, camera (and multi-sensor) calibration tasks, and simultaneous localization and mapping (SLAM). Target shapes for these tasks typically are symmetric (
Externí odkaz:
http://arxiv.org/abs/2109.01181
Autor:
Huang, Jiunn-Kai, Grizzle, Jessy W.
We propose and experimentally demonstrate a reactive planning system for bipedal robots on unexplored, challenging terrains. The system consists of a low-frequency planning thread (5 Hz) to find an asymptotically optimal path and a high-frequency rea
Externí odkaz:
http://arxiv.org/abs/2108.06699
Autor:
Gan, Lu, Kim, Youngji, Grizzle, Jessy W., Walls, Jeffrey M., Kim, Ayoung, Eustice, Ryan M., Ghaffari, Maani
This article presents a novel and flexible multitask multilayer Bayesian mapping framework with readily extendable attribute layers. The proposed framework goes beyond modern metric-semantic maps to provide even richer environmental information for r
Externí odkaz:
http://arxiv.org/abs/2106.14986