Towards Autonomous Multi-Modal Mobility Morphobot (M4) Robot: Traversability Estimation and 3D Path Planning

TL;DR

This paper advances the autonomy of the M4 robot for Mars and rescue missions by enabling autonomous mode selection and path planning in complex terrains using deep learning and energy-efficient algorithms.

Contribution

It introduces a scalable framework for autonomous mode switching and path planning in multi-modal robots, integrating deep learning and 2.5D mapping techniques.

Findings

Demonstrated intelligent mode switching between walking and flying.

Validated energy-efficient path planning in simulations.

Showcased scalability for future multi-modal integrations.

Abstract

This thesis enhances the autonomy of the M4 (Multi-Modal Mobility Morphobot) robot, designed for Mars and rescue missions. The research enables the robot to autonomously select its locomotion mode and path in complex terrains. Focusing on walking and flying modes, a Gazebo simulation, and custom perception-navigations pipelines are developed. Leveraging deep learning, the robot determines optimal mode transitions based on a 2.5D map. Additionally, an energy efficient path planner based on 2.5D mapping is implemented and validated in simulations. The contributions demonstrate scalability for future mode integrations. The M4 robot showcases intelligent mode switching, efficient navigation, and reduced energy consumption, bringing us closer to fully autonomous multi-modal robots for exploration and rescue missions. This work paves the way for future advancements in autonomous robotics, with the ultimate vision of deploying the M4 robot for exploration and rescue tasks, making a significant impact in the quest for intelligent and versatile robotic systems.