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.

Identification of the wheel sinkage of exploration rovers provides valuable insight into the characteristics of deformable soils and thus the ease of traversal is also identified. In this paper we propose a simple vision based approach that robustly detects and measures the sinkage of any shaped wheel in real-time and with little sensitivity to various operating conditions. The method is based on color-space segmentation to identify the wheel contour and consequently the depth of the sinkage. In addition, our approach also provides a dynamic sinkage analysis which potentially allows for the identification of non-geometric hazards. The robustness of the algorithm has been validated for poor lighting, blurring, and background noise. The experimental results presented are for a hybrid legged wheel from our in-house single-wheel test-bed.