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

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.

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Publication Details

Published
2024-10-06
DOI
https://doi.org/10.1109/smc54092.2024.10831196
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Mikhail Martynov, Sausar Karaf, Dzmitry Tsetserukou, Yasheerah Yaqoot et al.
Robotic Path Planning Algorithms
article

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

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

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.

Skolkovo Institute of Science and Technology (RU)
Russell Sage Foundation
Openalex Percentile: Top 74%
Robotic Path Planning Algorithms
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