Owing to different gravities, the same manned lunar rover shows different acceleration properties on the Earth and the Moon; therefore, it is of great significance for astronauts to be trained on the Earth under the condition of the acceleration properties found on the Moon. In this study, a novel acceleration imitation method focused on a single wheel is proposed to obtain equivalent acceleration when using the same acceleration driver input under different gravities, and the acceleration imitation algorithm is derived based on a mechanical model of a special metal wheel with a mesh surface. To reduce the complexity and improve the real-time properties of the algorithm, a fitting function for the imitation torque ratio is proposed to adjust the relationship between the acceleration driver input and the driving torque of the wheel. Finally, experiments were conducted to verify the acceleration imitation algorithm, and the results revealed that the wheel with an imitation torque ratio could effectively approximate on the Earth the same acceleration experienced by a single wheel of a manned lunar rover on the Moon, given equivalent acceleration driver inputs.

Physical compliance has been increasingly used in robotic legs, due to its advantages in terms of the mechanical regulation of leg mechanics and energetics and the passive response to abrupt external disturbances during locomotion. This article presents a review of the exploitation of physical compliance in robotic legs. Particular attention has been paid to the segmented, electrically actuated robotic legs, such that a comparable analysis can be provided. The utilization of physical compliance is divided into three main categories, depending on the setting locations and configurations, namely, (1) joint series compliance, (2) joint parallel compliance, and (3) leg distal compliance. With an overview of the representative work related to each category, the corresponding working principles and implementation processes of various physical compliances are explained. After that, we analyze in detail some of the structural characteristics and performance influences of the existing designs, including the realization method, compliance profile, damping design, and quantitative changes in terms of mechanics and energetics. In parallel, the design challenges and possible future works associated with physical compliance in robotic legs are also identified and proposed. This article is expected to provide useful paradigmatic implementations and design guidance for physical compliance for researchers in the construction of novel physically compliant robotic legs.

Soft manipulators can perform continuous operations due to their inherent compliance and dexterity, thus enabling safe interactions and smooth movements in confined environments. However, high compliance usually means low load capacity. It is important for a soft manipulator to possess proper flexibility while maintaining an acceptable stiffness to widen its applications. This paper has hence devoted efforts to a kind of variable stiffness mechanism for a soft manipulator actuated by pneumatic artificial muscles (PAMs). Due to the combination of contractile and extensor PAMs, the manipulator is able to vary its stiffness independently from the configuration. The stiffness characteristics of the soft manipulator are quantitatively analyzed by bending shape prediction under different loading and inflation conditions, and the prediction is built upon a nonlinear statics model coupled with PAM nonlinearity and the Cosserat theory. In addition, experimental measurements are conducted to further validate the expected performance of the manipulator design. The experimental and verified theoretical analysis results indicate that the manipulator shape and stiffness are greatly affected by the pressure variation of PAMs, realizing a large bending space with a high output force. The variable stiffness design obviously increases the manipulator’s ability to resist additional interference at the same position.

Whole-body motion planning is a key ability for legged robots, which allows for the generation of terrain adaptive behaviors and thereby improved mobility in complex environment. To this end, this paper addresses the issue of terrain geometry based whole-body motion planning for a six-legged robot over a rugged terrain. The whole-body planning is decomposed into two sub-tasks: leg support and swing. For leg support planning, the target pose of the robot torso in a walking step is first found by maximizing the stability margin at the moment of support-swing transition and matching the orientation of the support polygon formed by target footholds. Then, the torso and thereby the leg support trajectories are generated using cubic spline interpolation and transferred into joint space through inverse kinematics. In terms of leg swing planning, the trajectories in a walking step are generated by solving an optimal problem that satisfies three constraints and a bioinspired objective function. The proposed whole-body motion planning strategies are implemented with a simulation and a real-world six-legged robot, and the results show that stable and collision-free motions can be produced for the robot over rugged terrains.

Impedance control is widely used in manipulation tasks of robots for the purpose of modulating the contact force between the robot and the interaction environment. Currently, it is achievable to adapt the impedance control parameters to various interactive objects via optimization or learning approaches. However, the adapt efficiency is still one of the most important unsolved challenges. In this regard, this paper presents a Bayesian optimization-based efficient tuning strategy for the impedance controller. The interaction process from impedance control parameters to the produced contact force is modeled as a Gaussian Process and the expected improvement plus (EIP) acquisition function is employed as a sampling criterion to tune the impedance control parameters. For interacting with objects, the efficiency and performance of the proposed method are numerically verified and compared with two classical adaptive impedance control schemes. With a real-world robotic manipulator, it is demonstrated that thanks to the inherent data efficiency of the Bayesian optimization, our method is able to find suitable impedance parameters via as few as a dozen of interaction attempts. Finally, the robustness of the proposed method against prior data is discussed.

In low-illumination environments, visible light images significantly degrade, and the simultaneous localization and mapping (SLAM) system based on visible cameras fails. In contrast, thermal sensors are not affected by scene lighting conditions and have become a promising alternative. However, due to the problems of low contrast, high noise, and data interruption in thermal images, there are still many challenges in implementing thermal sensor SLAM. Therefore, this paper proposes an edge-aided monocular thermal sensor SLAM system, called EAMT-SLAM, suitable for low illumination environments. Firstly, the edge provides more feature associations to assist the SLAM system in initializing quickly and accurately. Subsequently, the saliency map is calculated based on the strong edge in the thermal image, and the weights are reasonably assigned to the feature points in the optimization process of pose estimation to obtain more accurate results. Finally, this paper proposes an edge-guided loop detection method, which achieves higher accuracy and real-time performance compared with the appearance-based method, effectively ensuring the global consistency of SLAM. In addition, this paper proposes a new thermal image processing method that can enhance image details and remove complex noise in real time, significantly improve the quality of thermal images, and broaden the application scope of the EAMT-SLAM system. The experimental results show that our method can obtain the highest quality thermal image under quantitative and qualitative evaluation, complete accurate loop detection, and finally realize accurate pose estimation in low-illumination environments.