  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.
