At Imperial College, the author has invented a form of universal gripper comprising two slightly separated ‘fingers’, each consisting of an array of 8 by 16 closely spaced pins which can ride vertically up and down independently of each other. Lowering the gripper over an object pushes some pins out of the way, so creating ‘customised’ fingers which mould round and fit the part. To grasp objects, either the two (slightly separated) fingers can be brought together to grip an object externally, or else they can be moved slightly apart (from a ‘closed’ position) for an internal grip. In addition, feedback from each pin of the gripper can provide tactile information about an object.

In this paper, the dynamics and biomimetic control of an artificial finger joint actuated by two opposing one-way shape memory alloy (SMA) muscle wires that are configured in a double spring-biased agonist–antagonist fashion is presented. This actuation system, which was described in Part I, forms the basis for biomimetic tendon-driven flexion/extension and abduction/adduction of the artificial finger. The work presented in this paper centres on thermomechanical modelling of the SMA wire, including both major and minor hysteresis loops in the phase transformation model, and co-operative control strategy of the agonist–antagonist muscle pair using a pulse-width-modulated proportional-integral-derivation (PWM–PID) controller. Parametric analysis and identification are carried out based on both simulation and experimental results. The performance advantage of the proposed co-operative control is shown using the metacarpophalangeal joint of the artificial finger.

To the best of our knowledge, this is the first paper focus on the adaptive impedance control of robot manipulators with consideration of joint flexibility and actuator dynamics. Controller design for this problem is difficult because each joint of the robot has to be described by a fifth-order cascade differential equation. In this paper, a backstepping-like procedure incorporating the model reference adaptive control strategy is employed to construct the impedance controller. The function approximation technique is applied to estimate time-varying uncertainties in the system dynamics. The proposed control law is free from the calculation of the tedious regressor matrix, which is a significant simplification in implementation. Closed-loop stability and boundedness of internal signals are proved by the Lyapunov-like analysis with consideration of the function approximation error. Computer simulation results are presented to demonstrate the usefulness of the proposed scheme.

In this paper, tip tracking control of a single-link flexible robot is studied through computer simulations. The controller investigated is of a two-loop PD type, which is deduced from a control approach originally developed for elastic joint robots. A very simple model, with the flexible link of the robot being lumped to a spring-mass unit, is employed for the controller design. Bounded Input and Bounded Output (BIBO) stable tip tracking performance is guaranteed. The controller is robustly stable under system parameter uncertainties, and is comparatively easy to implement. In the simulations, different payloads are considered, and the system performance is compared with the traditional Singular Perturbation (SP) method. The simulation results show that the PD type controller designed based on the simple lumped model is able to provide quite satisfactory performance.

We investigated and identified the conditions necessary for stable dynamic gait generation in biped robots from the mechanical energy balance point of view. The equilibrium point at impact in a dynamic gait is uniquely determined by two conditions; keeping the restored mechanical energy constant and settling the relative hip-joint angle to the desired value before impact. The generated gait then becomes asymptotically stable around the equilibrium point determined by these conditions. This is shown by a simple recurrence formula of the kinetic energy immediately before impact. We verified this stability theorem using numerical simulation of virtual passive dynamic walking. The results were compared with those for a rimless wheel and an inherent stability principle was derived. Finally, we derived a robust control law using a reference mechanical energy trajectory and demonstrated its effectiveness numerically.

The design of the robotic mechanisms is most important because they determine the performance characteristics of the robots. This paper concerns the issue of computer-aided design (CAD) for planar 3-DOF parallel robotic mechanisms by means of the physical model of the solution space, which can be used systematically to express the relationships between the performance criteria and all link lengths of one type of the robtic mechanism. The performance atlases of the workspace volume for the manipulators are plotted in the physical model of the solution space. The characteristics of the distribution of the workspace shapes in the physical model of the solution space are presented. The results are useful for the optimum design of the robotic mechanisms. This paper proposes a new way for robotic CAD.

This paper presents an architecture for the navigation of an autonomous mobile robot evolving in environments with obstacles. Instead of addressing the motion planning and control problems in different contexts, these issues are described in connected modules with performance requirement considerations. The path planning problem is formulated as a constrained receding horizon planning problem and is solved in real time with an efficient computational method that combines non-linear control theory, B-spline basis function and non-linear programming. An integral sliding mode controller is used for trajectory tracking. The closed-loop stability of the tracking errors is guaranteed in spite of unknown disturbances. It is also shown that this strategy is particularly useful if integral sliding mode control is combined with other methods to further robustify against perturbations. The effectiveness, perfect performance of obstacle avoidance, real time and high robustness properties are demonstrated by experimental results.

This paper presents a new approach to matching occupancy grid maps by means of finding correspondences between a set of sparse features detected in the maps. The problem is stated here as a special instance of generic image registration. To cope with the uncertainty and ambiguity that arise from matching grid maps, we introduce a modified RANSAC algorithm which searches for a dynamic number of internally consistent subsets of feature pairings from which to compute hypotheses about the translation and rotation between the maps. By providing a (possibly multi-modal) probability distribution of the relative pose of the maps, our method can be seamlessly integrated into large-scale mapping frameworks for mobile robots. This paper provides a benchmarking of different detectors and descriptors, along extensive experimental results that illustrate the robustness of the algorithm with a 97% success ratio in loop-closure detection for ~1700 matchings between local maps obtained from four publicly available datasets.

The 3 degree-of-freedom Gantry-Tau manipulator with the addition of the spherical wrist mechanism which is called Gantry-Tau-3R is designed as a high-G simulation-based motion platform (SBMP) with the capability of generating the large linear and angular displacement. The combination of both parallel and serial manipulator in newly designed Gantry-Tau-3R mechanism improves the ability of the mechanism to regenerate larger motion signals with higher linear acceleration and angular velocity. The high-frequency signals are reproduced using the parallel part of the mechanism, and sustainable low-frequency accelerations are regenerated via the serial part due to the larger rotational motion capability, which will be used through motion cueing algorithm tilt coordination channel. The proportional integral derivative (PID) and fuzzy incremental controller (FIC) are developed for the proposed mechanism to show the high path tracking performance as a motion platform. FIC reduces the motion tracking error of the newly designed Gantry-Tau-3R and increases the motion fidelity for the users of the proposed SBMP. The proposed method is implemented using Matlab/Simulink software. Finally, the results demonstrate the accurate motion signal generation using linear model predictive motion cues with a fuzzy controller, which is not possible using the common parallel and serial manipulators.

This paper presents a new approach for the design of variable structure control (VSC) of nonlinear systems. The approach is based on estimation of joint acceleration signals with introduction of load estimation with the asymptotic observer. The control system is insensitive to parameter variations for a chosen switching hypersurface in conditions when it is reached by the dynamic motion with the required dynamics. The parameter insensitive response provided by this control method is demonstrated on the model of the SCARA robot. Simulation results confirm the validity of accurate tracking capability and the robust performance.

Space robotic systems are expected to play an increasingly important role in the future. Unlike on the earth, space operations require the ability to work in the unstructured environment. Some autonomous behaviors are necessary to perform complex and difficult tasks in space. This level of autonomy relies not only on vision, force, torque, and tactile sensors, but also the advanced planning and decision capabilities. In this paper, the authors study the autonomous target capturing from the issues of theory and experiments. Firstly, we deduce the kinematic and dynamic equations of space robotic system. Secondly, the visual measurement model of hand–eye camera is created, and the image processing algorithms to extract the target features are introduced. Thirdly, we propose an autonomous trajectory planning method, directly using the 2D image features. The method predicts the target motion, plans the end-effector's velocities and solves the inverse kinematic equations using practical approach to avoid the dynamic singularities. At last, numeric simulation and experiment results are given. The ground experiment system is set up based on the concept of dynamic simulation and kinematic equivalence. With the system, the experiments of autonomous capturing a target by a free-floating space robot, composed of a 6-DOF manipulator and a satellite as its base, are conducted, and the results validate the proposed algorithm.

In this work, the 3-RPRR, a new kinematically redundant planar parallel manipulator with six-degrees-of-freedom, is presented. First, the manipulator is introduced and its inverse displacement problem discussed. Then, all types of singularities of the 3-RPRR manipulator are analysed and demonstrated. Thereafter, the dexterous workspace is geometrically obtained and compared with the non-redundant 3-PRR planar parallel manipulator. Finally, based on a geometrical measure of proximity to singular configurations and the condition number of the manipulators' Jacobian matrices, actuation schemes for the manipulators are obtained. Different actuation schemes for a given path are obtained and the quality of their actuation schemes are compared. It is shown that the proposed manipulator is capable of following a path while avoiding the singularities.

This paper describes current progress in a project to develop robotic systems for locating underground chemical sources. There are a number of economic and humanitarian applications for this technology. Finding unexploded ordinance, land mines, and sources of leaks from pipes and tanks are some examples. Initial experiments were conducted using an ethanol chemical source buried in coarse sand. To gain an understanding of the sensory environment that would be experienced by a robot burrowing through the ground, the factors affecting transport of chemical vapour through soil were investigated. A robot search algorithrn was then developed for gathering chemical gradient inforrnation and using this to guide a robot towards the source. Experiments were performed using a chemical sensing probe positioned by a UMI RTX robot manipulator arm. The resulting system was successful in locating a source of ethanol vapour buried in sand. This paper includes details of experiments to characterise the sand used in this project, the robot search algorithm, sensor probe and results of source location trials.

Cooperative robots are usually required in flexible manufacturing systems or complex working environments. In particular, when an object under processing is too big or too heavy, a single robot may not be enough to handle it. Two or more manipulators are to be used in such a case. This paper presents the study of the dynamic equations for two industrial robots holding a rigid object. To this end, holonomic constraints are combined with the manipulators and object equations of motion to obtain the dynamic model of the whole system, which can be used for simulation purposes. Experimental results are presented to validate the theoretical results.

This paper presents a novel reconfigurable parallel mechanism, which can serve as a machine tool. The proposed parallel mechanism can change its structure parameters by driving a bevel gear system fixed in the base platform. First, the forward and inverse kinematics of the proposed mechanism are investigated. Second, the reachable workspace and Jacobian matrix are conducted. Based on the Jacobian matrix, the stiffness model and dexterity of the end effector are developed in detail. Finally, a multi-objective optimization is performed by using the Genetic Algorithm, and the workspace and global performance indexes of stiffness as well as the dexterity are considered as the performance indices to improve the performance of the reconfigurable parallel mechanism. Finally, Pareto frontier figure and several tables are provided to illustrate the results of the optimization. The results showed the proposed method has improved the performance of the reconfigurable machine tool in terms of its stiffness and dexterity.

Low cost automation often requires accurate positioning. This happens whenever a vehicle or robotic manipulator is used to move materials, parts or minerals on the factory floor or outdoors. In last few years, such vehicles and devices are mostly autonomous. This paper presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of particular importance for guidance, navigation, and control of mobile, autonomous vehicles. The Extended Kalman Filter (EKF) and the noise characteristic have been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results (more accurate) than the EKF. The presented method is suitable for real-time control and is relatively inexpensive. Also, it applies to fusion process with sensors different than INS or GPS.

This paper contributes by presenting a parameter identification procedure for n-degrees-of-freedom flexible joint robot manipulators. An advantage of the given procedure is the obtaining of robot parameters in a single experiment. Guidelines are provided for the computing of the joint position filtering and velocity estimation. The method relies in the filtered robot model, for which no acceleration measurements are required. The filtered model is expressed in regressor form, which allows applying a parameter identification procedure based on the least squares algorithm. In order to assess the performance of the proposed parameter identification scheme, an implementation of a least squares with forgetting factor (LSFF) parameter identification method is carried out. In order to assess the reliability of the tested identification schemes, a model-based trajectory tracking controller has been implemented twice in different conditions: one control experiment using the estimated parameters provided by the proposed scheme, and another experiment using the parameters given by the LSFF method. These real-time control experiments are compared with respect to numerical simulations using the estimated parameters for each identification method. For the proposed scheme, the comparison between experiments and numerical simulations indicates better accuracy in the torque and position prediction.

Steep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35°). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot’s center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.