Large language models (LLMs) have achieved remarkable success across a wide spectrum
of tasks; however, they still face limitations in scenarios that demand long-term planning and …

The genetic algorithm (GA) is a well-known meta-heuristic technique for addressing the
static mobile robot global path planning (MRGPP) issue. Current GA, however, has certain …

In this research study, trajectory planning of mobile robot is accomplished using two
techniques, namely, a new variant of multi-objective differential evolution (heterogeneous …

When facing a new motion-planning problem, most motion planners solve it from scratch, eg,
via sampling and exploration or starting optimization from a straight-line path. However …

Safe and effective autonomous navigation in dynamic environments is challenging for four-
wheel independently driven steered mobile robots (FWIDSMRs) due to the flexible …

Despite their promise to perform complex reasoning, large language models (LLMs) have
been shown to have limited effectiveness in end-to-end planning. This has inspired an …

The planning of safe paths is an important issue for autonomous robot systems. The
Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a …

We present a novel approach to learning-augmented, long-horizon navigation under
uncertainty in large-scale environments in which considering the robot dynamics is essential …

This paper proposes an inflated medial axis based probabilistic roadmap (PRM) for the duct
environment. The study intends to explore a sampling strategy for PRM toward clearance …

We present a new way of constructing sparse roadmaps using point clouds that
approximates and measures the underlying topology of the C free space. The main …