Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Bylard, Andrew"'
This paper presents a set of simple and intuitive robot collision detection algorithms that show substantial scaling improvements for high geometric complexity and large numbers of collision queries by leveraging hardware-accelerated ray tracing on G
Externí odkaz:
http://arxiv.org/abs/2409.09918
Multi-suction-cup grippers are frequently employed to perform pick-and-place robotic tasks, especially in industrial settings where grasping a wide range of light to heavy objects in limited amounts of time is a common requirement. However, most exis
Externí odkaz:
http://arxiv.org/abs/2408.03498
Jerk-constrained trajectories offer a wide range of advantages that collectively improve the performance of robotic systems, including increased energy efficiency, durability, and safety. In this paper, we present a novel approach to jerk-constrained
Externí odkaz:
http://arxiv.org/abs/2404.07889
This paper proposes a real-time model predictive control (MPC) scheme to execute multiple tasks using robots over a finite-time horizon. In industrial robotic applications, we must carefully consider multiple constraints for avoiding joint position,
Externí odkaz:
http://arxiv.org/abs/2209.11880
Autor:
Schneider, Stephanie, Bylard, Andrew, Chen, Tony G., Wang, Preston, Cutkosky, Mark, Pavone, Marco
Robots are widely deployed in space environments because of their versatility and robustness. However, adverse gravity conditions and challenging terrain geometry expose the limitations of traditional robot designs, which are often forced to sacrific
Externí odkaz:
http://arxiv.org/abs/2110.10829
Despite decades of work in fast reactive planning and control, challenges remain in developing reactive motion policies on non-Euclidean manifolds and enforcing constraints while avoiding undesirable potential function local minima. This work present
Externí odkaz:
http://arxiv.org/abs/2101.01297
Safe deployment of autonomous robots in diverse scenarios requires agents that are capable of efficiently adapting to new environments while satisfying constraints. In this work, we propose a practical and theoretically-justified approach to maintain
Externí odkaz:
http://arxiv.org/abs/2008.11700
Sequential Convex Programming (SCP) has recently gained popularity as a tool for trajectory optimization due to its sound theoretical properties and practical performance. Yet, most SCP-based methods for trajectory optimization are restricted to Eucl
Externí odkaz:
http://arxiv.org/abs/1905.07654
Sequential Convex Programming (SCP) has recently seen a surge of interest as a tool for trajectory optimization. However, most available methods lack rigorous performance guarantees and they are often tailored to specific optimal control setups. In t
Externí odkaz:
http://arxiv.org/abs/1903.00155
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