An important issue in the field of motion control of wheeled mobile robots is that the design of most controllers is based only on the robot’s kinematics. However, when high-speed movements and/or heavy load transportation are required, it becomes essential to consider the robot dynamics as well. The control signals generated by most dynamic controllers reported in the literature are torques or voltages for the robot motors, while commercial robots usually accept velocity commands. In this context, we present a velocity-based dynamic model for differential drive mobile robots that also includes the dynamics of the robot actuators. Such model has linear and angular velocities as inputs and has been included in Peter Corke’s Robotics Toolbox for MATLAB, therefore it can be easily integrated into simulation systems that have been built for the unicycle kinematics. We demonstrate that the proposed dynamic model has useful mathematical properties. We also present an application of such model on the design of an adaptive dynamic controller and the stability analysis of the complete system, while applying the proposed model properties. Finally, we show some simulation and experimental results and discuss the advantages and limitations of the proposed model.
In their study "How Perceived Fit Affects Customers’ Satisfaction of In-Store Social Robot Advice", Stephanie van de Sanden, Tibert Verhagen, Ewout Nas, Jacqueline Arnoldy, and Koen Hindriks explore how various dimensions of perceived fit influence customer attitudes and satisfaction toward social robots providing product advice in retail settings. Drawing on theories from marketing and information systems, the authors conceptualize four types of technology fit—task-technology, individual-technology, store-technology, and shopping experience-technology—and propose a model linking these fits to customer attitudes and satisfaction. A field study conducted in a garden center using a robot that advised on potting soil involved 224 participants, whose responses were measured through established Likert and semantic differential scales. The findings aim to inform future design and deployment of social robots in retail by highlighting the importance of contextual and experiential alignment between the robot, task, customer, and environment.
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The estimation of the pose of a differential drive mobile robot from noisy odometer, compass and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment. https://doi.org/10.4018/IJAIML.2020010101 https://www.linkedin.com/in/john-bolte-0856134/
