Session: CIE-24-02 - AMS-CAPPD-SEIKEM: Artificial Intelligence and Machine Learning in Design and Manufacturing

Paper Number: 90105

90105 - Finding Optimal Sequence of Mobile Manipulator Placements for Automated Coverage Planning of Large Complex Parts 

Sensors are widely used in the industry to collect information about a physical object. Operational range of the sensor is limited and therefore the sensor needs to be moved around a large complex part in order to capture complete information. Robot arm or manipulators can provide the degrees of freedom needed to maneuver the sensor through the complex geometry. However, a robotic arm has a limited workspace as well and cannot cover large parts. Mobile base can enhance the capability of the robotic arm by adding mobility to the arm and carrying the arm around the part. Mobile base will need to relocate around the part during the process. Relocating the mobile base increases execution time and also introduces uncertainty in the localization as mobile base moves inaccurately. It is important to reduce the number of mobile base repositioning and reduce execution time and uncertainty. In this paper, we develop a motion planner that finds the minimum number of mobile base placements in order to find robotic arm trajectories that can cover a large complex part using a RGB-D camera sensor. The planning problem, also known as optimal base sequencing, is challenging due to the immensity of the search space. The computation costs involved in inverse kinematics calculations also adds to the search time. A branch and bound search algorithm is developed with efficient branch guiding and pruning heuristics that quickly explores the search space. A capability map based method is developed to improve the search space construction time. Output of our method is an optimal sequence of base placements for the mobile base that will lead to minimum number of placements and execution time required for the process.

Presenting Author: Rishi Malhan University of Southern California

Presenting Author Biography: Rishi Malhan is interested in realizing smart robotic assistants through advances in artificial intelligence. His research is focused on motion planning and self-directed learning for high degrees of freedom systems. He is currently pursuing his Ph.D. in Robotics and AI at the Center for Advanced Manufacturing, University of Southern California (USC) with Dr. Satyandra K. Gupta. I am primarily involved in developing algorithms (a) to find planning solutions to computationally challenging problems (b) process parameter estimation for improving productivity under process constraints. His research interests include reinforcement learning, deep learning, numerical optimization, manipulator motion planning, machine learning, manufacturing, and automation.

Authors:

Rishi Malhan University of Southern California
Satyandra Gupta University of Southern California