JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering 2019 Vol.20 No.3 P.307-317

http://doi.org/10.1631/FITEE.1900096

KeJia: towards an autonomous service robot with tolerance of unexpected environmental changes

Author(s): Wei Shuai, Xiao-ping Chen
Affiliation(s): DDepartment of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
Corresponding email(s): swwsag@mail.ustc.edu.cn, xpchen@ustc.edu.cn
Key Words: Robot, Task planning, Manipulation

Share this article to： More \|Next Article >>>

Abstract: KeJia is a domestic service robot, consisting of a mobile base, an arm, two cameras, and a set of software components for perception, manipulation, natural language understanding, motion and task planning, and decision making. With on-line running of these functions, a robot can adapt to dynamic environments which may have unexpected changes. In this paper, we propose a novel hierarchical method which combines motion planning with a neural network, so that the robot can tolerate errors from sensors, wear of parts, and human disturbances during motion execution. We evaluate our work on KeJia that cooks popcorn using a microwave oven, where humans try to disturb KeJia during the operation.

可佳：一种容忍环境变化的自助服务机器人

摘要：可佳是一个家庭服务机器人。硬件结构由一个可移动底盘、一个机械臂、两个相机构成；软件系统包括6个模块：感知、物体操纵、自然语言理解、运动规划、任务规划和整合决策。通过在线运行这些模块，机器人能够适应不可预知的动态环境。提出一个分层方法，将运动规划结果和神经网络结合，让机器人在执行动作时能够容忍传感器、齿轮间隙和人工干预造成的误差。在可佳机器人上验证了该方法，并让可佳在不断人为干扰下成功操纵微波炉烹饪爆米花。

关键词：机器人；任务规划；物品操纵

Reference

[1]Blackburn P, Bos J, 2005. Representation and inference for natural language: a first course in computational semantics. Comput Ling, 32(2):283-286.

[2]Bohren J, Cousins S, 2010. The SMACH high-level executive [ROS news]. IEEE Robot Automat, 17(4):18-20.

[3]Chen K, Lu DC, Chen YF, et al., 2014. The intelligent techniques in robot KeJia—the champion of RoboCup@Home 2014. LNCS, 8992:130-141.

[4]Chen K, Yang FK, Chen XP, 2016. Planning with task-oriented knowledge acquisition for a service robot. Proc 25^th Int Joint Conf on Artificial Intelligence, p.812-818.

[5]Chen XP, Ji JM, Jiang JQ, et al., 2010. Developing high-level cognitive functions for service robots. Proc 9^th Int Conf on Autonomous Agents and Multiagent Systems, p.989-996.

[6]Chen YF, Shuai W, Chen XP, 2015. A probabilistic, variable-resolution and effective quadtree representation for mapping of large environments. Int Conf on Advanced Robotics, p.605-610.

[7]Corazza S, Müendermann L, Chaudhari AM, et al., 2006. A markerless motion capture system to study musculoskeletal biomechanics: visual hull and simulated annealing approach. Ann Biomed Eng, 34(6):1019-1029.

[8]Cui GW, Chen GD, Zhang ZK, et al., 2018. A flexible grasping policy based on simple robot-camera calibration and pose repeatability of arm. Int Conf on Intelligent Robotics and Applications, p.89-99.

[9]Erdem E, Aker E, Patoglu V, 2012. Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution. Intell Serv Robot, 5(4):275-291.

[10]Gebser M, Kaminski R, Kaufmann B, et al., 2008. Engineering an incremental ASP solver. LNCS, 5366:190-205.

[11]Grisetti G, Stachniss C, Burgard W, 2007. Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans Robot, 23(1):34-46.

[12]Klein D, Manning CD, 2003. Accurate unlexicalized parsing. Proc 41^st Annual Meeting on Association for Computational Linguistics, p.423-430.

[13]Koenig N, Howard A, 2004. Design and use paradigms for Gazebo, an open-source multi-robot simulator. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.2149-2154.

[14]Krizhevsky A, Sutskever I, Hinton GE, 2012. ImageNet classification with deep convolutional neural networks. Proc 25^th Int Conf on Neural Information Processing Systems, p.1097-1105.

[15]Kuffner JJ, LaValle SM, 2000. RRT-connect: an efficient approach to single-query path planning. Proc IEEE Int Conf on Robotics and Automation, p.995-1001.

[16]Levine S, Pastor P, Krizhevsky A, et al., 2018. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int J Robot Res, 37(4-5):421-436.

[17]Lifschitz V, 2008. What is answer set programming? Proc 23^rd AAAI Conf on Artificial Intelligence, p.1594-1597.

[18]Mahler J, Liang J, Niyaz S, et al., 2017. Dex-net 2.0: deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. https://arxiv.org/abs/1703.09312

[19]Murray RM, 2017. A Mathematical Introduction to Robotic Manipulation. CRC Press, London, UK.

[20]Pinto L, Gupta A, 2016. Supersizing self-supervision: learning to grasp from 50K tries and 700 robot hours. IEEE Int Conf on Robotics and Automation, p.3406-3413.

[21]Popov I, Heess N, Lillicrap T, et al., 2017. Data-efficient deep reinforcement learning for dexterous manipulation. https://arxiv.org/abs/1704.03073

[22]Quigley M, Conley K, Gerkey B, et al., 2009. ROS: an open-source robot operating system. Proc ICRA Workshop on Open Source Software, p.1-6.

[23]Rusu RB, Cousins S, 2011. 3D is here: Point Cloud Library (PCL). IEEE Int Conf on Robotics and Automation, p.1-4.

[24]Sakagami Y, Watanabe R, Aoyama C, et al., 2002. The intelligent ASIMO: system overview and integration. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.2478-2483.

[25]Ulrich I, Borenstein J, 1998. VFH+: reliable obstacle avoidance for fast mobile robots. Proc IEEE Int Conf on Robotics and Automation, p.1572-1577.

[26]Vannoy J, Xiao J, 2008. Real-time adaptive motion planning (RAMP) of mobile manipulators in dynamic environments with unforeseen changes. IEEE Trans Robot, 24(5):1199-1212.

[27]Wisspeintner T, van der Zant T, Iocchi L, et al., 2009. RoboCup@Home: scientific competition and benchmarking for domestic service robots. Interact Stud, 10(3):392-426.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Similar articles

- Go to

可佳：一种容忍环境变化的自助服务机器人

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference