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MorphoMove: Bi-Modal Path Planner with MPC-based Path Follower for Multi-Limb Morphogenetic UAV

Muhammad Ahsan Mustafa, Yasheerah Yaqoot, Mikhail Martynov, Sausar Karaf, Dzmitry Tsetserukou

TL;DR

This work addresses autonomous navigation for MorphoGear, a morphogenetic UAV capable of both aerial flight and ground locomotion. It introduces a bi-modal path planning framework based on A* that seamlessly transitions between air and ground modes, complemented by an MPC-based path follower for precise ground tracking. Validation in a Unity simulation demonstrates strong ground-path following with an RMSE of 0.91 cm and a max error of 1.85 cm, enabling stable operation in cluttered environments while switching modes as needed. Together, these contributions enable reliable autonomous exploration across aerial and terrestrial domains, with potential for future enhancements in ground planning diversity and MPC-based flight control for manipulation during flight.

Abstract

This paper discusses developments for a multi-limb morphogenetic UAV, MorphoGear, that is capable of both aerial flight and ground locomotion. A hybrid path planning algorithm based on the A* strategy has been developed, enabling seamless transition between air-to-ground navigation modes, thereby enhancing robot's mobility in complex environments. Moreover, precise path following is achieved during ground locomotion with a Model Predictive Control (MPC) architecture for its novel walking behaviour. Experimental validation was conducted in the Unity simulation environment utilizing Python scripts to compute control values. The algorithm's performance is validated by the Root Mean Squared Error (RMSE) of 0.91 cm and a maximum error of 1.85 cm, as demonstrated by the results. These developments highlight the adaptability of MorphoGear in navigation through cluttered environments, establishing it as a usable tool in autonomous exploration, both aerial and ground-based.

MorphoMove: Bi-Modal Path Planner with MPC-based Path Follower for Multi-Limb Morphogenetic UAV

TL;DR

This work addresses autonomous navigation for MorphoGear, a morphogenetic UAV capable of both aerial flight and ground locomotion. It introduces a bi-modal path planning framework based on A* that seamlessly transitions between air and ground modes, complemented by an MPC-based path follower for precise ground tracking. Validation in a Unity simulation demonstrates strong ground-path following with an RMSE of 0.91 cm and a max error of 1.85 cm, enabling stable operation in cluttered environments while switching modes as needed. Together, these contributions enable reliable autonomous exploration across aerial and terrestrial domains, with potential for future enhancements in ground planning diversity and MPC-based flight control for manipulation during flight.

Abstract

This paper discusses developments for a multi-limb morphogenetic UAV, MorphoGear, that is capable of both aerial flight and ground locomotion. A hybrid path planning algorithm based on the A* strategy has been developed, enabling seamless transition between air-to-ground navigation modes, thereby enhancing robot's mobility in complex environments. Moreover, precise path following is achieved during ground locomotion with a Model Predictive Control (MPC) architecture for its novel walking behaviour. Experimental validation was conducted in the Unity simulation environment utilizing Python scripts to compute control values. The algorithm's performance is validated by the Root Mean Squared Error (RMSE) of 0.91 cm and a maximum error of 1.85 cm, as demonstrated by the results. These developments highlight the adaptability of MorphoGear in navigation through cluttered environments, establishing it as a usable tool in autonomous exploration, both aerial and ground-based.
Paper Structure (15 sections, 12 equations, 12 figures, 3 tables, 1 algorithm)

This paper contains 15 sections, 12 equations, 12 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Overview
  4. MorphoGear UAV
  5. Limb Structure of MorphoGear
  6. Path Planning
  7. Bi-Modal Path Planning Algorithm
  8. Path Following
  9. Canter Gait Locomotion
  10. Model Predictive Control (MPC)
  11. Analysis and Results
  12. Experimental Setup
  13. Experimental Evaluation
  14. Experimental Results
  15. Conclusion And Future Work

Figures (12)

  • Figure 1: MorphoGear in its mid-step while following the planned trajectory (yellow line) using the designed MPC in Unity environment.
  • Figure 2: MorphoGear - a multi-limb morphogenetic UAV for aerial and ground locomotion b1.
  • Figure 3: 3D CAD depicting the limb structure of MorphoGear b1.
  • Figure 4: The planned trajectory (red line) of MorphoGear incorporating both aerial and ground paths based on the environment.
  • Figure 5: Limb trajectories for canter gait motion to be followed by the end-effector of each limb.
  • ...and 7 more figures