Kinematic Modeling of Mobile Continuum Manipulator Robots

Résumé

This thesis focuses on the kinematic modeling of Mobile Continuum Manipulators (MCMs) with potential applications in the assistance of people with reduced mobility, rescue operations and exploration. Indeed, the continuum manipulators (CMs) are made of flexible materials that allow them to better operate in restricted environments. The operation of robots in these environments requires inverse kinematics (IK) modeling, which consists in determining the set of robot configurations for a given end-effector pose. However, while much research has been done on IK for static CMs, very little exists on MCMs. The latter are suitable for tasks requiring a large workspace and their hyper-redundant behavior can be exploited to avoid obstacles. However, these tasks often require complex configurations of the robot. Thus, it is important to develop accurate and computationally efficient IK algorithms. Our modeling approach consists of solving the IK while maintaining the multiplicity of IK solutions. Therefore, the configuration space of the MCM is discretized to reduce the infinite number of Inverse Kinematic Solutions to a finite number. Then, the Inverse Kinematic Problem (IKP) of the MCM is transformed into an IKP of a single section of the CM by parameterizing the configuration variables of the mobile platform and the first (N −1) sections of the manipulator, N being the number of sections. The variables are parameterized using the classification of the MCM workspace performed using the Growing Neural Gas algorithm. The database for the classification was obtained developing a Forward Kinematic Model of the CM based on a combination of Variable Curvature Approximation and Deep Neural Network. The proposed models have been validated by a series of simulations and experiments performed on a virtual and real MCM, respectively.

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