In the multiple fields covered by Artificial Intelligence (AI), path planning is undoubtedly one
of the issues that cover a wide range of research lines. To be able to find an optimal solution …

Chance-constrained optimization provides a promi-sing framework for solving control and
planning problems with uncertainties, due to its modeling capability to capture randomness …

The present paper extends the classically studied chance-constrained optimal control to
incorporate continuous-time chance constraints. While the classical approaches provide risk …

Demand for high-performance, robust, and safe autonomous systems has grown
substantially in recent years. These objectives motivate the desire for efficient safety …

We develop a set of novel autonomous controllers for multiple point‐mass robots or agents
in the presence of wall‐like rectangular planes in three‐dimensional space. To the authors' …

This paper addresses a continuous-time continuous-space chance-constrained stochastic
optimal control (SOC) problem via a Hamilton-Jacobi-Bellman (HJB) partial differential …

We present an analytical method to estimate the collision probability of motion plans for
autonomous agents with discrete-time dynamics operating under Gaussian motion and …

The present paper introduces a hybrid genetic algorithm for path planning problem with
obstacle avoidance. The genetic algorithm is combined with Ray Casting (RC) algorithm …

We propose Constraint-Generation Policy Optimization (CGPO) for optimizing policy
parameters within compact and interpretable policy classes for mixed discrete-continuous …

We present an analytical method to estimate the continuous-time collision probability of
motion plans for autonomous agents with linear controlled Itô dynamics. Motion plans …