A space robot working in a controlled-floating mode can be used for performing in-orbit telescope assembly through simultaneously controlling the motion of the spacecraft base and its robotic arm. Handling and assembling optical mirrors requires the space robot to achieve slow and precise manoeuvres regardless of the disturbances and errors in the trajectory. The robustness offered by the nonlinear H∞controller, in the presence of environmental disturbances and parametric uncertainties, makes it a viable solution. However, using fixed tuning parameters for this controller does not always result in the desired performance as the arm's trajectory is not known a priori for orbital assembly missions. In this paper, a complete study on the impact of the different tuning parameters is performed and a new adaptive H∞controller is developed based on bounded functions. The simulation results presented show that the proposed adaptive H∞controller guarantees robustness and precise tracking using a minimal amount of forces and torques for assembly operations using a small space robot.

The first demonstration of a pneumatic soft continuum robot is integrated in series with a rigid robot arm, safely performing teleoperated diathermic tissue-cutting. The rigid arm autonomously maintains a safe tool contact force, while the soft arm manually follows the desired cutting path. Ex-vivo experimentation demonstrates submillimetric deviations from target paths.

Successful operation of off-road mobile robots faces the challenge of mobility hazards posed by soft, deformable terrain, e.g., sand traps. The slip caused by these hazards has a significant impact on tractive efficiency, leading to complete immobilization in extreme circumstances. This paper addresses the interaction between dry frictional soil and the multilegged wheel-leg concept, with the aim of exploiting its enhanced mobility for safe, in situ terrain sensing. The influence of multiple legs and different foot designs on wheel-leg-soil interaction is analyzed by incorporating these aspects to an existing terradynamics model. In addition, new theoretical models are proposed and experimentally validated to relate wheel-leg slip to both motor torque and stick-slip vibrations. These models, which are capable of estimating wheel-leg slip from purely proprioceptive sensors, are then applied in combination with detected wheel-leg sinkage to successfully characterize the load bearing and shear strength properties of different types of deformable soil. The main contribution of this paper enables nongeometric hazard detection based on detected wheel-leg slip and sinkage.

In off-road applications, where mobile robots operate on rough environments, the physical properties of the terrain play a key role on their performance. An extreme example is posed by planetary rover missions to Mars, for which communication constraints and the inability of vision-based approaches to detect non-geometric hazards, e.g. sand traps hidden below thin duricrusts, can lead to permanent immobilisation, as experienced by NASA's Spirit rover. To prevent such events, this paper proposes a method to classify dry granular soils according to their physical properties by using an on-board sensor system for on-line analysis of sinkage, slippage and vibrations of the hybrid wheel-legs mounted on a highly mobile robot. As reflected by the experimental results, obtained using a single wheel-leg test bed, the novel approach produces an efficient and robust differentiation of soils with dissimilar physical properties. This output can enable autonomous avoidance of non-geometric hazards without endangering the mobility of the mission. Different classifier algorithms are trained, validated and compared in terms of classification accuracy and computational efficiency, revealing the advantages and disadvantages of each approach.

The class of Pneumatically-driven Lower-pressure Soft Actuators (PLSA) is a popular research topic as it can be potentially used in the surgical robotic applications. One fundamental problem lying in the PLSA research is the lack of a generally validated model for the complex nonlinear dynamic behaviours. In this paper, a new coefficient-adaptive orthonormal basis function model structure is specifically developed for the identification of the general PLSAs. It is a parameter-independent way directly used to identify the dynamic relation between the actuating pressures and the principal degrees of freedom of a PLSA, the bending and the steering. The approach is based on a modified auxiliary kinematic setting. Following the discussion of the identification procedure, the implementations for the double chamber bending and steering are demonstrated. The results show that the proposed approach can accurately capture the nonlinear pressure-shape dynamics. The approach is also efficient in the real-time applications. It can be further used to improve the current control design for the PLSAs in robotic applications.

Gain tuning is a crucial part of controller design and depends not only on an accurate understanding of the system in question, but also on the designer's ability to predict what disturbances and other perturbations the system will encounter throughout its operation. This letter presents ANUBIS (artificial neuromodulation using a Bayesian inference system), a novel biologically inspired technique for automatically tuning controller parameters in real time. ANUBIS is based on the Bayesian brain concept and modifies it by incorporating a model of the neuromodulatory system comprising four artificial neuromodulators. It has been applied to the controller of EchinoBot, a prototype walking rover for Martian exploration. ANUBIS has been implemented at three levels of the controller; gait generation, foot trajectory planning using Bézier curves, and foot trajectory tracking using a terminal sliding mode controller. We compare the results to a similar system that has been tuned using a multilayer perceptron. The use of Bayesian inference means that the system retains mathematical interpretability, unlike other intelligent tuning techniques, which use neural networks, fuzzy logic, or evolutionary algorithms. The simulation results show that ANUBIS provides significant improvements in efficiency and adaptability of the three controller components; it allows the robot to react to obstacles and uncertainties faster than the system tuned with the MLP, while maintaining stability and accuracy. As well as advancing rover autonomy, ANUBIS could also be applied to other situations where operating conditions are likely to change or cannot be accurately modeled in advance, such as process control. In addition, it demonstrates one way in which neuromodulation could fit into the Bayesian brain framework.