Accurately estimating the position and speed of pedestrians is crucial for autonomous vehicles to operate safely in congested environments. LiDAR technology is highly accurate in acquiring the distance to objects, but pedestrian point clouds may be occluded in practical situations. To address this issue, we propose a method that represents pedestrian existence as a probability distribution using kernel density estimation (KDE), accommodating occlusion with a Particle Filter (PF).

This paper presents a robust pedestrian trajectory estimation and combination leveraged by the Hungarian method in crowded environments. Traditional Bayesian filters, such as the Joint Probabilistic Data Association Filter (JPDAF), sometimes fail to track pedestrians continuously due to persistent occlusion and abrupt movements for collision avoidance. In this study, we have improved the success rate of JPDAF-based pedestrian tracking by combining broken trajectories using the Hungarian method. To validate our approach, we conducted multiple-pedestrian tracking experiments in a crowded environment observed by two LiDARs. We confirmed that, even when JPDAF lost track of the target due to frequent occlusion, the proposed method reflecting the direction of the pedestrian flow improved the tracking success rate substantially by reconnecting isolated trajectories.

Leg/wheel mobile robots are expected to play an active role in environments with many obstacles because their leg and wheel mechanisms allow them to adapt to uneven terrain and move efficiently. In this study, for a planar leg/wheel mobile robot, we develop an obstacle avoidance control that combines Model Predictive Control (MPC) based on Markov Chain Monte Carlo (MCMC), a sample-based solution method, and MPC based on a numerical solution to the Euler-Lagrange equations. Specifically, the optimal input is calculated utilizing both MCMC samples and the C/GMRES, and then the samples for the next control cycle are generated through resampling. This approach generates a sub-optimal control input sequence while searching for a global optimal solution, which anticipates that the robot prevents from stacking into a local optimum. The effectiveness of the proposed method is confirmed by comparing it with the MCMPC or C/GMRES methods, respectively, and implementing it into the onboard computer equipped with the actual robot.

In this paper, trajectory tracking and balancing control for autonomous bicycles with a balancer are discussed. In the proposed control method, an input-output linearization is applied for trajectory tracking control and a nonlinear stabilizing control is used for the balancing control. Even though control methods are designed independently, it is shown by several numerical simulations and experiments using a detail model and a real electric motor bike that the stability of the bicycles is ensured with the method even when the desired speed is zero and trajectory tracking to desired ones are achieved