LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Definition and Application of Variable Resistance Coefficient for Wheeled Mobile Robots on Deformable Terrain

Photo from wikipedia

Resistance coefficient (RC) is an important measure when designing wheel-driving mechanisms and accurate dynamic models for real-time mobility control of wheeled mobile robots (WMRs). This measure is typically formulated as… Click to show full abstract

Resistance coefficient (RC) is an important measure when designing wheel-driving mechanisms and accurate dynamic models for real-time mobility control of wheeled mobile robots (WMRs). This measure is typically formulated as a constant that depends on the wheel load, wheel dimensions, and soil that the WMR is designed for. This article proposes a novel variable RC that responds to terrain deformation. This variable RC is then applied to controllers for WMRs that estimate driving torques and slip ratios on deformable terrain. Simple yet accurate models of RC are developed from both experimental results and theoretical analysis, and these models are then compared with other methods. The proposed RC models give more accurate and more computationally efficient estimations of driving torques and slip ratios for WMRs, with average estimation errors less than 6% and the shortest computation time in experiments. The two proposed estimators are then applied to the design of the tracking-control systems for a WMR running on deformable terrain. Experiments with simulated sandy terrain demonstrate that both proposed control systems are feasible, and the slip estimation effectively decreases velocity tracking errors from more than 20% to less than 10%.

Keywords: deformable terrain; resistance coefficient; wheeled mobile; mobile robots

Journal Title: IEEE Transactions on Robotics
Year Published: 2020

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.