During the last decade, sampling-based path planning algorithms, such as probabilistic
roadmaps (PRM) and rapidly exploring random trees (RRT), have been shown to work well …

Planning algorithms are impacting technical disciplines and industries around the world,
including robotics, computer-aided design, manufacturing, computer graphics, aerospace …

In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning),
a method for trajectory optimization invariant to reparametrization. CHOMP uses functional …

The presence of uncertainty in a system description has always been a critical issue in
control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain …

We present, implement, and analyze a spectrum of closely-related planners, designed to
gain insight into the relationship between classical grid search and probabilistic roadmaps …

Abstract Cyber-physical systems (CPS), such as automotive systems, are starting to include
sophisticated machine learning (ML) components. Their correctness, therefore, depends on …

We present an approach to the problem of differentially constrained mobile robot motion
planning in arbitrary cost fields. The approach is based on deterministic search in a specially …

This paper presents several modifications to the basic rapidly-exploring random tree (RRT)
search algorithm. The fundamental idea is to utilize a heuristic quality function to guide the …

The probabilistic roadmap approach is one of the leading motion planning techniques. Over
the past eight years the technique has been studied by many different researchers. This has …

This study presents a new perspective for autonomous mobile robots path searching by
proposing a biasing direction towards causal entropy maximisation during random tree …