The problem of wheeled mobile robot (WMR) navigation toward an unknown target in a cluttered environment has been considered. The biologically inspired navigation algorithm is the equiangular navigation guidance (ENG) law combined with a local obstacle avoidance technique. The collision avoidance technique uses a system of active sensors which provides the necessary information about obstacles in the vicinity of the robot. In order for the robot to avoid collision and bypass the enroute obstacles, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The performance of the navigation strategy is confirmed with computer simulations and experiments with ActivMedia Pioneer 3-DX wheeled robot.

This paper presents new low-cost systems for the automation of some fish farm operations. Particularly, computer vision is applied to non-contact fish weight estimation. Stereo vision systems with synchronised convergent cameras are employed to perform fish 3-D segmentation in tanks and sea cages. Several pre-processing algorithms are applied to compensate for illumination local variations. The approach applied for fish 3-D segmentation consists in detecting in both images certain fish features. Once these points have been detected and validated in both images, the fish are 3-D segmented by applying stereo vision matching considerations. Fish weight is estimated by using simple length-weight relations well known in the aquaculture domain. The paper also briefly describes robotics systems for fish feeding and underwater pond cleaning, which can be also used to implement the above mentioned computer vision techniques for the fish estimation.

The estimation of the 2D relative motion of an indoor robot using monocular vision is presented. The camera calibration is known, and its motion is limited to be planar. These constraints are included in the robust regression of epipolar geometry from point matches. Motion is derived from the epipolar geometry. A sequence of 54 real images is used to test the algorithm. Accurate motion, both in rotation and translation angles of 0.4 and 1.7 deg, is successfully derived.

This work examines the Cooperative Hunters problem, where a swarm of unmanned air vehicles (UAVs) is used for searching one or more “evading targets,” which are moving in a predefined area while trying to avoid a detection by the swarm. By arranging themselves into efficient geometric flight configurations, the UAVs optimize their integrated sensing capabilities, enabling the search of a maximal territory.

This paper addresses the problems of barrier coverage and sweep coverage in a corridor environment with a network of self-deployed mobile autonomous robotic sensors. Using the ideas of nearest neighbor rules and information consensus, we propose a decentralized control law for the robotic sensors to solve the coverage problems. Numerical simulations illustrate the effectiveness of the proposed algorithm. The results in this paper demonstrate that such simple motion coordination rules can play a significant role in addressing the issue of coverage in a mobile robotic sensor network.

An efficient, simple, and practical real time path planning method for multiple mobile robots in dynamic environments is introduced. Harmonic potential functions are utilized along with the panel method known in fluid mechanics. First, a complement to the traditional panel method is introduced to generate a more effective harmonic potential field for obstacle avoidance in dynamically changing environments. Second, a group of mobile robots working in an environment containing stationary and moving obstacles is considered. Each robot is assigned to move from its current position to a goal position. The group is not forced to maintain a formation during the motion. Every robot considers the other robots of the group as moving obstacles and hence the physical dimensions of the robots are also taken into account. The path of each robot is planned based on the changing position of the other robots and the position of stationary and moving obstacles. Finally, the effectiveness of the scheme is shown by modeling an arbitrary number of mobile robots and the theory is validated by several computer simulations and hardware experiments.

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.

The Tangent Bundle Rapidly Exploring Random Tree (TB-RRT) is an algorithm for planning robot motions on curved configuration space manifolds, in which the key idea is to construct random trees not on the manifold itself, but on tangent bundle approximations to the manifold. Curvature-based methods are developed for constructing tangent bundle approximations, and procedures for random node generation and bidirectional tree extension are developed that significantly reduce the number of projections to the manifold. Extensive numerical experiments for a wide range of planning problems demonstrate the computational advantages of the TB-RRT algorithm over existing constrained path planning algorithms.

Manipulability ellipsoids are effective tools to perform task space analysis of robotic manipulators in terms of velocities, accelerations and forces at the end effector. In this paper a new definition of a dynamic manipulability ellipsoid for redundant manipulators is proposed which leads to more correct results in evaluating manipulator capabilities in terms of task-space accelerations. The case of manipulators in singular configurations is also analyzed. Two case studies are presented to illustrate the correctness of the proposed approach.

A non-holonomic robot with a bounded control input travels in a dynamic unknown 3D environment with moving obstacles. We propose a 3D navigation strategy to reach a given final destination point while avoiding collisions with obstacles. A formal analysis of the proposed 3D robot navigation algorithm is given. Computer simulation results and experiments with a real flying autonomous vehicle confirm the applicability and performance of the proposed guidance approach.

This paper describes a micro-optical three-axis tactile sensor capable of sensing not only normal force, but also shearing force. The normal force was detected from the integrated gray-scale values of bright pixels emitted from the contact area of conical feelers. The conical feelers were formed on a rubber sheet surface that maintains contact with an optical waveguide plate. The shearing force was detected from horizontal displacement of the conical feeler. In the experiments, a precise multi-axial loading machine was developed to measure sensing characteristics of the present sensor. Results show that the normal force was specified uniquely under combined force conditions and that the shearing force was specified by modifying the relationship between the shearing force and the horizontal displacement on the basis of normal force. We formulated a set of expressions to derive the normal force and the shearing force by taking into account this modification. Furthermore, calibration coefficients were identified for transforming the integration of gray-scale values into the normal force and for transforming the horizontal displacement into the shearing force. This result suggests that the expressions can estimate the normal force and the shearing force in wide-load regions.

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.

The current paper presents a path planning method based on probability maps and uses a new genetic algorithm for a group of UAVs. The probability map consists of cells that display the probability which the UAV will not encounter a hostile threat. The probability map is defined by three events. The obstacles are modeled in the probability map, as well. The cost function is defined such that all cells are surveyed in the path track. The simple formula based on the unique vector is presented to find this cell position. Generally, the cost function is formed by two parts; one part for optimizing the path of each UAV and the other for preventing UAVs from collision. The first part is a combination of safety and length of path and the second part is formed by an exponential function. Then, the optimal paths of each UAV are obtained by the genetic algorithm in a parallel form. According to the dimensions of path planning, genetic encoding has two or three indices. A new genetic operator is introduced to select an appropriate pair of chromosome for crossover operation. The effectiveness of the method is shown by several simulations.

This paper is concerned with a stability theory of motion governed by Lagrange's equation for a pair of multi-degrees of freedom robot fingers with hemi-spherical finger ends grasping a rigid object under rolling contact constraints. When a pair of dual two d.o.f. fingers is used and motion of the overall fingers-object system is confined to a plane, it is shown that the total degree of freedom of the fingers-object system is redundant for realization of stable grasping though there arise four algebraic constraints. To resolve the redundancy problem without introducing any other extra and artificial performance index, a concept of stability of motion starting from a higher dimensional manifold to a lower-dimensional manifold, expressing a set of states of stable grasp with prescribed contact force, is introduced and thereby it is proved in a rigorous way that stable grasp in a dynamic sense is realized by a sensory feedback constructed by means of measurement data of finger joint angles and the rotational angle of the object. Further, it is shown that there exists an additional sensory feedback that realizes not only stable grasp but also orientation control of the object concurrently. Results of computer simulation based on Baumgarte's method are presented, which show the effectiveness of the proposed concept and analysis.

Strawberry is a very delicate fruit that requires special treatment during harvesting. A hierarchical control scheme is proposed based on a fuzzy controller for the force regulation of the gripper and proper grasping criteria, that can detect misplaced strawberries on the gripper or uneven distribution of forces. The design of the gripper and the controller are based on conducted experiments to measure the maximum gripping force and the required detachment force under a variety of detachment techniques. It is demonstrated that the hand motion for detaching the fruit from the stem has a significant role in the process because it can reduce the required force. By analysing those results a robotic gripper with pressure profile sensors is developed that demonstrates an efficiency comparable to the human hand for strawberry grasping. The designed gripper and fuzzy controller performance is tested with a considerable number of fresh fruits to demonstrate the effectiveness to the uncertainties of strawberry grasping.

This paper describes a new approach to the parameterization of robot excitation trajectories for optimal robot identification. The trajectory parameterization is based on a combined Fourier series and polynomial functions. The coefficients of the Fourier series are optimized for minimal sensitivity of the identification to measurement disturbances, which is measured as the d-optimality criterion, taking into account motion constraints in joint and Cartesian space. This parameterization satisfies both the guarantees of convergence by adding terms and the matching of the boundary conditions. Application of the method for the identification of the CRS A465 industrial robot proves the validity of the proposed approach.

MDSF is a generic manipulator development and simulation facility developed by Spar Aerospace Limited, as a prime contractor to the Canadian Space Agency. It is an integral part of Canada's contribution to the International Space Station (ISS) program. MDSF is capable of simulating general robotic systems with complex and flexible structures undergoing all kinds of tasks including payload hand-over and contact with the environment. Rigorous validation and configuration control have been imposed throughout the development course of the simulation facility. Examples of current applications in the ISS program are included.

The paper is aimed at generating optimal gait cycles in the sagittal plane of a biped, the locomotion system of which has anthropomorphic characteristics. Both single and double support phases are globally optimised, considering incompletely specified transition postural configurations from one phase to the other. An impactless heel-touch is prescribed. Full dynamic models are developed for both gait phases. They are completed by specific constraints attached to the unilaterality of contact with the supporting ground.

A parametric optimisation method is implemented. The biped joint coordinates are approximated by cubic splines functions connected at uniformly distributed knots along the motion time. The finite set of unknowns consists of the joint coordinate values at knots, some gait pattern parameters at phase transitions, and the motion time of each phase. The step length is adjusted to the prescribed gait speed by the optimisation process. Numerical simulations concerning slow and fast optimal gaits are presented and discussed.

One of the simplest and natural appealing motion control strategies for robot manipulators is the PD control with feedforward compensation. Although successful experimental tests of this control scheme have been published since the beginning of the eighties, the proof of global asymptotic stability has remained unattended until now. The contribution of this paper is to prove that global asymptotic stability can be guaranteed provided that the proportional and derivative gains are adequately selected. The performance of the PD control with feedforward compensation evaluated on a two degrees-of-freedom direct-drive arm appears as fine as the classical model-based computed torque control scheme.

In this paper, an optimal kinematic design method of a three translational DoFs parallel manipulator is presented. The design is based on the concept of performance chart, which can show the relationship between a criterion and design parameters graphically and globally. The normalization on the design parameters of the studied manipulator makes it possible that the design space, which is made up of the normalized parameters, is limited. The design space includes of all possible basic similarity manipulators (BSMs). As any one of the BSMs represents all of its similarity manipulators (SMs) in terms of performances, if one BSM is optimal, its SMs are optimized as well. The said optimal BSM is from the optimum region, which is the intersecting result of involved performance charts. In this paper, the related performance criteria are good-conditioning workspace (GCW), global conditioning index (GCI) and global stiffness index (GSI). As an applying example, a design result of the parallel manipulator with a desired task workspace is presented. The results of the paper are very useful for the design and application of a parallel manipulator.