Tele Running - Energy Efficient Locomotion for Elastic Joint Robots by Imitation Learning

This thesis presents an imitation learning approach to energy-efficient trajectory generation for elastic, legged robots. The trajectories are generated by teleoperation with force feedback. The presented framework allows an operator to achieve locomotion on an one-leg hopper by controlling its foot tip. The force feedback is designed to assist the operator to find gaits which exploit the natural harmonics of the hopper and thus improve energy efficiency. The resulting trajectory is approximated, parameterized, and replayed on the robot. The operator achieves a cost of transport of 0.25 at 0.63 m/s, considering the mechanical energy. Black-box optimization is used to keep this value with varying hardware parameters, such as different foot-tip stiffness. A reinforcement learning algorithm stabilizes lateral movement by active balance in simulation. Learning on hardware shows an improvement in stability. The concept is extended to multi-legged robots by teleoperating the two feet of the biped DLR C-Runner in simulation. The force feedback assists the operator to find stable gaits where the center of mass does not leave the support polygon of the feet. On both systems, the presented teleoperation framework utilizes the human's capability of estimating the properties of non-linear dynamics by designing appropriate haptic feedback

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction

A Holistic Approach to Energy Harvesting for Indoor Robots:Theoretical Framework and Experimental Validations

Service robotics is a fast expanding market. Inside households, domestic robots can now accomplish numerous tasks, such as floor cleaning, surveillance, or remote presence. Their sales have considerably increased over the past years. Whereas 1.05 million domestic service robots were reportedly sold in 2009, at least 2.7 million units were sold in 2013. Consequently, this growth gives rise to an increase of the energy needs to power such a large and growing fleet of robots. However, the unique properties of mobile robots open some new fields of research. We must find technologies that are suitable for decreasing the energy requirements and thus further advance towards a sustainable development. This thesis tackles two fundamental goals based on a holistic approach of the global problem. The first goal is to reduce the energy needs by identifying key technologies in making energy-efficient robots. The second goal is to leverage innovative indoor energy sources to increase the ratio of renewable energies scavenged from the environment. To achieve our first goal, new energy-wise metrics are applied to real robotic hardware. This gives us the means to assess the impact of some technologies on the overall energy balance. First, we analysed seven robotic vacuum cleaners from a representative sample of the market that encompasses a wide variety of technologies. Simultaneous Localisation and Mapping (SLAM) was identified as a key technology to reduce energy needs when carrying out such tasks. Even if the instantaneous power is slightly increased, the completion time of the task is greatly reduced. We also analysed the needed sensors to achieve SLAM, as they are largely diversified. This work tested three sensors using three different technologies. We identified several important metrics. As of our second goal, potential energy sources are compared to the needs of an indoor robot. The sunshine coming through a building's apertures is identified as a promising source of renewable power. Numerical simulations showed how a mobile robot is mandatory to take full advantage of this previously unseen situation, as well as the influence of the geometric parameters on the yearly energy income under ideal sunny conditions. When considering a real system, the major difficulty to overcome is the tracking of the sunbeam along the day. The proposed algorithm uses a hybrid method. A high-level cognitive approach is responsible for the initial placement. Following realignments during the day are performed by a low-level reactive behaviour. A solar harvesting module was developed for our research robot. The tests conducted inside a controlled environment demonstrate the feasibility of this concept and the good performances of the aforementioned algorithm. Based on a realistic scenario and weather conditions, we computed that between 1 and 14 days of recharge could be necessary for a single cleaning task. In the future, our innovative technology could greatly lower the energy needs of service robots. However, it is not completely possible to abandon the recharge station due to occasional bad weather. The acceptance of this technology inside the user's home ecosystem remains to be studied

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described