Investigations on the effect of wall thickness on magnetic adhesion for wall climbing robots

The focus of this work is to investigate the adhesion characteristics of a permanent magnet arrangement over ferromagnetic surfaces for wall climbing robot applications. The changes in wall thickness affect the adhesion characteristics of the robot, this in turn influences the payload and alters the operating conditions. The effect of varying the wall thickness on the adhesion strength of a wall-climbing robot is an area barely investigated and this is being explored in this work. A two-dimensional model of the adhesion mechanism and the ferromagnetic surface is developed and simulated in this study. The adhesion characteristics are studied for different thicknesses of the ferromagnetic surface with different grades of the magnet. Two different standoff distances which comprise the gap between the magnet and the surface to be inspected are investigated therein. Experimental studies are also carried out to measure the performance, and the results show a strong correlation with the simulation results. Simulation with experimental validation of magnetic adhesion presented will provide better insights into magnetic wall climbing systems

Design and parametric investigations of permanent magnet adhesion mechanism for robots climbing on reinforced concrete walls

Wall Climbing Robots (WCRs) have found extensive applications in the past decade in numerous engineering fields, however, the design of efficient adhesion mechanism for robots climbing on concrete surfaces remains a challenge and attracts research attention. This paper proposes various designs of magnetic adhesion mechanism for concrete surfaces and investigates the adhesion force and payload capacities each design would accommodate for wall climbing robot applications. Permanent magnet is used as the magnetic adhesion mechanism and a yoke structure helps in holding the magnets and influences the adhesion characteristics of the mechanism. The effect of various structural designs of adhesion mechanisms on the adhesion force and payload capacity on the concrete surface is studied in this work. The adhesion forces against the different standoff distances which comprise the gap between the magnet and the concrete surface are also investigated therein. The results show that the developed adhesion mechanism can be applied for concrete walls generating the required adhesion forces and providing a better insight in choosing the best configuration, number of magnets and standoff distances for the design of adhesion mechanism against the required payload of WCR

2020 Technical Program

INSPIRE University Transportation Center 2020 Annual MeetingAugust 3-4, 202

A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots

Re-configurable modular robotic (RMR) systems are advantageous for their reconfigurability and versatility. A new modular robot can be built for a specific task by using modules as building blocks. However, constructing a kinematic model for a newly conceived robot requires significant work. Due to the finite size of module-types, models of all module-types can be built individually and stored in a database beforehand. With this priori knowledge, the model construction process can be automated by detecting the modules and their corresponding interconnections. Previous literature proposed theoretical frameworks for constructing kinematic models of modular robots, assuming that such information was known a priori. While well-devised mechanisms and built-in sensors can be employed to detect these parameters automatically, they significantly complicate the module design and thus are expensive. In this paper, we propose a vision-based method to identify kinematic chains and automatically construct robot models for modular robots. Each module is affixed with augmented reality (AR) tags that are encoded with unique IDs. An image of a modular robot is taken and the detected modules are recognized by querying a database that maintains all module information. The poses of detected modules are used to compute: (i) the connection between modules and (ii) joint angles of joint-modules. Finally, the robot serial-link chain is identified and the kinematic model constructed and visualized. Our experimental results validate the effectiveness of our approach. While implementation with only our RMR is shown, our method can be applied to other RMRs where self-identification is not possible