Abstract
Mobile manipulation, which has more flexibility than fixed-base manipulation, has always been an important topic in the field of robotics. However, for sophisticated operation in complex environments, efficient localization and dynamic tracking grasp still face enormous challenges. To address these challenges, this paper proposes a mobile manipulation method integrating laser-reflector-enhanced adaptive Monte Carlo localization (AMCL) algorithm and a dynamic tracking and grasping algorithm. First, by fusing the information of laser-reflector landmarks to adjust the weight of particles in AMCL, the localization accuracy of mobile platforms can be improved. Second, deep-learning-based multiple-object detection and visual servo are exploited to efficiently track and grasp dynamic objects. Then, a mobile manipulation system integrating the above two algorithms into a robotic with a 6-degrees-of-freedom (DOF) operation arm is implemented in an indoor environment. Technical components, including localization, multiple-object detection, dynamic tracking grasp, and the integrated system, are all verified in real-world scenarios. Experimental results demonstrate the efficacy and superiority of our method.
Highlights
Mobile manipulation is an important issue in the field of robotics
To elaborate on the method we propose in this paper, the mobile manipulation system is divided into the simultaneous localization and mapping (SLAM) subsystem, including the mobile chassis and navigation system, and the grasping subsystem, including the manipulation arm and perception system
In 20 repeated experiments, all methods successfully navigated the mobile manipulator to the target point, but the position errors vary between methods
Summary
Mobile manipulation is an important issue in the field of robotics. Though the breaking down of sophisticated tasks into many single tasks, fixed-base manipulators, such as palletizing robots [1] and assembly robots [2], can complete tasks with high precision via pre-programming. With the developments of robotic technology and artificial intelligence, mobile manipulation has become increasingly practical. In a typical mobile manipulating process, the mobile manipulator should autonomously and safely reach the operating space, avoiding obstacles and achieving self-localization during movement. It will proactively detect and locate the target and complete the grasping task
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
