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

Trajectory and Foothold Optimization using Low-Dimensional Models for Rough Terrain Locomotion

We present a trajectory optimization framework for legged locomotion on rough terrain. We jointly optimize the center of mass motion and the foothold locations, while considering terrain conditions. We use a terrain costmap to quantify the desirability of a foothold location. We increase the gait's adaptability to the terrain by optimizing the step phase duration and modulating the trunk attitude, resulting in motions with guaranteed stability. We show that the combination of parametric models, stochastic-based exploration and receding horizon planning allows us to handle the many local minima associated with different terrain conditions and walking patterns. This combination delivers robust motion plans without the need for warm-starting. Moreover, we use soft-constraints to allow for increased flexibility when searching in the cost landscape of our problem. We showcase the performance of our trajectory optimization framework on multiple terrain conditions and validate our method in realistic simulation scenarios and experimental trials on a hydraulic, torque controlled quadruped robot

Correlation of p16INK4A Expression and HPV Copy Number with Cellular FTIR Spectroscopic Signatures of Cervical Cancer Cells

Cervical cancer, a potentially preventable disease, has its main aetiology in infection by high risk human papillomavirus (HR-HPV). Approaches to improving cervical cancer screening and diagnostic methodologies include molecular biological analysis, targeting of biomarker proteins, but also exploration and implementation of new techniques such as vibrational spectroscopy. This study correlates the biomarker protein p16INK4A expression levels dependent on HPV copy number with the infrared absorption spectral signatures of the cervical cancer cell lines, HPV negative C33A, HPV-16 positive SiHa and CaSki and HPV-18 positive HeLa. Confocal fluorescence microscopy demonstrated that p16INK4A is expressed in all investigated cell lines in both nuclear and cytoplasmic regions, although predominantly in the cytoplasm. Flow cytometry was used to quantify the p16INK4A expression levels and demonstrated a correlation, albeit nonlinear, between the reported number of integrated HPV copies and p16INK4A expression levels. CaSki cells were found to have the highest level of expression, HeLa intermediate levels, and SiHa and C33A the lowest levels. FTIR spectra revealed differences in nucleic acid, lipid and protein signatures between the cell lines with varying HPV copy number. Peak intensities exhibited increasing tendency in nucleic acid levels and decreasing tendency in lipid levels with increasing HPV copy number, and although they were found to be nonlinearly correlated with the HPV copy number, their dependence on p16INK4A levels was found to be close to linear. Principal Component Analysis (PCA) of the Infrared absorption spectra revealed differences between nuclear and cytoplasmic spectroscopic signatures for all cell lines, and furthermore clearly differentiated the groups of spectra representing each cell line. Finally, Partial Least Squares (PLS) analysis was employed to construct a model which can predict the p16INK4A expression level based on a spectral fingerprint of a cell line, demonstrating the diagnostic potential of spectroscopic techniques

Pathological chemotherapy response score is prognostic in tubo-ovarian high-grade serous carcinoma: A systematic review and meta-analysis of individual patient data

There is a need to develop and validate biomarkers for treatment response and survival in tubo-ovarian high-grade serous carcinoma (HGSC). The chemotherapy response score (CRS) stratifies patients into complete/near-complete (CRS3), partial (CRS2), and no/minimal (CRS1) response after neoadjuvant chemotherapy (NACT). Our aim was to review current evidence to determine whether the CRS is prognostic in women with tubo-ovarian HGSC treated with NACT.This article is freely available via Open Access. Click on the Publisher URL to access the full-text via the publisher's site