This paper aims to show a sensory-motor coordination control scheme that realizes stable pinching of rigid objects with parallel or nonparallel flat surfaces movable in 2-dimensional vertical plane by a pair of robot fingers with hemispherical ends. The proposed control signal is composed of gravity compensation for fingers, damping shaping, exertion of forces to the object from opposite directions, generation of moments for balancing of rotational moments, and regressors for estimating unknown steady-state terms, all of which neither need the knowledge of object parameters nor use any object sensing data. In other words, stable grasping can be realized by using only finger-joint sensing in a blind manner without using force sensors or tactile sensing. Stability of pinching motion with convergence to the state of force/torque balance is shown through computer simulations and is also proved theoretically.

This paper presents a novel method for localization of mobile robots in structured environments. The estimation of the position and orientation of the robot relies on the minimisation of the partial Hausdorff distance between ladar range measurements and a floor plan image of the building. The approach is employed in combination with an extended Kalman filter to obtain accurate estimates of the robot's position, heading and velocity. Good estimates of these variables were obtained during tests performed using a differential drive robot, thus demonstrating that the approach provides an accurate, reliable and computationally feasible alternative for indoor robot localization and autonomous navigation.

This paper suggests one possible mechanism for the biological flexor reflex by emulating a cat's behavior with its robotic counterpart, making it capable of walking around and clearing obstacles autonomously in various environments. A central pattern generator and a hip-to-knee mapping function are employed to realize basic rhythmic motion for a quadrupedal robot. When an input from a contact sensor on the robot's toe is detected, a patterned motion generated by the flexor reflex emerges depending on the location of the bumping phase, and replaces the ongoing rhythmic motion of that leg, causing it to be raised high enough to clear the obstacle. By restricting this reflex to within one cycle time of the walk and only to the bumping leg, rhythm and stability of motion are ensured. Numerical simulations and experimental implementation on a physical quadrupedal robot demonstrate the effectiveness of the proposed method.

Robotic sensing is a relatively new field of activity compared with the design and control of robot mechanisms. In both areas the role of geometry is natural and necessary for the development of devices, their control and use in challenging environments. At the very beginning odometry, tactile and touch sensors dominated robot sensing. More recently, due to the fall in the price of laser devices, they have become more attractive to the community. On the other hand, progress in photogrametry, particularly during the nineties as the n-view geometry in projective geometry matured, boot-strapped the use of computer vision as an extra powerful sensor technique for robot guidance. Cameras were used in monocular or stereoscopic fashion, catadioptric systems for ominidirectional vision, fish-eye cameras and camera networks made the use of computer vision even more diverse. Researchers started to combine sensors for 2D and 3D sensing by fusing sensor data in a projective framework. Thanks to the continuous progress in mechatronics, the low prices of fast computers and increasing accuracy of sensor systems, one can build a robot to perceive its surroundings, reconstruct, plan and ultimately act intelligently. In these perception-action systems there is of course, the urgent need for a geometric stochastic framework to deal with uncertainty in the sensing, planning and action in a robust manner. Here geometry can play a central role for the representation and computing in higher dimensions using projective geometry and differential geometry on Lie groups manifolds with a pseudo Euclidean metric. Let us review briefly the developments towards modern geometry that have been often overlooked by the robotic researchers and practitioners.

The automatic landmark identification is very important in autonomous robot navigation tasks. In this paper, we use a monocular omnidirectional vision system to extract the image features and the conformal geometric algebra to compute the projective invariants from such features. We show how these features can be used to compute projective and permutation p2-invariants from any kind of omnidirectional vision system. The p2-invariants represent scene sublandmarks, and a set of them characterize a landmark. The advantage of this representation is that the landmarks are more robust than the single cross-ratio.

This work considers the problem of force/position regulation for a robotic finger in compliant contact with an unknown curved surface resulting in uncertain force and position control subspaces. The proposed controller is an adaptive control scheme of a simple structure that achieves the desired target by the on-line tuning of the position and force control actions to their corresponding actual subspaces at the desired point using motion state feedback. The local asymptotic stability of the system equilibrium point is proved and an estimate of the region of attraction is given. The controller performance is illustrated by a simulation example.

A novel method of Zero-Moment-Point (ZMP) compensation is proposed to improve the stability of locomotion of a biped, which is subjected to disturbances. A compensating torque is injected into the ankle-joint of the foot of the robot to improve stability. The value of the compensating torque is computed from the reading of the force sensors located at the four corners of each foot. The effectiveness of the method is verified on a humanoid robot, MANUS-I. With the compensation technique, the robot successfully rejected disturbances in different forms. It carried an additional weight of 390 gm (17% of body weight) while walking. Also, it walked up a 10° slope and walked down a 3° slope.

This paper reports the design of sliding-mode control laws for controlling multiple small-sized autonomous helicopters in arbitrary formations. Two control schemes, which are required for defining arbitrary three-dimensional formation meshes, are discussed. In the presented leader–follower formation control schemes, each helicopter only needs to receive motion information from at most two neighboring helicopters. A nonlinear six-degree-of-freedom dynamic model has been used for each helicopter. Four control inputs, the main and the tail rotor thrusts, and the roll and pitch moments, are assumed. Parameter uncertainty in the dynamic model and wind disturbance are considered in designing the controllers. The effectiveness and robustness of these control laws in the presence of parameter uncertainty in the dynamic model and wind disturbances are demonstrated by computer simulations.

In this paper, we present a novel structure of a snake-like robot. This structure enables passive locomotion in snake-like robots. Dynamic equations are obtained for motion in a horizontal plane, using Gibbs–Appell method. Kinematic model of the robot include numerous nonholonomic constraints, which can be omitted at the beginning by choosing proper coordinates to describe the model in Gibbs–Appell framework. In such a case, dynamic equations will be significantly simplified, resulting in considerable reduction of simulation time. Simulation results show that, by proper selection of initial conditions, joint angles operate in a limit cycle and robot can locomote steadily on a passive trajectory. It can be seen that the passive trajectory is approximately a Serpenoid curve.

This paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method to non-Gaussian noise. Among the different techniques compared are the well-known Kalman filters and their different variants (e.g. extended and unscented), and the more recent techniques relying on Sequential Monte Carlo Sampling methods, such as particle filters and Gaussian Mixture Sigma Point Particle Filter.

In this study, we consider a model based robust control scheme for kinematically redundant robot manipulators that also enables the use of self motion of the manipulator to perform multiple sub-tasks (e.g., maintaining manipulability, avoidance of mechanical joint limits, and obstacle avoidance). The controller proposed ensures uniformly ultimately bounded end-effector and sub-task tracking despite the parametric uncertainty associated with the dynamic model. A Lyapunov based approach has been utilized in the controller design and extension to a non minimum set of parameters for orientation representation has been presented to illustrate the flexibility of the approach. Extensive simulation studies performed initially on a 3 link planar robot arm (for the planar case) and on a six degree of freedom (DOF) Puma type robot arm (for the 3D case with quaternion feedback) are presented to demonstrate the capabilities and the performance of the controller. The results were then experimentally tested on an actual Puma 560 robot to illustrate the feasibility of the proposed method.

This paper presents a global ultrasonic system with selective activation algorithm for autonomous navigation of an indoor mobile robot. The global ultrasonic system consists of several ultrasonic transmitters fixed at reference positions in global coordinates and two receivers at moving coordinates of a mobile robot. By activating the ultrasonic transmitters through an radiofrequency (RF) channel, the robot is able to obtain distance information to the reference positions and localize itself in the global coordinates. Due to limitations in signal strength and beam width, the ultrasonic signals from some transmitters may not be delivered to the robot and the ultrasonic data become invalid. In order to improve the effectiveness of the global ultrasonic system, a so-called selective activation algorithm is developed. Based on the current position of the robot, the selective activation calls a proper ultrasonic transmitter and generates valid ultrasonic data at every sampling instant, resulting in faster, more accurate response for self-localization than does simple sequential activation. Path-following control experiments are conducted to verify the effectiveness of the self-localization based on the proposed selective activation algorithm with the global ultrasonic system.

A nonholonomic system subjected to external noise from the environment, or internal noise in its own actuators, will evolve in a stochastic manner described by an ensemble of trajectories. This ensemble of trajectories is equivalent to the solution of a Fokker–Planck equation that typically evolves on a Lie group. If the most likely state of such a system is to be estimated, and plans for subsequent motions from the current state are to be made so as to move the system to a desired state with high probability, then modeling how the probability density of the system evolves is critical. Methods for solving Fokker-Planck equations that evolve on Lie groups then become important. Such equations can be solved using the operational properties of group Fourier transforms in which irreducible unitary representation (IUR) matrices play a critical role. Therefore, we develop a simple approach for the numerical approximation of all the IUR matrices for two of the groups of most interest in robotics: the rotation group in three-dimensional space, SO(3), and the Euclidean motion group of the plane, SE(2). This approach uses the exponential mapping from the Lie algebras of these groups, and takes advantage of the sparse nature of the Lie algebra representation matrices. Other techniques for density estimation on groups are also explored. The computed densities are applied in the context of probabilistic path planning for kinematic cart in the plane and flexible needle steering in three-dimensional space. In these examples the injection of artificial noise into the computational models (rather than noise in the actual physical systems) serves as a tool to search the configuration spaces and plan paths. Finally, we illustrate how density estimation problems arise in the characterization of physical noise in orientational sensors such as gyroscopes.

This research presents a comprehensive and useful survey of the kinematic models of wheeled mobile robots and their optimal configurations. The kinematic modeling of wheeled mobile robots with no-slip is presented, by considering four common types of wheels: fixed, orientable, castor, and Swedish. Next, the accuracy of the kinematic models is discussed considering their sensitivity or relative error amplification, giving rise to the isotropy concept. As practical application of the previous theory, all types of three-wheeled mobile robots are modeled and their optimal (isotropic) configurations for no error amplification are obtained. Finally, three practical examples of error amplification are developed for several types of wheeled mobile robots in order to illustrate the benefits and limitations of the isotropic configurations.

This paper suggests a generalized approach to the mathematical modeling of biped locomotion systems (humans or humanoid robots) with special attention paid to impact and contact dynamics. Instead of the usual inductive approach that starts from the analysis of different situations of real motion (walking, running, jumping, climbing the stairs, etc.) and tries to make a generalization, a deductive approach is pursued, whereby an entirely general problem is considered. Impact dynamics is modeled applying the linear complementarity problem (LCP) formulation. General methodology is explained and demonstrated via the synthesis of a spatial biped model. The validity of the modeling approach is proved by experimental measurements on a human subject in the laboratory conditions. Plenty of graphic presentations illustrating experimental results, as well as the results of the corresponding simulation tests are shown.

This paper studies the mechanical configuration and the periodic gaits of multi-legged locomotion systems based on its kinematic and dynamic models. The purpose is to determine the system performance during walking, and the best set of locomotion variables that minimize a set of optimization indices. In this perspective, two kinematic and four dynamic indices are formulated to quantitatively measure the performance of the walking robot. The kinematic indices consist of the perturbation analysis and the locomobility measure, and the dynamic performance indices of the walking robot locomotion are the mean absolute density of energy, the mean power density dispersion, the density of power lost and the mean force at the body-legs interface. A set of model-based simulation experiments reveals the system configuration and the type of movements that lead to a better performance, for a specific locomotion mode, from the viewpoint of the proposed indices.

Operational space control of industrial robots is addressed in this document. We analyze a two-loop hierarchical control with the resolved motion rate controller (RMRC) as outer loop and the joint velocity PI controller as inner loop; the latter is the typical velocity controller used in industrial robots. We prove, by the first time, that these simple controllers make the solutions of the closed-loop system uniformly ultimately bounded. Additionally, we give some simple guidelines for the selection of the control gains so as to ensure an explicit bound of the tracking error.

Genetic algorithm is used to determine the optimal capture points for the multi agents required to grasp a moving generic prismatic object by arresting it in form closure. Thereafter, the agents approach their respective moving goals using a decentralized projective path planning algorithm. Post arrest, the object is guided along a desired linear path to a desired goal point. Form closure of the object is obtained using the concept of accessibility angle. A convex envelop is formed around the object, and the goal points on the object boundary are mapped onto the envelope. The robots approach the mapped goal points first, and then, converge on the actual object. This ensures that the agents reach the actual goal points almost simultaneously, and do not undergo looping at a local concave region. The object is assumed alive while being captured but is assumed compromised thereafter. Post arrest, robots alter their positions optimally around the object to transport it along a desired direction. Frictionless point contact between the object and a robot is assumed. The shape of the mobile robot is considered cylindrical such that it can only apply force along the outward radial direction. Simulation results are presented that illustrate the effectiveness of the proposed method.

Motor algebra, a 4D degenerate geometric algebra, offers a rigorous yet simple representation of the 3D velocity of a rigid body. Using this representation, we study 3D extended arm pointing and reaching movements. We analyze the choice of arm orientation about the vector connecting the shoulder and the wrist, in cases for which this orientation is not prescribed by the task. Our findings show that the changes in this orientation throughout the movement were very small, possibly indicating an underlying motion planning strategy. We additionally examine the decomposition of movements into submovements and reconstruct the motion by assuming superposition of the velocity profiles of the underlying submovements by analyzing both the translational and rotational components of the 3D spatial velocity. This movement decomposition method reveals a larger number of submovement than is found using previously applied submovement extraction methods that are based only on the analysis of the hand tangential velocity. The reconstructed velocity profiles and final orientations are relatively close to the actual values, indicating that single-axis submovements may be the basic building blocks underlying 3D movement construction.