Path planning is one of the most crucial elements in the field of robotics, such as
autonomous driving, minimally invasive surgery and logistics distribution. This review begins …

This paper presents a tutorial overview of path integral (PI) approaches for stochastic
optimal control and trajectory optimization. We concisely summarize the theoretical …

Accurate detection of brain tumors is crucial for enhancing patient outcomes, yet the
interpretation of Magnetic Resonance Imaging (MRI) scans poses significant challenges …

The classical Model Predictive Path Integral (MPPI) control framework, while effective in
many applications, lacks reliable safety features since it relies due to its reliance on a risk …

The Two-Wheeled Inverted Pendulum with Rolljoint (TWIP-R) model and Dynamic Variance
Model Predictive Path Integral (DV-MPPI) controller are proposed to simultaneously solve …

Efficient navigation in unknown and dynamic environments is crucial for expanding the
application domain of mobile robots. The core challenge stems from the non-availability of a …

This paper presents a novel method for modeling the shape of a continuum robot as a
Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance …

Autonomous navigation in unknown environments is challenging and requires the
consideration of both geometric and semantic information to assess the navigability of the …

We propose a new method for autonomous navigation in uneven terrains by utilizing a
sparse Gaussian Process (SGP) based local perception model. The SGP local perception …

Efficient navigation through uneven terrain remains a challenging endeavor for autonomous
robots. We propose a new geometric-based uneven terrain mapless navigation framework …