Robot navigation is a large area of research, where many different approaches have already been tried, including navigation based on visual memories. The Sparse Distributed Memory (SDM) is a kind of associative memory based on the properties of high-dimensional binary spaces. It exhibits characteristics, such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach followed here was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store, adjusting memory parameters to the level of noise and inferring new paths from the learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. This paper reports novel results of the limits of the model under different typical navigation problems. The SDM showed to be very robust to illumination and scenario changes, occlusion and saturation. An algorithm to build a topological map of the environment based on the visual memories is also described.

In this paper, autonomous motion control approaches to generate the coordinated motion of a dual-arm space robot for target capturing are presented. Two typical cases are studied: (a) The coordinated dual-arm capturing of a moving target when the base is free-floating; (b) one arm is used for target capturing, and the other for keeping the base fixed inertially. Instead of solving all the variables in a unified differential equation, the solution equation of the first case is simplified into two sub-equations and practical methods are used to solve them. Therefore, the computation loads are largely reduced, and feasible trajectories can be determined. For the second case, we propose to deal with the linear and angular momentums of the system separately. The linear momentum conservation equation is used to design the configuration and the mounted pose of a balance arm to keep the inertial position of the base's center of mass, and the angular momentum conservation equation is used to estimate the desired momentum generated by the reaction wheels for maintaining the inertial attitude of the base. Finally, two typical tasks are simulated. Simulation results verify the corresponding approaches.

This paper extends a recently proposed singularity analysis method to lower-mobility parallel manipulators having an articulated nacelle. Using screw theory, a twist graph is introduced in order to simplify the constraint analysis of such manipulators. Then, a wrench graph is obtained in order to represent some points at infinity on the Plücker lines of the Jacobian matrix. Using Grassmann–Cayley algebra, the rank deficiency of the Jacobian matrix amounts to the vanishing condition of the superbracket. Accordingly, the parallel singularities are expressed in three different forms involving superbrackets, meet and join operators, and vector cross and dot products, respectively. The approach is explained through the singularity analysis of the H4 robot. All the parallel singularity conditions of this robot are enumerated and the motions associated with these singularities are characterized.

This paper presents a novel robotic platform, Abigaille II, designed to climb vertical surfaces using dry adhesion. Abigaille II is a lightweight hexapod prototype actuated by 18 miniaturized motors. The robot's feet consist of adhesive patches, which have microhairs with mushroom-shaped caps fixed on the top of millimeter-scale flexible posts. A pentapedal gait is used to climb flat vertical surfaces as this gait maximizes the number of legs in contact to the surface. Abigaille can however also walk by using other gaits, including the tripod gait.

This work is dedicated to the analysis of the application of active impedance control for the realisation of three objectives simultaneously: velocity regulation in free motion, impact attenuation and finally force tracking. At first, a brief analysis of active impedance control is made, deducing the value of each parameter in order to achieve the three objectives. It is demonstrated that the system may be made overdamped with the adequate selection of the parameters if the characteristics of the environment are known, avoiding high overshoots of force during the impact. The second and most important contribution of this work is an additional measure for impact control in the case when the characteristics of the environment are unknown. It consists in switching among different values of the parameters of the impedance in order to dissipate faster the energy of the system, limiting the peaks of force and avoiding losses of contact. The optimal switching criteria are deduced for every parameter in order to dissipate the energy of the system as fast as possible. The results are verified in simulation.

In this paper we propose an intuitive and practical grasp quality measure for grasping 3D objects with a multi-fingered robot hand. The proposed measure takes into account the object geometries through the concept of object wrench space. Physically, the positive measure value has a meaning of the minimum single disturbance that grasp cannot resist, while the negative measure value implies the minimum necessary helping force that restores a non-force-closure grasp into a force-closure one. We show that the measure value is invariant between similar grasps and also between different torque origins. We verify the validity of the proposed measure via simulations by using computer models of a three-fingered robot hand and polygonal objects.

In this paper, an approach based on linear programming (LP) is proposed for path planning in three-dimensional space, in which an aerial vehicle is requested to pursue a target while avoiding static or dynamic obstacles. This problem is very meaningful for many aerial robots, such as unmanned aerial vehicles. First, the tasks of target-pursuit and obstacle-avoidance are modelled with linear constraints in relative coordination according to LP formulation. Then, two weighted cost functions, representing the optimal velocity resolution, are integrated into the final objective function. This resolution, defined to achieve the optimal velocity, deals with the optimization of a pair of orthogonal vectors. Some constraints, such as boundaries of the vehicle velocity, acceleration, sensor range, and flying height, are considered in this method. A number of simulations, under static and dynamic environments, are carried out to validate the performance of generating optimal trajectory in real time. Compared with ant colony optimization algorithm and genetic algorithm, our method has less parameters to tune and can achieve better performance in real-time application.

In order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization algorithm using self-adaptive samples (abbreviated as SAMCL). By employing a pre-caching technique to reduce the online computational burden, SAMCL is more efficient than the regular MCL. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems together, thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of the SAMCL algorithm are demonstrated by both simulations and experiments carried out with different intentions. Extensive experimental results and comparisons are also given in this paper.

Contrary to the inverse kinematics, the forward kinematics of parallel manipulators involves solving highly non-linear equations and provides more than one feasible end-effector pose, which are called the assembly modes, for a given set of link lengths or joint angles. Out of the multiple feasible solutions, only one solution can be achieved from a certain initial configuration. Therefore, in this paper, the Kohonen's self-organizing map (SOM) is proposed to learn and classify the multiple solution branches of the forward kinematics and then provide a unique real-time solution among the assembly modes. Each solution of the multiple feasible ones is coded using IF-THEN rules based on the values of the passive joint variables. Due to not only the classification but also the associative memory learning abilities of the SOM, the passive joint variables vector, the end-effector pose vector, and this class code are associated with the active joint variables vector constituting the input vector to the SOM in the offline learning phase. In the online testing phase, only the active joint variables vector and the class code are fed to the SOM to obtain the unique end-effector pose vector. The Jacobian matrix calculated at the SOM output layer is used for further fine tuning this output to obtain an accurate end-effector pose vector. Simulations are conducted for 3-RPR and 3-RRR planar parallel manipulators to evaluate the performance of the proposed method. The results proved high accuracy of the desired unique solution in real-time.

Many industrial robots employ closed-loop actuating elements such as the parallelogram mechanism for increased stiffness. Modeling these manipulators for the purpose of calibration presents a challenge due to complex nonlinear couplings between parameters of the chains. The modeling method presented in this paper involves the integration of the open- and closed-loop elements whose errors can be resolved as linear functions of their parameters. As a result, the model is similar to that of a serial-link robot, which makes it possible to use existing well-defined calibration techniques in the area. Simulation and experimental studies on an industrial robot for verifying the correctness and effectiveness of the proposed model are also described.

SLAM is a well-studied technique for robots to build a map of environments while at the same time keeping track of their pose (position and orientation). However, SLAM does not provide motion control approaches for how the robot explores the environment. This paper presents an integrated approach to create a fully autonomous exploring and mapping robot. An Extended Kalman Filter (EKF)-SLAM approach is used to fuse Advanced Sonar measurements and the results of Laser Polar Scan Matching. The inclusion of Advanced Sonar data tackles the problem of map drifts in environments where lasers do not supply sufficient information, such as along a corridor. In addition, the proposed exploration strategy takes advantage of the characteristic of the Voronoi graph to enable the robot to strategically explore the environment in a loop-closing fashion and a safe manner. By revisiting areas to close loops as early as possible, the robot builds a more stable map incrementally while still reliably tracking its pose. Experimental results of the integrated approach are shown to demonstrate the paper's approach that provides real-time exploration of a mobile robot in an initially unknown real environment. Experimental map quality comparisons using exploration strategies show the benefits of early local loop-closing.

This paper exploits a natural symmetry present in a 3D robot in order to achieve asymptotically stable steering. The robot under study is composed of 5-links and unactuated point feet; it has 9 DoF (degree-of-freedom) in the single-support phase and six actuators. The control design begins with a hybrid feedback controller that stabilizes a straight-line walking gait for the 3D bipedal robot. The closed-loop system (i.e., robot plus controller) is shown to be equivariant under yaw rotations, and this property is used to construct a modification of the controller that has a local, but uniform, input-to-state stability (ISS) property, where the input is the desired turning direction. The resulting controller is capable of adjusting the net yaw rotation of the robot over a step in order to steer the robot along paths with mild curvature. An interesting feature of this work is that one is able to control the robot's motion along a curved path using only a single predefined periodic motion.

Vision sensors are increasingly being used in the implementation of Simultaneous Localization and Mapping (SLAM). Even though the mathematical framework of SLAM is well understood, considerable issues remain to be resolved when a particular sensing modality is considered. For instance, the observation model of a small baseline stereo camera is known to be highly nonlinear. As a consequence, state estimations obtained from standard recursive estimators, such as the Extended Kalman Filter, tend to be inconsistent. Further, vision-based approaches are plagued with high feature densities, and the consequent requisite of maintaining large feature databases for loop closure and data association. This paper proposes a two-tier solution for resolving these issues, inspired by the mechanics of human navigation. The proposed two-tier solution addresses the consistency issue by formulating the SLAM problem as a nonlinear batch optimization and presents a novel method for feature management through a two-tier map representation. Simulations and experiments are carried out in an office-like environment to validate the performance of the algorithm.

It is well known that Parallel Kinematic Mechanisms (PKMs) have an intrinsic dynamic potential (very high speed and acceleration) with high precision and high stiffness. Nevertheless, the choice of optimal dimensions that provide the best performances remains a difficult task, since performances strongly depend on dimensions. On the other hand, there are many criteria of performance that must be taken into account for dimensional synthesis, and which are sometimes antagonist. This paper presents an approach of multiobjective optimization for PKMs that takes into account several criteria of performance simultaneously that have a direct impact on the dimensional synthesis of PKMs. We first present some criteria of performance such as the workspace, transmission speeds, stiffness, dexterity, precision, as well as dynamic dexterity. Secondly, we present the problem of dimensional synthesis, which will be defined as a multiobjective optimization problem. The method of genetic algorithms is used to solve this type of multiobjective optimization problem by means of NSGA-II and SPEA-II algorithms. Finally, based on a linear Delta architecture, we present an illustrative application of this methodology to a 3-axis machine tool in the context of manufacturing of automotive parts.

This paper introduces the mathematical logical proposition into kinematics and presents a novel method for topological design and representation of mechanisms. The six-value non-classical logical matrix in three orders is proposed to represent spatial basic motions, relations, and selection of moving reference frame. The combinatorial logical sets of mechanisms are built and axiomatized to analyze serial–parallel hybrid mechanisms. In accordance to the logical relations, the mapping between topological arrangement and topological geometry is developed, and algorithm of decomposition synthesis is presented. Furthermore, the higher dimensional topological arrangement of multi-loop hybrid mechanism can be obtained by mapping of higher dimensional geometry. At last, four typical examples are developed to illustrate application of method of logical proposition in topological design of mechanisms. The method is helpful for topological design and analysis of multi-loop hybrid mechanisms.

In this paper, a Brain–Computer Interface (BCI) control approach for the assistive robotic system FRIEND is presented. The objective of the robot is to assist elderly and persons with disabilities in their daily and professional life activities. FRIEND is presented here from an architectural point of view, that is, as an overall robotic device that includes many subareas of research, such as human–robot interaction, perception, object manipulation and path planning, robotic safety, and so on. The integration of the hardware and software components is described relative to the interconnections between the various elements of FRIEND and the approach used for human–machine interaction. Since the robotic system is intended to be used especially by patients suffering from a high degree of disability (e.g., patients which are quadriplegic, have muscle diseases or serious paralysis due to strokes, or any other diseases with similar consequences for their independence), an alternative non-invasive BCI has been investigated. The FRIEND–BCI paradigm is explained within the overall structure of the robot. The capabilities of the robotic system are demonstrated in three support scenarios, one that deals with Activities of daily living (ADL) and two that are taking place in a rehabilitation workshop. The proposed robot was clinically evaluated through different tests that directly measure task execution time and hardware performance, as well as the acceptance of robot by end-users.

This paper covers a wide knowledge of physical and dynamical models useful for building flying robots and a new generation of flying platform developed in the similarity of flying animals. The goal of this work is to develop a simulation environment and dynamic control using the high-level calculation tool MatLab and the modeling, simulation, and analysis of dynamic systems tool Simulink. Once created the dynamic models to study, this work involves the study and understanding of the dynamic stability criteria to be adopted and their potential use in the control of flying models.

This paper proposes a novel design of a reconfigurable parallel kinematic manipulator used for a machine tool. After investigating the displacement and inverse kinematics of the proposed manipulator, it is found that the parasitic motions along x-, y-, and θz-axes can be eliminated. The system stiffness of the parallel manipulator is conducted. In order to locate the highest system stiffness, single and multiobjective optimizations are performed in terms of rotation angles in x- and y-axes and translation displacement in z-axis. Finally, a case study of tool path planning is presented to demonstrate the application of stiffness mapping. Through this integrated design synthesis process, the system stiffness optimization is conducted with Genetic Algorithms. By optimizing the design variables including end-effector size, base platform size, the distance between base platform and middle moving platform, and the length of the active links, the system stiffness of the proposed parallel kinematic manipulator has been greatly improved.

This paper proposes a novel design of a reconfigurable humanoid robot head, based on biological likeness of human being so that the humanoid robot could agreeably interact with people in various everyday tasks. The proposed humanoid head has a modular and adaptive structural design and is equipped with three main components: frame, neck motion system and omnidirectional stereovision system modules. The omnidirectional stereovision system module being the last module, a motivating contribution with regard to other computer vision systems implemented in former humanoids, it opens new research possibilities for achieving human-like behaviour. A proposal for a real-time catadioptric stereovision system is presented, including stereo geometry for rectifying the system configuration and depth estimation. The methodology for an initial approach for visual servoing tasks is divided into two phases, first related to the robust detection of moving objects, their depth estimation and position calculation, and second the development of attention-based control strategies. Perception capabilities provided allow the extraction of 3D information from a wide range of visions from uncontrolled dynamic environments, and work results are illustrated through a number of experiments.

This paper presents a model of a three-joint (four links) carangiform fish robot. The smooth gait or smooth motion of a fish robot is optimized by using a combination of the Genetic Algorithm (GA) and the Hill Climbing Algorithm (HCA) with respect to its dynamic system. Genetic algorithm is used to create an initial set of optimal parameters for the two input torque functions of the system. This set is then optimized by using HCA to ensure that the final set of optimal parameters is a “near” global optimization result. Finally, the simulation results are presented in order to demonstrate that the proposed method is effective.