RB
R.B. Burger
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1
A large number of novel path planning methods for a wide range of problems have been described in literature over the past few decades. These algorithms can often be configured using a set of parameters that greatly influence their performance. In a typical use case, these parameters are only very slightly tuned or even left untouched. Systematic approaches to tune parameters of path planning algorithms have been largely unexplored. At the same time, there is a rising interest in the planning and robotics communities regarding the real world application of these theoretically developed and simulation-tested planning algorithms. In this work, we propose the use of Sequential Model-based Algorithm Configuration (SMAC) tools to address these concerns. We show that it is possible to improve the performance of a planning algorithm for a specific problem without the need of in-depth knowledge of the algorithm itself. We compare five planners that see a lot of practical usage on three typical industrial pick-and-place tasks to demonstrate the effectiveness of the method.
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A large number of novel path planning methods for a wide range of problems have been described in literature over the past few decades. These algorithms can often be configured using a set of parameters that greatly influence their performance. In a typical use case, these parameters are only very slightly tuned or even left untouched. Systematic approaches to tune parameters of path planning algorithms have been largely unexplored. At the same time, there is a rising interest in the planning and robotics communities regarding the real world application of these theoretically developed and simulation-tested planning algorithms. In this work, we propose the use of Sequential Model-based Algorithm Configuration (SMAC) tools to address these concerns. We show that it is possible to improve the performance of a planning algorithm for a specific problem without the need of in-depth knowledge of the algorithm itself. We compare five planners that see a lot of practical usage on three typical industrial pick-and-place tasks to demonstrate the effectiveness of the method.
Conference paper
(2017)
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C. Hernandez Corbato, Mukunda Bharatheesha, Mihai Morariu, Jihong Ju, X. Gerrmann, Ronald Ensing, Jan Van Frankenhuyzen, Martijn Wisse, Wilson Ko, Hans Gaiser, Jethro Tan, Kanter van Deurzen, Maarten de Vries, Bas Van Mil, Jeff van Egmond, Ruben Burger
This paper describes Team Delft’s robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions. The goal of the challenge is to automate pick and place operations in unstructured environments, specifically the shelves in an Amazon warehouse. Team Delft’s robot is based on an industrial robot arm, 3D cameras and a customized gripper. The robot’s software uses ROS to integrate off-the-shelf components and modules developed specifically for the competition, implementing Deep Learning and other AI techniques for object recognition and pose estimation, grasp planning and motion planning. This paper describes the main components in the system, and discusses its performance and results at the Amazon Picking Challenge 2016 finals.
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This paper describes Team Delft’s robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions. The goal of the challenge is to automate pick and place operations in unstructured environments, specifically the shelves in an Amazon warehouse. Team Delft’s robot is based on an industrial robot arm, 3D cameras and a customized gripper. The robot’s software uses ROS to integrate off-the-shelf components and modules developed specifically for the competition, implementing Deep Learning and other AI techniques for object recognition and pose estimation, grasp planning and motion planning. This paper describes the main components in the system, and discusses its performance and results at the Amazon Picking Challenge 2016 finals.