TY - GEN
T1 - Application of sensory body schemas to path planning for micro air vehicles (MAVs)
AU - Enikov, Eniko T.
AU - Escareno, Juan Antonio
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - To date, most autonomous micro air vehicles (MAV-s) operate in a controlled environment, where the location of and attitude of the aircraft are measured be dedicated high-power computers with IR tracking capability. If MAV-s are to ever exit the lab and carry out autonomous missions, their flight control systems needs to utilize on-board sensors and high-efficiency attitude determination algorithms. To address this need, we investigate the feasibility of using body schemas to carry out path planning in the vision space of the MAV. Body schemas are a biologically-inspired approach, emulating the plasticity of the animal brains, allowing efficient representation of non-linear mapping between the body configuration space, i.e. its generalized coordinates and the resulting sensory outputs. This paper presents a numerical experiment of generating landing trajectories of a miniature rotor-craft using the notion of body and image schemas. More specifically, we demonstrate how a trajectory planning can be executed in the image space using a pseudo-potential functions and a gradientbased maximum seeking algorithm. It is demonstrated that a neural-gas type neural network, trained through Hebbian-type learning algorithm can learn a mapping between the rotor-craft position/attitude and the output of its vision sensors. Numerical simulations of the landing performance of a physical model is also presented, The resulting trajectory tracking errors are less than 8 %.
AB - To date, most autonomous micro air vehicles (MAV-s) operate in a controlled environment, where the location of and attitude of the aircraft are measured be dedicated high-power computers with IR tracking capability. If MAV-s are to ever exit the lab and carry out autonomous missions, their flight control systems needs to utilize on-board sensors and high-efficiency attitude determination algorithms. To address this need, we investigate the feasibility of using body schemas to carry out path planning in the vision space of the MAV. Body schemas are a biologically-inspired approach, emulating the plasticity of the animal brains, allowing efficient representation of non-linear mapping between the body configuration space, i.e. its generalized coordinates and the resulting sensory outputs. This paper presents a numerical experiment of generating landing trajectories of a miniature rotor-craft using the notion of body and image schemas. More specifically, we demonstrate how a trajectory planning can be executed in the image space using a pseudo-potential functions and a gradientbased maximum seeking algorithm. It is demonstrated that a neural-gas type neural network, trained through Hebbian-type learning algorithm can learn a mapping between the rotor-craft position/attitude and the output of its vision sensors. Numerical simulations of the landing performance of a physical model is also presented, The resulting trajectory tracking errors are less than 8 %.
KW - Artificial neural network
KW - Body schema
KW - Cognitive robotics
KW - Micro-air vehicles
KW - Path planning
UR - http://www.scopus.com/inward/record.url?scp=84943544178&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84943544178&partnerID=8YFLogxK
U2 - 10.5220/0005547000250031
DO - 10.5220/0005547000250031
M3 - Conference contribution
AN - SCOPUS:84943544178
T3 - ICINCO 2015 - 12th International Conference on Informatics in Control, Automation and Robotics, Proceedings
SP - 25
EP - 31
BT - ICINCO 2015 - 12th International Conference on Informatics in Control, Automation and Robotics, Proceedings
A2 - Filipe, Joaquim
A2 - Filipe, Joaquim
A2 - Madani, Kurosh
A2 - Gusikhin, Oleg
A2 - Sasiadek, Jurek
PB - SciTePress
T2 - 12th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2015
Y2 - 21 July 2015 through 23 July 2015
ER -