Robotics
♻ ★ Language-Driven Closed-Loop Grasping with Model-Predictive Trajectory Replanning
Combining a vision module inside a closed-loop control system for a
\emph{seamless movement} of a robot in a manipulation task is challenging due
to the inconsistent update rates between utilized modules. This task is even
more difficult in a dynamic environment, e.g., objects are moving. This paper
presents a \emph{modular} zero-shot framework for language-driven manipulation
of (dynamic) objects through a closed-loop control system with real-time
trajectory replanning and an online 6D object pose localization. We segment an
object within $\SI{0.5}{\second}$ by leveraging a vision language model via
language commands. Then, guided by natural language commands, a closed-loop
system, including a unified pose estimation and tracking and online trajectory
planning, is utilized to continuously track this object and compute the optimal
trajectory in real-time. Our proposed zero-shot framework provides a smooth
trajectory that avoids jerky movements and ensures the robot can grasp a
non-stationary object. Experiment results exhibit the real-time capability of
the proposed zero-shot modular framework for the trajectory optimization module
to accurately and efficiently grasp moving objects, i.e., up to \SI{30}{\hertz}
update rates for the online 6D pose localization module and \SI{10}{\hertz}
update rates for the receding-horizon trajectory optimization. These advantages
highlight the modular framework's potential applications in robotics and
human-robot interaction; see the video in
https://www.acin.tuwien.ac.at/en/6e64/.
comment: 9 pages, 6 figures
♻ ★ Multi-AUV Kinematic Task Assignment based on Self-organizing Map Neural Network and Dubins Path Generator
To deal with the task assignment problem of multi-AUV systems under kinematic
constraints, which means steering capability constraints for underactuated AUVs
or other vehicles likely, an improved task assignment algorithm is proposed
combining the Dubins Path algorithm with improved SOM neural network algorithm.
At first, the aimed tasks are assigned to the AUVs by improved SOM neural
network method based on workload balance and neighborhood function. When there
exists kinematic constraints or obstacles which may cause failure of trajectory
planning, task re-assignment will be implemented by change the weights of SOM
neurals, until the AUVs can have paths to reach all the targets. Then, the
Dubins paths are generated in several limited cases. AUV's yaw angle is
limited, which result in new assignments to the targets. Computation flow is
designed so that the algorithm in MATLAB and Python can realizes the path
planning to multiple targets. Finally, simulation results prove that the
proposed algorithm can effectively accomplish the task assignment task for
multi-AUV system.