On the Hardware Feasibility of Nonlinear Trajectory Optimization for Legged Locomotion based on a Simplified Dynamics

Simplified models are useful to increase the computational efficiency of a motion planning algorithm, but their lack of accuracy have to be managed. We propose two feasibility constraints to be included in a Single Rigid Body Dynamics-based trajectory optimizer in order to obtain robust motions in challenging terrain. The first one finds an approximate relationship between joint-torque limits and admissible contact forces, without requiring the joint positions. The second one proposes a leg model to prevent leg collision with the environment. Such constraints have been included in a simplified nonlinear non-convex trajectory optimization problem. We demonstrate the feasibility of the resulting motion plans both in simulation and on the Hydraulically actuated Quadruped (HyQ) robot, considering experiments on an irregular terrain

Heterogeneous Sensor Fusion for Accurate State Estimation of Dynamic Legged Robots

In this paper we present a system for the state estimation of a dynamically walking and trotting quadruped. The approach fuses four heterogeneous sensor sources (inertial, kinematic, stereo vision and LIDAR) to maintain an accurate and consistent estimate of the robot’s base link velocity and position in the presence of disturbances such as slips and missteps. We demonstrate the performance of our system, which is robust to changes in the structure and lighting of the environment, as well as the terrain over which the robot crosses. Our approach builds upon a modular inertial-driven Extended Kalman Filter which incorporates a rugged, probabilistic leg odometry component with additional inputs from stereo visual odometry and LIDAR registration. The simultaneous use of both stereo vision and LIDAR helps combat operational issues which occur in real applications. To the best of our knowledge, this paper is the first to discuss the complexity of consistent estimation of pose and velocity states, as well as the fusion of multiple exteroceptive signal sources at largely different frequencies and latencies, in a manner which is acceptable for a quadruped’s feedback controller. A substantial experimental evaluation demonstrates the robustness and accuracy of our system, achieving continuously accurate localization and drift per distance traveled below 1 cm/m

Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs

Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees remains a challenge. We present here a real-time, dynamic foothold adaptation strategy based on visual feedback. Our method adjusts the landing position of the feet in a fully reactive manner, using only on-board computers and sensors. The correction is computed and executed continuously along the swing phase trajectory of each leg. To efficiently adapt the landing position, we implement a self-supervised foothold classifier based on a convolutional neural network. Our method results in an up to 200 times faster computation with respect to the full-blown heuristics. Our goal is to react to visual stimuli from the environment, bridging the gap between blind reactive locomotion and purely vision-based planning strategies. We assess the performance of our method on the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe foothold adaptation is clearly demonstrated by the overall robot behavior

Legged Robots

International audienc

Legged Robots

International audienc

Stiffness control of robot manipulators in the operational space using fuzzy mapping of dynamic functions

In this paper a stiffness control strategy based on the fuzzy mapped nonlinear terms of the robot manipulator dynamic model is proposed. The proposed stiffness controller is evaluated on a research robot manipulator performing a task in the operational space. Tests attempted to achieve fast motion with reasonable accuracy associated with lower computational load compared to the non-fuzzy approach. The stability analysis is presented to conclude about the mapping error influence and to obtain precondition criteria for the gains adjustment to face the trajectory tracking problem. Simulation results that supported the implementation are presented, followed by experiments and results obtained. These tests are conducted on a robot manipulator with SCARA configuration to illustrate the feasibility of this strategy