Parameter identification and modeling of contact properties for robotic applications

Abstract

AcknowledgmentIn first place I would like to thank the Centre for Automation and Robotics UPM-CSIC in which this thesis was carried out, and the Spanish National Research Council for funding my research.I am especially grateful to Dr. Elena Garcia under whom direction and advise this research has been carried out.I would like to express my gratitude towards my coworkers (Daniel, Manuel, Fernando, Luis, etc.) for they were very supportive and a great aid at some moments.Finally, I would like to thank Mariana for her support and comprehension, and my parents and family for their unconditional support. Without them, this would not have been possible. AbstractThe sense of touch is especially important in task performance in unstructured environments. Many of the tasks we perform normally would be problematic, if not impossible, without sensory information of this type. Daily tasks that we take for granted, such as chewing or walking require the use of tactile information (pressure, exerted force, etc.) to be done. Thus, it is logical to think that in order to build robots capable of performing tasks in unstructured environments, it is necessary to provide them with the same information.A research area that would benefit from the inclusion of tactile information, and one that is of particular interest to robotics, is biomimetic locomotion. Improving biomimetic locomotion can impact fields such as service robotics, rehabilitation robotics, and search and rescue robots. For this reason, we will focus on biomimetic locomotion, but we will keep in other applications that can use haptic perception.Animals change their apparent leg stiffness when changing from a rigid surface to a softer one. This change is done because animals and humans maintain the same center of mass trajectory in different surfaces; thus, a change on the apparent stiffness must occur. The realization of this change requires information on the contact properties of the environment. How to extract this information is a question that still needs much research. Because of this, we have done an experimental study that compares the performance of different system identification techniques when they are applied to contact modeling. During the evaluation of the results it was found that the recursive least squares method has the best performance for haptic applications. Based on the results of this study we have selected the recursive least squares method and the spring dashpot model. With this design parameters, we devised an algorithm to be used in a robotic leg.The algorithm was implemented on a 3-degree-of-freedom (DoF) underactuated leg and then it was tested using four different terrains. The results show that the algorithm was capable of differentiating between terrains according to their stiffness and that the convergence time was under the average time a human runner is in contact with the ground. Nevertheless, the algorithm was not able to differentiate the damping coefficient in granular media. This result is to be expected because granular media...

Monocular vision-based multiinformation map construction framework for field terrain

“… – 8 With stiffness information, they can adjust more beneficial leg stiffness to adapt to different terrains 9 – 11 According to the physical properties of terrain, wheeled robots can avoid excessive wheel slip and sinking, which can save energy, improve safety, and reduce odometer error 12 , 13 . The combination of 3D spatial information, terrain type information and physical properties information helps to establish a better environmental model, which can be used to generate multivariate cost maps and guide mobile robots to achieve better motion planning and body control 14 , 15 …”