An angle-changeable tracked robot with human-robot interaction in unstructured environments

The purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs.,The paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs.,This paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method.,The theoretical analysis of this paper provides basic reference for the structural design of tracked robots.

[1]  Jianzhong Shang,et al.  A reconfigurable tracked mobile robot based on four-linkage mechanism , 2013 .

[2]  Junyao Gao,et al.  A Mobile Robot Platform with Double Angle-Changeable Tracks , 2009, Adv. Robotics.

[3]  Shenglan Liu,et al.  Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling , 2016 .

[4]  Bin Yao,et al.  Performance-Oriented Coordinated Adaptive Robust Control for Four-Wheel Independently Driven Skid Steer Mobile Robot , 2017, IEEE Access.

[5]  Chun-Yi Su,et al.  Neural Control of Bimanual Robots With Guaranteed Global Stability and Motion Precision , 2017, IEEE Transactions on Industrial Informatics.

[6]  Zhijian Ji,et al.  Controllability of multiagent systems based on path and cycle graphs , 2018 .

[7]  Ke Wang,et al.  A bio-inspired scan matching algorithm for mobile robots in outdoor environments , 2016 .

[8]  Ruifeng Li,et al.  Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer , 2018, IEEE Transactions on Automation Science and Engineering.

[9]  Jing Na,et al.  Adaptive Parameter Estimation and Control Design for Robot Manipulators With Finite-Time Convergence , 2018, IEEE Transactions on Industrial Electronics.

[10]  Haisheng Yu,et al.  A New Perspective to Graphical Characterization of Multiagent Controllability , 2017, IEEE Transactions on Cybernetics.

[11]  Hai Lin,et al.  Protocols Design and Uncontrollable Topologies Construction for Multi-Agent Networks , 2015, IEEE Transactions on Automatic Control.

[12]  Guangming Xie,et al.  Coordination of Multiple Robotic Fish With Applications to Underwater Robot Competition , 2016, IEEE Transactions on Industrial Electronics.

[13]  Jae-Yun Jun,et al.  Pose estimation-based path planning for a tracked mobile robot traversing uneven terrains , 2016, Robotics Auton. Syst..

[14]  Changyin Sun,et al.  Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[15]  Junzhi Yu,et al.  Depth Control of a Bioinspired Robotic Dolphin Based on Sliding-Mode Fuzzy Control Method , 2018, IEEE Transactions on Industrial Electronics.

[16]  Robin R. Murphy,et al.  Human-robot interaction in rescue robotics , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[17]  Guangjun Liu,et al.  Track--Stair Interaction Analysis and Online Tipover Prediction for a Self-Reconfigurable Tracked Mobile Robot Climbing Stairs , 2009, IEEE/ASME Transactions on Mechatronics.

[18]  Toshio Tsuji,et al.  A virtual myoelectric prosthesis training system capable of providing instructions on hand operations , 2017 .

[19]  Jianwei Zhang,et al.  A Survey on CPG-Inspired Control Models and System Implementation , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[20]  Sungchul Kang,et al.  Double-track mobile robot for hazardous environment applications , 2001, Adv. Robotics.

[21]  Changyin Sun,et al.  Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer , 2017, IEEE Transactions on Cybernetics.

[22]  Peter I. Corke,et al.  Rubbing Shoulders With Mobile Service Robots , 2015, IEEE Access.

[23]  De Xu,et al.  Embedded Vision-Guided 3-D Tracking Control for Robotic Fish , 2016, IEEE Transactions on Industrial Electronics.