Moohyun Cha’s research while affiliated with Korea Institute of Machinery and Materials and other places

This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets of coefficients for adaptability to the surrounding environment. The simulation results demonstrate that the algorithm is appropriate for generating obstacle avoidance paths. The algorithm was implemented on the ROS platform using NVIDIA’s Jetson Xavier, and driving experiments were conducted with a steer-type AGV. Through measurements of computation time and real obstacle avoidance experiments, it was shown to be practical in the real world.

Light detection and ranging (LiDAR) sensors can create high-quality scans of an environment. However, LiDAR point clouds are affected by harsh weather conditions since airborne particles are easily detected. In literature, conventional filtering and artificial intelligence (AI) filtering methods have been used to detect, and remove, airborne particles. In this paper, a convolutional neural network (CNN) model was used to classify airborne dust particles through a voxel-based approach. The CNN model was compared to several conventional filtering methods, where the results show that the CNN filter can achieve up to 5.39 % F1 score improvement when compared to the best conventional filter. All the filtering methods were tested in dynamic environments where the sensor was attached to a mobile platform, the environment had several moving obstacles, and there were multiple dust cloud sources.

The paper presents a novel method for real-time detection of off-road drivable regions in LiDAR point clouds without relying on deep learning. Our method directly distinguishes drivable regions while accounting for noise caused by snow and fog. By focusing on the scanning characteristics of rotating LiDAR, our PIIC approach achieves robust performance in challenging off-road environments. The proposed technique divides the space into sections and segments drivable areas by counting points in each section. It allows noise influence to be countable using a simple probabilistic model. The developed solution offers significant advancements in autonomous driving in off-road environments since the method offers drivability in real-time with noise immunity. Experimental results demonstrate the efficiency of the proposed method in hazardous situations such as heavy snow, fog, and crosstalk from other LiDAR signals. Importantly, the low computational load enables real-time operation and provides promising opportunities for fusion with other recognition methods.

Automated excavation is hard to achieve due to several inherent problems such as resistive force acting against the bucket, non-homogenous dynamics of various excavation media, and nonlinearities of the excavator’s hydraulics system. To deal with this issue, this paper provides an integrative control strategy for successful autonomous excavation that considers the mutually associated factors, i.e., position, contour, and force control. For the position tracking, a non-linear PI controller was designed to track the position of individual actuators of the excavator and thereby control the bucket tip’s position. In addition, the contour control technique was applied to achieve an optimal excavation path to minimize contour errors. Finally, to compensate for the ground resistive force during digging tasks, a force impedance controller was designed along with the time-delayed control that reduces the effect of dynamic uncertainties. Experimental results with a modified mini-wheeled excavator show that the developed integrative tracking control strategy can provide a comprehensive solution to improving the tracking performance for autonomous excavation that can simultaneously deal with the critical components of position, contour, and force control.