DaDu Series - Fast and Efficient Robot Accelerators

Research on accelerators for robotics is increasing. This article introduces the kinematics, motion planning and collision detection algorithms and our accelerators in robotics, and then analyzes their advantages, disadvantages and bottlenecks. In view of the shortcomings of the existing accelerators, this paper will show a series accelerators named “DaDu” that we have proposed. For kinematics, we have proposed Dadu [1] to accelerate the inverse kinematics algorithm, which achieves 1700x speedup than the CPU implementation, 30x speedup than the GPU implementation, and 776x higher energy efficiency than the GPU implementation. For motion planning, we have proposed Dadu-P [2] to accelerate the PRM algorithm. It can get 26.5x speedup than an existing CPU-based approach for collision detection. Furthermore, with an incremental approach, the performance of motion planning can further be improved by 10x while the solution quality is degraded by 10% only. For the collision detection algorithm in motion planning, the proposed accelerator Dadu-CD [3] elaborates the in-memory processing architecture, achieving at least 5x speedup than Dadu-P in the total planning time and 9.55x lower energy consumption than Dadu-P.

[1]  Takeo Kanade,et al.  Automated Construction of Robotic Manipulation Programs , 2010 .

[2]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[3]  Neil W. Bergmann,et al.  Parallel RRT∗ architecture design for motion planning , 2017, 2017 27th International Conference on Field Programmable Logic and Applications (FPL).

[4]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[5]  S. LaValle Rapidly-exploring random trees : a new tool for path planning , 1998 .

[6]  Seth Hutchinson,et al.  A Framework for Real-time Path Planning in Changing Environments , 2002, Int. J. Robotics Res..

[7]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[8]  Rüdiger Dillmann,et al.  Unified GPU voxel collision detection for mobile manipulation planning , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Chris L. Jackins,et al.  Oct-trees and their use in representing three-dimensional objects , 1980 .

[10]  Ronan Boulic,et al.  Parallel Inverse Kinematics for Multithreaded Architectures , 2016, ACM Trans. Graph..

[11]  Xiaoming Chen,et al.  Dadu-CD: Fast and Efficient Processing-in-Memory Accelerator for Collision Detection , 2020, 2020 57th ACM/IEEE Design Automation Conference (DAC).

[12]  Onur Mutlu,et al.  Ambit: In-Memory Accelerator for Bulk Bitwise Operations Using Commodity DRAM Technology , 2017, 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[13]  Andreas Koch,et al.  Efficient Inverse Kinematics Algorithm Based on Conformal Geometric Algebra - Using Reconfigurable Hardware , 2008, GRAPP.

[14]  Yinhe Han,et al.  Dadu-P: A Scalable Accelerator for Robot Motion Planning in a Dynamic Environment , 2018, 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC).

[15]  S. Buss Introduction to Inverse Kinematics with Jacobian Transpose , Pseudoinverse and Damped Least Squares methods , 2004 .

[16]  George Konidaris,et al.  The microarchitecture of a real-time robot motion planning accelerator , 2016, 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[17]  Patrick Beeson,et al.  TRAC-IK: An open-source library for improved solving of generic inverse kinematics , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[18]  Dinesh Manocha,et al.  g-Planner: Real-time Motion Planning and Global Navigation using GPUs , 2010, AAAI.

[19]  Bruce Jacob,et al.  DRAMSim2: A Cycle Accurate Memory System Simulator , 2011, IEEE Computer Architecture Letters.

[20]  Dinesh Manocha,et al.  GPU-based parallel collision detection for fast motion planning , 2012, Int. J. Robotics Res..

[21]  Samuel R. Buss,et al.  Selectively Damped Least Squares for Inverse Kinematics , 2005, J. Graph. Tools.

[22]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[23]  Emilio Frazzoli,et al.  Massively parallelizing the RRT and the RRT , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Ying-Shieh Kung,et al.  Fixed-point computation of robot kinematics in FPGA , 2014, 2014 International Conference on Advanced Robotics and Intelligent Systems (ARIS).

[25]  Xiaowei Li,et al.  Dadu: Accelerating Inverse Kinematics for high-DOF robots , 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).