Abstract
Abstract High degrees of freedom (DOF) mobile manipulators provide more flexibility than conventional manipulators. They also provide manipulation operations with a mobility capacity and have potential in many applications. However, due to high redundancy, planning and control become more complicated and difficult, especially when obstacles occur. Most existing obstacle avoidance methods are based on off-line algorithms and most of them mainly focus on planning a new collision-free path, which is not appropriate for some applications, such as teleoperation and uses many system resources as well. Therefore, this paper presents an online planning and control method for obstacle avoidance in mobile manipulators using online sensor information and redundancy resolution. An obstacle contour reconstruction approach employing a mobile manipulator equipped with an active laser scanner system is also introduced in this paper. This method is implemented using a mobile manipulator with a seven-DOF manipulator and a four-wheel drive mobile base. The experimental results demonstrate the effectiveness of this method.
Highlights
High degrees of freedom (DOF) mobile manipulators are widely used in many fields including industry, the military and in space [1, 2]
We propose a new obstacle avoidance method for a mobile manipulator with a seven‐DOF manipulator and a four‐wheel drive mobile base
According to these 3D contours and the position of the critical points of the manipulator, the control system changes the manipulator configuration automatically to adapt to the environment to avoid collision
Summary
High DOF mobile manipulators are widely used in many fields including industry, the military and in space [1, 2]. They consist of a high DOF manipulator and a mobile base. The mobile base greatly expands the workspace of the manipulator and the high redundancy provides more flexibility. A wider workspace and flexibility are desired features, this combined structure and high redundancy makes some classical control problems become more complicated. Automatic obstacle avoidance ability is very important to improve teleoperation performance
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