12 accepted papers
Multi-Agent Path Finding (MAPF) in complex environments remains challenging due to high computational complexity, frequent conflicts, and realistic motion constraints. Most existing methods focus on discrete spaces or idealized omnidirectional models, often neglecting or partially considering nonhol
Accurate camera–LiDAR fusion relies on precise extrinsic calibration, which fundamentally depends on establishing reliable cross-modal correspondences under potentially large misalignments. Existing learning-based methods typically project LiDAR points into depth maps for feature fusion, which disto
In motion planning, efficiently navigating from a start state to a goal state in spaces with narrow passages remains a significant challenge. Recently, learning-based methods have attracted considerable attention owing to their higher inference speeds compared to traditional approaches. However, the
This paper presents a novel continuous-time gradient-proportional-integral flow (GPIF) for motion planning with obstacle avoidance. We first frame the motion planning task as a constrained optimization problem, which is relaxed to be an unconstrained optimization problem that can be locally solved v
With the rapid advancement of intelligent manufacturing and the rise of emerging markets, global auto-mobile exports have surged, placing unprecedented demands on logistics infrastructure. Efficient coordination of multiple robots for vehicle autonomous transfer is essential in high-density storage
Cameras and LiDAR are essential sensors for autonomous vehicles. The fusion of camera and LiDAR data addresses the limitations of individual sensors but relies on precise extrinsic calibration. Recently, numerous end-to-end calibration methods have been proposed; however, most predict extrinsic para
In the context of unstructured and unknown environment, the autonomous navigation still faces many challenges, such as assessing rough terrain and deciding how to safely navigate complex terrain. In this work, we propose a robust and practical off-road navigation framework that has been successfully…
In the labor-intensive Roll-On/Roll-Off (RO/RO) terminal environment, research on vehicle transport robots with mobility, stability, and reliability is receiving increasing attention. This paper presents a novel control framework for a Straddle-Type Dual-Body vehicle transfer robot. Initially, fine…
Tracking dynamic targets poses a significant challenge for Unmanned Ground Vehicles (UGVs). Existing methods often lack research on multi-disturbed conditions. To address this issue, we propose a trajectory-prediction-based dynamic tracking scheme, which includes target localization, trajectory pred…
In order to guarantee autonomous vehicles' autonomy, mobility, and ride quality in rugged environments, a real-time speed planning method based on the time-frequency transformation of terrain characteristics is designed to achieve adaptive speed planning of autonomous vehicles in rough ground. On th…
This article investigates the real-time semantic segmentation in robot engineering applications based on the Broad Learning System (BLS), and a novel Multi-level Enhancement Layers Network (MELNet) based on BLS framework is proposed for real-time vision tasks in a complex street scene on the unmanne…
In this paper, we propose a parallel six-wheel-legged robot that can traverse irregular terrain while carrying objectives to do heavy-duty work. This robot is equipped with six Stewart platforms as legs and tightly integrates the additional degrees of freedom introduced by the wheels. The presented…