# Implementation of SLAM-Based Online Mapping and Autonomous Trajectory Execution in Software and Hardware on the Research Platform Nimbulus-e

## TL;DR

This paper describes a system for autonomous navigation on the Nimbulus-e vehicle using SLAM and trajectory planning algorithms, validated through simulations and a physical prototype.

## Contribution

The paper introduces a novel integration of SLAM, nonholonomic constraints, and control algorithms for autonomous navigation on a unique vehicle platform.

## Key findings

- The system successfully maps and navigates complex environments using LiDAR and odometry data.
- Combining A* and Hybrid A* algorithms ensures smooth and efficient trajectory execution.
- Validation through simulation and a physical prototype confirms the system's effectiveness.

## Abstract

This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four corners. The associated eight joint variables serve as control inputs, allowing precise trajectory following. These control inputs can be derived from the vehicle’s trajectory using nonholonomic constraints. A LiDAR sensor is used to map the environment and detect obstacles. The system processes LiDAR data in real time, continuously updating the environment map and enabling localization within the environment. The inclusion of vehicle odometry data significantly reduces computation time and improves accuracy compared to a purely visual approach. The A* and Hybrid A* algorithms are used for trajectory planning and optimization, ensuring smooth vehicle movement. The implementation is validated through both full vehicle simulations using an ADAMS Car—MATLAB co-simulation and a scaled physical prototype, demonstrating the effectiveness of the system in navigating complex environments. This work contributes to the field of autonomous systems by demonstrating the potential of combining advanced sensor technologies with innovative control algorithms to achieve reliable and efficient navigation. Future developments will focus on improving the robustness of the system by implementing a robust closed-loop controller and exploring additional applications in dense urban traffic and agricultural operations.