This paper deals with the design and implementation of a visual kinematic control scheme for a redundant manipulator. The inverse kinematic map for a redundant manipulator is a one-to-many relation problem; i.e. for each Cartesian position, multiple joint angle vectors are associated. When this inverse kinematic relation is learnt using existing learning schemes, a single inverse kinematic solution is achieved, although the manipulator is redundant. Thus a new redundancy preserving network based on the self-organizing map (SOM) has been proposed to learn the one-to-many relation using sub-clustering in joint angle space. The SOM network resolves redundancy using three criteria, namely lazy arm movement, minimum angle norm and minimum condition number of image Jacobian matrix. The proposed scheme is able to guide the manipulator end-effector towards the desired target within 1-mm positioning accuracy without exceeding physical joint angle limits. A new concept of neighbourhood has been introduced to enable the manipulator to follow any continuous trajectory. The proposed scheme has been implemented on a seven-degree-of-freedom (7DOF) PowerCube robot manipulator successfully with visual position feedback only. The positioning accuracy of the redundant manipulator using the proposed scheme outperforms existing SOM-based algorithms.

A novel analytic approach is proposed for determining the singularities of some four degree of freedom (DOF) parallel manipulators (PMs). First, the constraint and displacement of a general 4-DOF PM are analyzed. Second, a common 3 × 4 translational Jacobian matrix Jν and a common 3 × 4 rotational Jacobian matrix Jω are derived, and a 4 × 4 general Jacobian matrix J of the 4-DOF PMs is derived from Jν and Jω. Since a complicated process to determine singularities from the 4 × 6 Jacobian matrix is transformed into a simple process to determine singularity from J, the singularities of the some 4-DOF PMs with 3 translations and 1 rotation, or with 3 rotations and 1 translation, or with combined translation–rotations are analyzed and determined easily by this approach.

In the present paper, the kinematics of a three-degree-of-freedom spherical wrist is investigated. The wrist consists of a fixed base connected to a moving platform by three identical legs, each with a RRUR chain (R and U denote a revolute pair and a universal pair, respectively). For each leg, the first R pair is to be considered actuated. Although in previous works the kinematics synthesis of this architecture was carried out, no detailed studies were presented on the kinematic issues of the wrist. This paper presents the mobility analysis, the direct and inverse position kinematics, the differential kinematics of the manipulator including inspection on the jacobian matrix and the analysis of the singularities. The geometrical condition matched in case of mechanical interference between legs is addressed, too. A numerical example of the manipulator kinematics was performed to obtain the workspace, the condition number and the mechanical inteference condition.

Inspired by the mechanisms of idiotypic network hypothesis and ant finding food, a hybrid ant colony and immune network algorithm (AC-INA) for motion planning is presented. Taking the environment surrounding the robot and robot action as antigen and antibody respectively, an artificial immune network is constructed through the stimulation and suppression between the antigen and antibody, and the antibody network is searched using improved ant colony algorithm (ACA) with pseudo- random-proportional rule and super excellent ant colony optimization strategy. To further accelerate the convergence speed of AC-INA and realize the optimal dynamic obstacle avoidance, an improved adaptive artificial potential field (AAPF) method is provided by constructing new repulsive potential field on the basis of the relative position and velocity between the robot and obstacle. Taking the planning results of AAPF method as the prior knowledge, the initial instruction definition of new antibody is initialized through vaccine extraction and inoculation. During the motion planning, once the robot meets with moving obstacles, the AAPF method is used for the optimal dynamic obstacle avoidance. The simulation results indicate that the proposed algorithm is characterized by good convergence property, strong planning ability, self-organizing, self-learning, and optimal obstacle avoidance in dynamic environments. The experiment in known indoor environment verifies the validity of AAPF-based AC-INA, too.

Many of the advanced driver assistance systems have the goal of perceiving the surroundings of a vehicle. One of them, adaptive cruise control, takes charge of searching for other vehicles in order to detect and track them with the aim of maintaining a safe distance and to avoid dangerous maneuvers. In the research described in this article, this task is accomplished using an on board camera. Depending on when the vehicles are detected the system analyzes movement or uses a vehicle geometrical model to perceive them. After, the detected vehicle is tracked and its behavior established. Optical flow is used for movement while the geometric model is associated with a likelihood function that includes information of the shape and symmetry of the vehicle and the shadow it casts. A genetic algorithm finds the optimum parameter values of this function for every image. As the algorithm receives information from a road detection module some geometric restrictions are applied. Additionally, a multiresolution approach is used to speed up the algorithm. Examples of real image sequences under different weather conditions are shown to validate the algorithm.

This paper focuses on the motion control of the two-degree-of-freedom (2-DOF) planar parallel manipulator of a hybrid machine tool. On the basis of the performance analysis of the kinematic control system, a parameter-tuning method is proposed for regulating the control parameters. To improve the response performance, the proportional-derivative control and a low-pass filter are introduced to the position-loop controller. The simulation shows that the response speed is increased and that the tracking error is reduced. Furthermore, the effect of load torque on the contour error is investigated, and the dynamic feedforward control is used to control the parallel manipulator. On the basis of the principle of dual-channel compensation, the feedforward compensator is designed. The simulations, that the moving platform moves along a linear trajectory and circular trajectory, show that the dynamic feedforward control can reduce the effect of load torque on contour error.

This paper presents a control algorithm for biped walking by extension of previous work in the fields of central pattern generator (CPG) and passive walking. The algorithm takes advantage of the passive dynamics of walking, assisting only when necessary with an intermittent sinusoidal oscillator. The parameterized oscillator is used to drive the hip joint; the triggering and ceasing of the oscillator during a walking cycle can be modulated by the sensory feedback. The results from simulation indicate a stable, efficient gait, and robustness against model inaccuracy and environmental variation. We also examine the effects of oscillator parameters and link parameters on the gait, and design a controller to suppress the bifurcation phenomenon based on the error of prior step periods.

This paper presents a task-priority motion planning algorithm for underactuated robotic systems. The motion planning algorithm combines two features: the idea of the task-priority control of redundant manipulators and the endogenous configuration space approach. This combination results in the algorithm which solves the primary motion planning task simultaneously with one or more secondary tasks ordered in accordance with decreasing priorities. The performance of the task-priority motion planning algorithm has been illustrated with computer simulations of the motion planning problem for a container ship.

In recent years hexagonal hexapod robots gained the interest of international research community. The aim of this paper is twofold. First, after summarizing all known gaits of such robots, we introduce some improvements both for normal conditions and for fault tolerance. Then we show the advantages of hexagonal hexapod robots over rectangular ones by comparing different gaits from theoretical and experimental points of view. Stability, fault tolerance, turning ability, and terrain adaptability are analyzed. For reaching these aims we also introduce a robot kinematics that considers at the same time supporting and transferring legs. The trajectories of feet are described as well. Finally, single leg stride selection is studied for side wave and for kick-off gaits to optimize walking ability and energy management.

The theoretical results presented herein have been validated with experiments conducted on a prototype of the Novel Robotics System for Planetary Exploration (Rovetta et al., “New Robot Concepts for Mars Soil Exploration: Mechanics and Functionality,” ASTRA 2004, Eighth ESA Workshop on Advanced Space Technologies for Robotics and Automatian, Nordwijk, The Netherlands Nov. 2–4, 2004) (NOROS), developed by Politecnico di Milano and Beijing University of Astronautics and Aeronautics, and the results are summarized in this paper.

In this work a new nonoverconstrained redundant decoupled robot, free of compound joints, formed from three parallel manipulators, with two moving platforms and provided with six active limbs connected to the fixed platform, called LinceJJP, is presented. Interesting applications such as multi-axis machine tools with parallel kinematic architectures, solar panels, radar antennas, and telescopes are available for this novel spatial mechanism.

This paper presents the requirements, design criteria and methodology used to develop the design of a new self-contained prosthetic hand to be used by transradial amputees. The design is based on users' needs, on authors background and knowledge of the state of the art, and feasible fabrication technology with the aim of replicating as much as possible the functionality of the human hand. The paper focuses on the design approach and methodology which is divided into three steps: (i) the mechanical actuation units, design and actuation distribution; (ii) the mechatronic development and finally (iii) the controller architecture design. The design is presented here and compared with significant commercial devices and research prototypes.

In our previous work, a random-sampling-based footstep planner has been proposed for global biped navigation. Goal-probability threshold (GPT) is the key parameter that controls the convergence rate of the goal-biased nonuniform sampling in the planner. In this paper, an approach to optimized GPT adaptation is explained by a benchmarking planning problem. We first construct a benchmarking model, in which the biped navigation problem is described in selected parameters, to study the relationship between these parameters and the optimized GPT. Then, a back-propagation (BP) neural network is employed to fit this relationship. With a trained BP neural network modular, the optimized GPT can be automatically generated according to the specifications of a planning problem. Compared with previous methods of manual and empirical tuning of GPT for individual planning problems, the proposed approach is self-adaptive. Numerical experiments verified the performance of the proposed approach and furthermore showed that planning with BP-generated GPTs is more stable. Besides the implementation in specific parameterized environments studied in this paper, we attempt to provide the frame of the proposed approach as a reference for footstep planning in other environments.

This paper deals with a comparative study of two planar two degrees of freedom parallel mechanisms: one with 2-RRR and the other with 3-RRR structures. For a fair comparison, both 3-RRR and 2-RRR mechanisms are symmetrical. The kinematics and Jacobian matrix of the two mechanisms are studied. Conditioning index, stiffness index and payload index are proposed to compare the condition, stiffness and payload performance of the two mechanisms. The results show that the 2-RRR and 3-RRR mechanisms have the similar condition performance, and the planar 3-RRR mechanism has better payload and stiffness performance than the 2-RRR one.

Figures 2 and 5 were incorrectly reproduced in the above publication (Ratajczak et al. 2009). The figures are reproduced below in their correct form. Fig. 2.

Task-priority algorithm (both S1 and S2).

Single-task algorithm (only S1).