Biped wall-climbing robots well adapt for carrying out tasks in large-scale 3D environments such as skeleton-facade structures, due to their good abilities to transit between walls and surmount obstacles. However, to plan 3D collision-free climbing paths in such environments is challenging and significant for biped wall-climbing robots. In this paper, we propose a novel hierarchical framework for planning 3D biped climbing navigation, including three levels, namely the global route, the foothold, and the single-step motion. We also present three effective planners incorporated in the framework to solve this challenging planning problem efficiently. A global route planner is designed on top, for rapidly analyzing the possibility for the robot to transit between walls, and searching walls that must be passed through and key footholds for performing transition, in order to optimize the route length globally. A local foothold planner is implemented in the middle, to search and optimize footholds on each via wall, considering the robot's ability to step over, step on, and bypass obstacles. A single-step motion planner is deployed at the bottom, for planning collision-free movement swinging the robot suction module from a foothold to another. Simulations are conducted to fully verify the effectiveness and performance of the presented planners. Experiments with W-Climbot are carried out to demonstrate that the proposed framework and planners succeed in planning 3D collision-free optimized climbing paths for biped wall-climbing robots.

Depth estimation is essential for many human-object interaction tasks. Despite its advantages, traditional depth sensors, including Kinect or depth camera, are always not wearable-friendly due to several critical drawbacks, such as over-size or over-weight. Monocular camera, on the other hand, provides a promising solution with limited burden to users and attracts more and more attentions in the literature. In this paper, we propose a depth estimation method with monocular camera. Our main idea lies in the weak-supervised learning model of monocular depth estimation based on left and right consistency. To learn an accurate depth estimation, on our training step, we employ LiDAR data, which are generated by laser radar with very high depth accuracy, to semi-supervise the learning scheme. We train our network on ResNet and propose a new penalty function, which takes into account the LiDAR depth loss in training. Compared with several state-of-the-art monocular camera depth estimators, our proposed method obtains the highest depth accuracy.

Power grasps and fine manipulations with a robotic hand usually require dexterity in the fingers and a rich tactile function. In this article, we propose a novel soft hand with a tactile sensing function and investigate how tactile information can be used to perform object classification and fine grasping adjustments. We first developed a pneumatically driven soft hand with an inflatable palm and bendable fingers, where a TacTip-type sensor and several Flex sensors were integrated. The inflatable palm can not only significantly improve grasping but also object sensing. Based on the perspective projection model of a camera, a 3-D reconstruction algorithm for the deformed surface of a soft palm is presented. A spatial characterization of geometric information regarding objects based on sensing information from the palm and fingers was deduced and analyzed. A classifier was designed for object classification. Finally, a control method for the fine adjustment of grasps based on tactile sensing information was presented and verified using a series of grasps of different objects with a soft hand. The object classification and contact state sensed by the soft tactile sensors can be used for grasp planning and manipulation control with the soft hand.

Although the LiDAR SLAM technique has been already widely deployed on various robots, it may still suffers from degeneracy caused by inadequate constraints in scenes with sparse geometric features. If the degeneracy is not detected and properly processed, the accuracy of localization and mapping will significantly decrease. In this letter, we propose the P2d-DO method, which consists of a point-to-distribution degeneracy detection algorithm and a point cloud-weighted degeneracy optimization algorithm, to relieve the negative impact of degeneracy. The degeneracy detection algorithm outputs factors that characterize the degeneracy state by observing changes in the distribution probabilities within a local region. Factors reflecting the confidence of the point clouds are then fed to the degeneracy optimization algorithm, enabling the system to prioritize reliable point clouds by assigning larger weights during the matching process. Comprehensive experiments validate the effectiveness of our method, demonstrating significant improvements in both degeneracy detection and pose estimation in terms of accuracy and robustness.

A variety of soft robots with prospective applications has been developed in recent years. As a key component of a soft robot, the soft actuator plays a critical role and hence must be designed carefully according to application requirements. The soft body may deform in undesired directions if no restraint is endued, due to the isotropy of the pure soft material. For some soft robots such as an inchworm-like biped climbing robot, the actuation direction must be constrained with the appropriate structure design of the soft actuator. This study proposes a pneumatic soft actuator (PSA) to achieve pure in-plane bending motion with anisotropic soft and rigid restraints. The in-plane bending pneumatic soft actuator (2D-PSA) is developed with a composite structure where a metal hinge belt is embedded into the soft material. The design method, material choice, and fabrication process are presented in detail in this paper. Tests are conducted to measure the actuating performance of 2D-PSA in terms of the relationship between the bending angle or force and the input air pressure. Dynamic response is also measured with a laser tracker. Furthermore, a comparative experiment is carried out between the presented 2D-PSA and a general PSA, with results verifying the effectiveness of the presented 2D-PSA. A robot consisting of two serially-connected 2D-PSAs and three pneumatic suckers, which can climb on a flat surface mimicking a snake’s locomotion, is developed as an application demo of the presented 2D-PSA. Its locomotion capability presents the in-plane performance and mobility of 2D-PSA.