A Real-Time Trajectory Generator With 3rd-Order Constraints Satisfaction Through Velocity Mode Evolution

This article proposes a new real-time trajectory generator, RTG-VM, aimed at enabling a robot to respond quickly and smoothly to sudden external commands and adjust the original trajectory in real-time. It is based on velocity mode evolution, which rapidly plans time-optimal trajectories between arbitrary initial and final motion states. Initially, two velocity mode (VM) planning methods without considering the target displacement are studied: time-optimal VM and reverse initial jerk VM. Subsequently, the VM acceleration profile is adjusted with minimal time cost to reach the desired target displacement. Finally, an effective topological structure of the evolution of acceleration types over planning times is presented. In the experiments, the computation time of RTG-VM for a single axis is about one microsecond. Its real-time performance is at least 38% better than that of Ruckig. Finally, an experiment is conducted on a 5-degree-of -freedom hybrid robot passing through the vicinity of a singular configuration.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Industrial Electronics
Published
2024-11-27
DOI
https://doi.org/10.1109/tie.2024.3497300
Citations
3
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.75
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Real-Time Trajectory Generator With 3rd-Order Constraints Satisfaction Through Velocity Mode Evolution

Juliang Xiao, Wei Zhao, Haitao Liu, Mingli Wang
3 citations
IEEE Transactions on Industrial Electronics
Robotic Path Planning Algorithms
0.75
article

A Real-Time Trajectory Generator With 3rd-Order Constraints Satisfaction Through Velocity Mode Evolution

Juliang Xiao, Wei Zhao, Haitao Liu, Mingli Wang
article en
3 citations

Abstract

This article proposes a new real-time trajectory generator, RTG-VM, aimed at enabling a robot to respond quickly and smoothly to sudden external commands and adjust the original trajectory in real-time. It is based on velocity mode evolution, which rapidly plans time-optimal trajectories between arbitrary initial and final motion states. Initially, two velocity mode (VM) planning methods without considering the target displacement are studied: time-optimal VM and reverse initial jerk VM. Subsequently, the VM acceleration profile is adjusted with minimal time cost to reach the desired target displacement. Finally, an effective topological structure of the evolution of acceleration types over planning times is presented. In the experiments, the computation time of RTG-VM for a single axis is about one microsecond. Its real-time performance is at least 38% better than that of Ruckig. Finally, an experiment is conducted on a 5-degree-of -freedom hybrid robot passing through the vicinity of a singular configuration.

IEEE Transactions on Industrial ElectronicsVol. 72(6)
Tianjin University (CN)
Openalex Percentile: Top 26%
Robotic Path Planning Algorithms
0.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.