Dynamic capture is one of the most challenging issues to be solved in the field of robotics. Although robotic hands/grippers have emerged for decades, there are still few of them that can dynamically capture moving targets because the process is strongly accompanied by impact force and uncertainties in relative gripper/target locations and velocities. In this article, we present the novel design of a multitentacle gripper inspired by the motions of sea anemones. It is found that each tentacle of the sea anemone is not individually able to capture a fish but the collaboration of a larger number of tentacles can greatly enhance the overall capture ability. Based on this concept, 12 continuum arms including active and passive types are proposed and evaluated for their deformation to external forces. In addition, a deployable base, inspired by origami and Sarrus mechanisms, is utilized to change the posture of the continuum arms and drive the active arms. An inertial measurement unit is equipped to sense the impact of the dynamic targets. Finally, a series of experiments proved that the proposed multitentacle gripper could capture dynamic targets with different shapes, velocities, and collision angles, showing satisfactory capture robustness.

The present paper proposes user gesture recognition to control industrial robots. To recognize hand gestures, MediaPipe software package and an RGB camera is used. The proposed communication approach is an easy and reliable approach to provide commands for industrial robots. The landmarks which are extracted by MediaPipe software package are used as the input to a gesture recognition software to detect hand gestures. Five different hand gestures are recognized by the proposed machine learning approach in this paper. Hand gestures are then translated to movement directions for the industrial robot. The corresponding joint angle updates are generated using damped least squares inverse kinematic approach to move the industrial robot in a plane. The motion behaviour of the industrial robot is simulated within V-REP simulation environment. It is observed that the hand gestures are communicated with high accuracy to the industrial robot and the industrial robot follows the movements accurately.

Precision object handling and manipulation require precise positioning of industrial robots. The common practice to perform end effector positioning is to use joint angle readings and industrial robot forward kinematics. However, forward kinematics of the robot rely on its DH parameter values which include uncertainties. Sources of uncertainty associated with robot forward kinematics include mechanical wear, manufacturing, assembly tolerances, and robot dimensional measurement errors. It is therefore highly required to increase the precision of DH parameter values. In this paper, we use heuristic optimization approaches to perform industrial robot DH parameter calibration. To perform this task, a highly accurate laser tracker system, Leica AT960-MR is utilized. The precision of this non-contact metrology device is less than 3μm/m and therefore it can be easily used to calibrate industrial robots with precision of 0.1mm. Heuristic optimization approaches such as genetic algorithm, particle swarm optimization, and differential evolution are used to perform the calibration using laser tracker position data as their target values. It is observed that using the proposed approach, the accuracy of industrial robot FKs in terms of mean absolute errors of static and near-static motion over all three position dimensions decreases from its measured value 66.5μm to 51.6μm (more than 22.4% improvement).

In this paper, an XOR binary gravitational search algorithm is introduced. Gravitational search algorithms, a physics inspired optimization algorithm, have previously been successfully applied to different real-valued optimization problems. In their binary version, the definition of a velocity vector changes to probability of change in corresponding dimensions. However, analysis shows that existing velocity vector update equation for binary gravitational search algorithm do not direct the particle towards better particle in certain cases. To apply this algorithm to binary optimization problems we introduce an XOR operator in the acceleration term. After a mathematical comparison to existing binary gravitational search algorithms it is shown that the proposed modification complies more with the definition of change in each dimension. Extensive simulations are performed showing the superiority of the proposed algorithm over other existing algorithms such as binary particle swarm optimization and an existing version of binary gravitational search algorithm.

Precise actuation of continuum surfaces in combination with continuum robotic arms that undergo large deformation is of high interest in soft robotics but of limited model-based study to date. This work develops this area towards enabling the robust design and control of large deformation continuum surfaces (LDCS) across multiple industrial applications in the healthcare, aerospace, manufacturing, and automotive domains. It introduces an actuation based dynamic model of LDCSs to accurately determine their deflection due to application of concentrated external forces while maintaining many physical characteristics and constraints on actuation elements and surface structure such as gravity, inertia, damping, elasticity, and interactive forces between actuators and LDCS. Using the lumped-mass methodology, a 3D integrated surface-arm model is developed, simulated and then validated experimentally where a pair of parallel arms are attached to the surface to actuate and deform it. The surface is then simultaneously subjected to a concentrated constant external force at its top center between the two arms. Comparing measured displacements between the experimental and modelling results over actuation time yielded the maximum error is less than 1% of the length of the surface's side at its final deflected profile despite the limited number of nodes (masses) used in the LDCS model while it is exposed to a significant external force.

The Freefall Camera (FFC) is a robot designed to film skydivers in free fall and is, to the author's knowledge, the first robot to control its position and terminal velocity relative to another falling object. The robot is equipped with a vision sensor to track objects of a chosen colour. The control surfaces for free fall motion are four vertical ailerons that control planar position and orientation and two retractable flaps that control terminal velocity. To develop a robust controller, equations of motion were created and validated experimentally for movement in the vertical and planar rotational directions. The static and dynamic response of the system were then tested in a vertical wind tunnel to validate the models and use them to design and tune a PID + Filter controller for the free fall flight of the robot. Planar rotation tests showed a decrease in steady state angular velocity and dynamic response gain compared to the model and a slight increase in dynamic response phase shift. This was attributed to the effect of the turbulent air near the shell of the robot. Vertical translation tests showed a decrease in maximum terminal velocity compared to the model, likely due to the effect of air viscosity at higher Reynolds numbers. The models were used to tune a PID + Filter controller to achieve stable target tracking in the rotational and vertical degrees of freedom using image based visual servoing. Controlled flight of the FFC was then tested in a vertical wind tunnel, where the robot was able to successfully track a coloured target within 15° of the centre of the field of view.

This paper presents an Octopus inspired walking robot with pneumatic muscle actuator (PMA) driven continuum arms. Each arm is made up of 4 longitudinally arranged PMAs, consistent with octopus arm anatomy. We first present the design and construction of a single continuum arm followed by its modeling and experimental validation. The design of the walking robot is then presented followed by details of extended dynamic model describing the full walking robot with four arms. Basic control architecture is introduced for the robot to achieve walking motion and experimental results analyzed. Initial results show good agreement between the experimental results and simulation results.

This paper presents the application of a hydraulic DC-DC converter, namely a step down Buck Converter to the actuation of a robot leg that is part of the quadruped robot HyQ. The use of a Hydraulic Buck Converter (HBC) offers significant advantages in terms of improved efficiency of hydraulic actuation systems analogously to an electric switching DC-DC converter as opposed to a rheostatic-type system. In this paper, a HBC consisting of two digital valves and two check valves is introduced to improve the efficiency performance of a singl leg of a hydraulic quadruped robot (HyQ). This type of hydraulic buck converter is able to support the locomotion in two directions. The HBC operates at a switching frequency of 100 Hz in pulse-width-modulation. The better energy performance compared to proportional control is achieved by the use of fast check valves. The performance of the system with a 3-way-4-position proportional valve is compared with the HBC drive. A test rig is set up to investigate the performance of HBC with two different controllers and a Hydraulic Proportional Drive (HPD) system, based on proportional valves which control flow, by throttling it, in a dissipative manner. The performance of position tracking and energy consumption is evaluated. The experimental results indicate that HBC systems can achieve similar position tracking with relatively less consumed energy.

Conventional rigid body robots typically use few degrees of freedom (DOF). This results in a manipulator that lacks flexibility and maneuverability when compared to continuum robots that utilize a much higher number of DOF. However, due to their continuous nature, the difficulty of measuring and controlling a large number of actuated DOF, and their high degree of nonlinearity, the development of control algorithms for continuum robot manipulators is an ongoing challenge. This paper presents an algorithm inspired by biological solutions from live octopus that utilizes division of functionality to achieve simple and robust control of continuum arm based systems. Simulated results for single and multiple dynamic continuum arms show the controller is capable of producing motions similar to that of octopus. The resulting controller is also computationally efficient enough for real-time implementation. In future this work will be implemented on a prototype robot with multiple continuum arms.

This paper presents an improved mode shape function-based 3D dynamic model for pneumatic muscle actuated continuum arms, and validates the model and simulation results through experimental testing. The model also facilitates the direct control of pneumatic muscle actuated continuum arms through the use of input pressure. This is achieved without additional intermediary transformations and does not have singularity problems present in previous models. The proposed arm model uses a new pneumatic muscle actuator (PMA) dynamic model with hysteresis that is capable of modelling both extending and contracting PMAs. The proposed hysteric model is simple, easily adaptable, and validated experimentally. The PMA model can be applied to dynamically model any PMA based system as well as PMA actuated continuum arms utilizing different actuator configurations.

This paper examines the design and control of a robotic arm inspired by the anatomy and neurophysiology of Octopus vulgaris in light of embodiment theory. Embodiment in an animal is defined as the dynamic coupling between sensorymotor control, anatomy, materials, and the environment that allows for the animal to achieve effective behaviour. Octopuses in particular are highly embodied and dexterous animals: their arms are fully flexible, can bend in any direction, grasp objects and modulate stiffness along their length. In this paper the biomechanics and neurophysiology of octopus have been analysed to extract relevant information for use in the design and control of an embodied soft robotic arm. The embodied design requirements are firstly defined, and how the biology of the octopus meets these requirements presented. Next, a prototype continuum arm and control architecture based on octopus biology, and meeting the design criteria, are presented. Finally, experimental results are presented to show how the developed prototype arm is able to reproduce motions performed by live octopus for contraction, elongation, bending, and grasping.

Continuum structures with a very high or infinite number of degrees of freedom (DOF) are very interesting structures in nature. Mimicking this kind of structures artificially is challenging due to the high number of required DOF. This paper presents a kinematic and dynamic model for an underwater robotic manipulator inspired by Octopus vulgaris. Then, a prototype arm inspired by live octopus is presented and the model validated experimentally. Initial comparisons of simulated and experimental results show good agreement.

Soft robots are difficult to control because of their compliant and elastic body dynamics compared with robots made of rigid bodies. In this paper, we present a control scheme inspired by the octopus called timing-based control for soft robotic arms. This control scheme is motivated to positively exploit the natural dynamics of the soft body. We demonstrate a scheme for controlling an object-reaching task by using an echo state network on a 3D physical soft robotic arm simulator and show that this network can successfully perform the task. Detailed analyses and evaluations of the generalization capacity of the network and the performances to the reaching task are presented.

This paper presents a control algorithm to achieve crawling locomotion for a multi-arm robotic system inspired by live octopuses. First the paper introduces a dynamic model of a continuum arm. The model accounts for the key features relevant to crawling locomotion, namely longitudinal muscles and suckers that provide force interaction with the surrounding environment. This single arm model is then validated against live octopus data and expanded to an 8-arm system. Appropriate coordination algorithms of the eight arms result in crawling locomotion. The results of this work can be used to study the motor control schemes for both multiple continuum arm robots and live octopuses.

This paper presents a new three dimensional kinematic and dynamic model for variable length continuum arm robotic structures using a novel shape function-based approach. The model incorporates geometrically constrained structure of the arm to derive its deformation shape function. It is able to simulate spatial bending, pure elongation, and incorporates a new stiffness control feature. The model is validated through numerical simulations, based on a prototype continuum arm, that yields physically accurate results.

Continuum robotic arms are based on non-rigid components that result in a nearly infinite number of degrees of freedom (DOF). Due to this reason it can be very complex to establish mathematical models for continuum robotic arms. This paper presents a 3D dynamic model of an arm based on octopus anatomy that utilizes 4 longitudinal and 4 radial muscles. The arm is composed of a multi-segment structure having distributed stiffness and damping to represent the muscles. The simulations are applied to a multi-segment arm, and results mimic several typical octopus arm motions.

This paper presents a new three dimensional (3D) kinematic model based on mode shape functions (MSF) for multisection continuum arms. It solves the singularity problems associated with previous models and introduces a novel approach for intuitively deriving exact, singularity-free MSFs, thus avoiding mode switching schemes and simplifying error models. The model is able to simulate spatial bending, pure elongation/contraction, and introduces inverse orientation kinematics for the first time to multisection continuum arms. Also, it carefully accounts for physical constraints in the joint space to provide enhanced insight into practical mechanics, and produces correct results for both forward and inverse kinematics. The model is validated through simulations, based on a prototype continuum robotic arm. Proposed approach is applicable to a broad spectrum of continuum robotic arm designs.

The octopus arm is a unique tool that combines strength and flexibility. It can shorten, elongate and bend at any point along its length. To model this behavior, a hyper-redundant manipulator composed of multiple segments is proposed. Each segment is a parallel robotic mechanism with redundant actuation. The kinematics and dynamics of this manipulator are analyzed and simulated utilizing a modular computational modeling method. Simulation results for some primitive movements are presented, and the effect of hydrodynamic forces is included.