Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments - a case study

Jaime Zabalza, Zixiang Fei, Cuebong Wong, Yijun Yan, Carmelo Mineo, Erfu Yang, Tony Rodden, Jorn Mehnen, Quang Cuong Pham, Jinchang Ren

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
15 Downloads (Pure)


Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a KUKA KR90 R3100, is provided with smart sensing capabilities such as vision and adaptive reasoning for real-time collision avoidance and online path planning in dynamically-changing environments. A machine vision module based on low-cost cameras and color detection in the hue, saturation, value (HSV) space is developed to make the robot aware of its changing environment. Therefore, this vision allows the detection and localization of a randomly moving obstacle. Path correction to avoid collision avoidance for such obstacles with robotic manipulator is achieved by exploiting an adaptive path planning module along with a dedicated robot control module, where the three modules run simultaneously. These sensing/smart capabilities allow the smooth interactions between the robot and its dynamic environment, where the robot needs to react to dynamic changes through autonomous thinking and reasoning with the reaction times below the average human reaction time. The experimental results demonstrate that effective human-robot and robot-robot interactions can be realized through the innovative integration of emerging sensing techniques, efficient planning algorithms and systematic designs.
Original languageEnglish
Article number1354
Number of pages23
Issue number6
Publication statusPublished - 18 Mar 2019


  • adaptive reasoning
  • dynamic environments
  • human-robot interaction
  • path planning
  • Robot control
  • smart sensing

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