Despite their soft and compliant bodies, most of today’s soft robots have limitations when it comes to elongation or extension of their main structure. In contrast to this, a new type of soft robot called the eversion robot can grow longitudinally, exploiting the principle of eversion. Eversion robots can squeeze through narrow openings, giving the possibility to access places that are inaccessible by conventional robots. The main drawback of these types of robots is their limited bending capability due to the tendency to move along a straight line. In this paper, we propose a novel way to fuse bending actuation with the robot’s structure. We devise an eversion robot whose body forms both the central chamber that acts as the backbone as well as the actuators that cause bending and manoeuvre the manipulator. The proposed technique shows a significantly improved bending capability compared to externally attaching actuators to an eversion robot showing a 133% improvement in bending angle. Due to the increased manoeuvrability, the proposed solution is a step towards the employment of eversion robots in remote and difficult-to-access environments.

In the last decade, soft robots have been at the forefront of a robotic revolution. Due to the flexibility of the soft materials employed, soft robots are equipped with a capability to execute new tasks in new application areas -beyond what can be achieved using classical rigid-link robots. Despite these promising properties, many soft robots nowadays lack the capability to exert sufficient force to perform various real-life tasks. This has led to the development of stiffness-controllable inflatable robots instilled with the ability to modify their stiffness during motion. This new capability, however, poses an even greater challenge for robot control. In this paper, we propose a model-based kinematic control strategy to guide the tip of an inflatable robot arm in its environment. The bending of the robot is modelled using an Euler-Bernoulli beam theory which takes into account the variation of the robot's structural stiffness. The parameters of the model are estimated online using an observer based on the Extended Kalman Filter (EKF). The parameters' estimates are used to approximate the Jacobian matrix online and used to control the robot's tip considering also variations in the robot's stiffness. Simulation results and experiments using a fabric-based planar 3-degree-of-freedom (DOF) inflatable manipulators demonstrate the promising performance of the proposed control algorithm.

Thin and imperceptible soft skins that can detect internal deformations as well as external forces, can go a long way to address perception and control challenges in soft robots. However, decoupling proprioceptive and exteroceptive stimuli is a challenging task. In this paper, we present a silicone-based, capacitive E-skin for exteroception and proprioception (SCEEP). This soft and stretchable sensor can perceive stretch as along with touch at 100 different points via its 100 tactels. In this paper, we present a novel algorithm that decouples global strain from local indentations due to external forces. The soft skin is 10.1cm in length and 10cm in width and can be used to accurately measure the global strain of up to 25% with an error of under 3%; while at the same time, can determine the amplitude and position of local indentations. This is a step towards a fully soft electronic skin that can act as a proprioceptive sensor to measure internal states while measuring external forces.

In this paper, a magnetic-field-inspired robot navigation is used to navigate an under-actuated quad-copter towards the desired position amidst previously-unknown arbitrary-shaped convex obstacles. Taking inspiration from the phenomena of magnetic field interaction with charged particles observed in nature, the algorithm outperforms previous reactive navigation algorithms for flying robots found in the literature as it is able to reactively generate motion commands relying only on a local sensory information without prior knowledge of the obstacles' shape or location and without getting trapped in local minima configurations. The application of the algorithm in a dynamic model of quadcopter system and in the realistic model of the commercial AscTec Pelican micro-aerial vehicle confirm the superior performance of the algorithm.

This paper describes a novel approach in human-robot interaction driven by ergonomics. With a clear focus on optimising ergonomics, the approach proposed here continuously observes a human user’s posture and by invoking appropriate cooperative robot movements, the user’s posture is, whenever required, brought back to an ergonomic optimum. Effectively, the new protocol optimises the human-robot relative position and orientation as a function of human ergonomics. An RGB-D camera is used to calculate and monitor human joint angles in real-time and to determine the current ergonomics state. A total of 6 main causes of low ergonomic states are identified, leading to 6 universal robot responses to allow the human to return to an optimal ergonomics state. The algorithmic framework identifies these 6 causes and controls the cooperating robot to always adapt the environment (e.g. change the pose of the workpiece) in a way that is ergonomically most comfortable for the interacting user. Hence, human-robot interaction is continuously re-evaluated optimizing ergonomics states. The approach is validated through an experimental study, based on established ergonomic methods and their adaptation for real-time application. The study confirms improved ergonomics using the new approach.

In this paper, we present a reactive robot navigation method for a non-holonomic mobile robot taking inspiration from the phenomena observed in magnetic fields. The algorithm is shown to be able to guide mobile robots in arbitrary-shaped convex environment without being trapped in local minima by exploiting the local sensory information without priori knowledge about the environment. A preliminary validation study involving simulation of and experiments with a TurtleBot mobile robot platform show the advantage of the proposed method over existing ones.

Taking inspiration from the properties of magnetic fields, we propose a reactive navigation method for soft continuum manipulators operating in unknown environments. The proposed navigation method outperforms previous works since it is able to successfully achieve collision-free movements towards the goal in environments with convex obstacles without relying on a priori information of the obstacles' shapes and locations. Simulations for the kinematic model of a soft continuum manipulator and preliminary experiments with a 2-segments soft continuum arm are performed, showing promising results and the potential for our approach to be applied widely.

Soft robotic structures might play a major role in the 4thindustrial revolution. Researchers have successfully demonstrated advantages of soft robotics over traditional robots made of rigid links and joints in several application areas including manufacturing, healthcare and surgical interventions. However, soft robots have limited ability to exert higher forces when it comes to interaction with the environment, hence, change their stiffness on demand over a wide range. One stiffness mechanism embodies tendon-driven and pneumatic air actuation in an antagonistic way achieving variable stiffness values. In this paper, we apply a beam-mechanics-based model to this type of soft stiffness controllable robot. This mathematical model takes into account the various stiffness levels of the soft robotic manipulator as well as interaction forces with the environment at the tip of the manipulator. The analytical model is implemented into a robotic actuation system made of motorised linear rails with load cells (obtaining applied forces to the tendons) and a pressure regulator. Here, we present and analyse the performance and limitations of our model.

In this paper, a potential-field-based real-time path planning algorithm for a multi-segment continuum manipulator is proposed. This planner is employed to enable a continuum-style manipulator to move autonomously in dynamic environments in real-time. The classic potential field method is modified to make it applicable for a kinematics model based on the constant-curvature assumption. The contribution of this paper lies in the design of a novel potential field in the actuator space satisfying the mechanical constraints of the manipulator. The planning algorithm is tested and validated in real-time simulation for a 3 segments continuum manipulator. Preliminary tests for a tendon-driven single-segment continuum manipulator prototype confirm the performance of the proposed planner.

The complexity of soft continuum manipulators with hybrid and tuneable structures poses a challenging task to achieve an inverse kinematics model which is both precise and computationally efficient for control and optimization purposes. In this paper, a new method based on the principle of virtual work and a geometry deformation approach is presented for the inverse kinematics model of the STIFF-FLOP arm which is a pneumatically actuated continuum manipulator. We propose a novel simplified and computationally efficient yet accurate analytical solution to analyse the static behaviour of a compound soft manipulator in the presence of external and body forces which is verified against experimental data, showing promising agreement with 10% mean error for planar movements. In the process, we present a new modelling approach for braided soft extensor actuators with no braid-surface relative slip constraint. For the first time, our model predicts a simple analytical solution for the cross section deformation which is essential to control soft manipulators with regional tunable stiffness structure.

In this paper, we present a novel pose estimation and obstacle avoidance approach for tendon-driven multi-segment continuum manipulators moving in dynamic environments. A novel multi-stage implementation of an Extended Kalman Filter is used to estimate the pose of every point along the manipulator's body using only the position information of each segment tip. Combined with a potential field, the overall algorithm will guide the manipulator tip to a desired target location and, at the same time, keep the manipulator body safe from collisions with obstacles. The results show that the approach works well in a real-time simulation environment that contains moving obstacles in the vicinity of the manipulator.