A framework for safe human-humanoid coexistence

This work is focused on the development of a safety framework for Human-Humanoid coexistence, with emphasis on humanoid locomotion. After a brief introduction to the fundamental concepts of humanoid locomotion, the two most common approaches for gait generation are presented, and are extended with the inclusion of a stability condition to guarantee the boundedness of the generated trajectories. Then the safety framework is presented, with the introduction of different safety behaviors. These behaviors are meant to enhance the overall level of safety during any robot operation. Proactive behaviors will enhance or adapt the current robot operations to reduce the risk of danger, while override behaviors will stop the current robot activity in order to take action against a particularly dangerous situation. A state machine is defined to control the transitions between the behaviors. The behaviors that are strictly related to locomotion are subsequently detailed, and an implementation is proposed and validated. A possible implementation of the remaining behaviors is proposed through the review of related works that can be found in literature

Designing an algorithm for bioloid humanoid navigating in its indoor environment

Gait analyses are the preliminary requirements to establish a navigation system of a humanoid robot. Designing a suitable indoor environment and its mapping are also important for the android localization, selection of a goal to achieve it and to perform the assigned tasks in its surroundings. This paper delineates the various gaits like walking, turning, obstacle overcoming and step up-down stairs for a humanoid system. The writing also explicates the design of the indoor test environment with the stationary obstacles placed on the navigation routes. The development of an efficient algorithm is also excogitated based on the various analyses of gaits and the predefined map of the test environment. As the navigation map is predetermined, the designed algorithm animates the humanoid to navigate by selecting an optimal route, depending on some external commands, to reach at the goal position. Finally the performance of the system is analysed based on the elapsed time of the navigation action with the validation of optimal navigation strategy where the designed algorithm demonstrates the robustness of its implementation and execution

Whole-body MPC for highly redundant legged manipulators: experimental evaluation with a 37 DoF dual-arm quadruped

Recent progress in legged locomotion has rendered quadruped manipulators a promising solution for performing tasks that require both mobility and manipulation (loco-manipulation). In the real world, task specifications and/or environment constraints may require the quadruped manipulator to be equipped with high redundancy as well as whole-body motion coordination capabilities. This work presents an experimental evaluation of a whole-body Model Predictive Control (MPC) framework achieving real-time performance on a dual-arm quadruped platform consisting of 37 actuated joints. To the best of our knowledge this is the legged manipulator with the highest number of joints to be controlled with real-time whole-body MPC so far. The computational efficiency of the MPC while considering the full robot kinematics and the centroidal dynamics model builds upon an open-source DDP-variant solver and a state-of-the-art optimal control problem formulation. Differently from previous works on quadruped manipulators, the MPC is directly interfaced with the low-level joint impedance controllers without the need of designing an instantaneous whole-body controller. The feasibility on the real hardware is showcased using the CENTAURO platform for the challenging task of picking a heavy object from the ground. Dynamic stepping (trotting) is also showcased for first time with this robot. The results highlight the potential of replanning with whole-body information in a predictive control loop.Comment: Accepted at the 2023 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2023), final version with video and acknowledgement