The paper considers the problem of motion planning and posture control of multiple n-link doubly nonholonomic mobile manipulators in an obstacle-cluttered and bounded workspace. The workspace is constrained with the existence of an arbitrary number of fixed obstacles (disks, rods and curves), artificial obstacles and moving obstacles. The coordination of multiple n-link doubly nonholonomic mobile manipulators subjected to such constraints becomes therefore a challenging navigational and steering problem that few papers have considered in the past. Our approach to developing the controllers, which are novel decentralized nonlinear acceleration controllers, is based on a Lyapunov control scheme that is not only intuitively understandable but also allows simple but rigorous development of the controllers. Via the scheme, we showed that the avoidance of all types of obstacles was possible, that the manipulators could reach a neighborhood of their goal and that their final orientation approximated the desired orientation. Computer simulations illustrate these results.

This paper presents an efficient approach for planning collision-free and dynamically feasible trajectories that enable a mobile robot to carry out tasks specified as regular languages over workspace regions. A sampling-based tree search is conducted over the feasible motions and over an abstraction obtained by combining the automaton representing the regular language with a workspace decomposition. The abstraction is used to partition the motion tree into equivalence classes and estimate the feasibility of reaching accepting automaton states from these equivalence classes. The partition is continually refined to discover new ways to expand the search. Comparisons to related work show significant speedups.

In this paper, a method for the indirect solution of the optimal control problem (OCP) in the presence of pure state variable inequality constraints (SVICs) and mixed state-control inequality constraints (SCIC), without a need for a close initial guess is presented. In the proposed method, using the finite difference approximation (FDA), the pure SVICs are converted to SCIC. Here, the distance of the constraint function to the feasibility bounds of the constraint is computed in every situation and the control signal is chosen appropriately to facilitate the constraint stays safe. In this method, prior knowledge of the numbers and sequences of activation times is not required. So, it can be simply implemented in continuous boundary value problem (BVP) solvers. The proposed method simply applies the SVICs and since the constraint is directly applied on the control signal, it improves the convergence. On the other hand, because of the convergence problem in the indirect solution of OCP, the simple homotopy continuation method (HCM) is used to overcome the initial guess problem by deploying a secondary OCP for which the initial guess can be zero. The proposed approach is applied on a few comprehensive problems in the presence of different constraints. Simulations are compared with the direct solution of the OCP to confirm the accuracy and with the penalty function method and the sequential constraint-free OCP to confirm the convergence. The results indicate that the FDA method for handling the constraints along with the HCM is easy to apply with acceptable accuracy and convergence, even for highly nonlinear problems in robotic systems such as the constrained time optimal control of a two-link manipulator (TLM) and a three-link common industrial robot.

An advanced sonar morphology-based posterior approach (SMP) to building an occupancy grid map for a mobile robot is proposed in this study. It is very important for a mobile robot to find its surrounding saliencies and to localize itself for indoor navigation. Ultrasonic sensors are of great practical value in building environmental maps and for autonomous operation. However, grid maps constructed by ultrasonic sensors cannot typically form a realistic representation of a given environment due to incorrect sonar measurements caused by specular reflection and wide beam width. The sonar sensor model proposed in this study, in which the negative effect of incorrect sonar measurements is minimized by geometric association with sonar footprints, is adopted to build a high-quality grid map. Experimental results and evaluations in home and corridor environments demonstrate the validity of the proposed methods.

An efficient obstacle detection technique is required so that navigating robots can avoid obstacles and potential hazards. This task is usually simplified by relying on structural patterns. However, obstacle detection constitutes a challenging problem in unstructured unknown environments, where such patterns may not exist. Talukder et al. (2002, IEEE Intelligent Vehicles Symposium, pp. 610–618.) successfully derived a method to deal with such environments. Nevertheless, the method has a high computational cost and researchers that employ it usually rely on approximations to achieve real-time. We hypothesize that by using a graphics processing unit (GPU), the computing time of the method can be significantly reduced. Throughout the implementation process, we developed a general framework for processing dynamically-sized sliding windows on a GPU. The framework can be applied to other problems that require similar computation. Experiments were performed with a stereo camera and an RGB-D sensor, where the GPU implementations were compared to multi-core and single-core CPU implementations. The results show a significant gain in the computational performance, i.e. in a particular instance, a GPU implementation is almost 90 times faster than a single-core one.

A hyper-redundant manipulator is made by mounting the serial and/or parallel mechanisms on top of each other as modules. In discrete actuation, the actuation amounts are a limited number of certain values. It is not feasible to solve the kinematic analysis problems of discretely actuated hyper-redundant manipulators (DAHMs) by using the common methods, which are used for continuous actuated manipulators. In this paper, a new method is proposed to solve the trajectory tracking problem in a static prescribed obstacle field. To date, this problem has not been considered in the literature. The removing first collision (RFC) method, which is originally proposed for solving the inverse kinematic problems in the obstacle fields was modified and used to solve the motion planning problem. For verification, the numerical results of the proposed method were compared with the results of the genetic algorithm (GA) method. Furthermore, a novel DAHM designed and implemented by the authors is introduced.

The main innovation of this paper is determining the dynamic load carrying capacity (DLCC) of a flexible joint manipulator (FJM) using a closed form nonlinear optimal control approach. The proposed method is compared with two closed loop nonlinear methods that are usually applied to robotic systems. As a new idea, DLCC of the manipulator is considered as a criterion to compare how well controllers perform point to point mission for the FJMs. The proposed method is compared with feedback linearization (FL) and robust sliding mode control (SMC) methods to show better performance of proposed nonlinear optimal control approach. An optimal controller is designed by solving a nonlinear partial differential equation named the Hamilton–Jacobi–Bellman (HJB) equation. This equation is complicated to solve exactly for complex dynamics so it is solved numerically using an iterative approximation combined with the Galerkin method. In the FL method, angular position, velocity, acceleration and jerk of links are considered as new states to linearize the dynamic equations. In the case of SMC, the dynamic equations of manipulator are changed to the standard form then the Slotine method is used to design the sliding mode controller. Two simulations are performed for a planar and a spatial Puma manipulator and performances of controllers are compared. Finally an experimental test is done on 6R manipulator and the Stereo vision method is used to determine the position and orientation of the end-effector.

A new approach to flow measurement and visualisation in fluid dynamics based on a group of perceptive communicating capsules has been developed. Experiments were carried out with fluid-mobilised and stationary capsules deployed in a fluid flow test rig (raceway pond). Each capsule contains a microcontroller, battery, infra-red and visible LEDs and other electronics. Using optical communications, capsules can record encounters with one another. From the resulting interaction patterns, fluid flow speed and path-frequency measurements were obtained. Additionally, the capsules have shown the capacity for distributed sensing, and their streaklines provide a valuable means of external visualisation.

With regard to the small basin of attraction of the passive limit cycles, it is important to start from a proper initial condition for stable walking. The present study investigates the passive dynamic behaviors of two-dimensional bipedal walkers of a compass gait model with different foot shapes. In order to find proper initial conditions for stable and unstable period-one gait limit cycles, a method based on solving the nonlinear equations of motion is presented as a boundary value problem (BVP). An initial guess is required to solve the related BVP that is obtained by solving an initial value problem (IVP). For parametric analysis purposes, a continuation method is applied. Simulation results reveal two, period-one gait cycles and the effects of parameters variation for all models.

Legged locomotion systems have been effective in numerous robotic missions, and such locomotion is especially useful for providing better mobility over irregular landscapes. However, locomotion capabilities of robots are often constrained by a limited range of gaits and the associated energy efficiency. This paper presents the design of a novel reconfigurable Klann mechanism capable of producing a variety of useful gait cycles. Such an approach opens up new research avenues, opportunities and applications. The position analysis problem that arises when dealing with reconfigurable Klann mechanisms was solved here using a bilateration method, which is a distance-based formulation. By changing the linkage configurations, our aim was to generate a set of useful gaits for a legged robotic platform. In this study, five gait patterns of interest were identified, analysed and discussed that validate the feasibility of our approach and considerably extend the capabilities of the original design.

Complementary filtering is a frequency based method used to design data processing algorithms exploiting signals with complementary spectra. The technique is mostly used in sensor fusion architectures, but it may also be effective in the design of state estimators. In spite of its potential in several areas of robotics, the complementary filtering paradigm is poorly used as compared to alternative time domain methods. The first part of the paper aims at reviewing the basics of complementary filtering in sensor data processing and linear systems state estimation. The second part of the paper describes how to exploit the main ideas of complementary filtering to design a depth controller for an actuator redundant autonomous underwater vehicle (AUV). Unlike with alternative state space methods commonly used to address the design of control solutions for actuator redundant systems, the proposed approach allows to fully exploit the knowledge of frequency characteristics of actuators. Simulation results are reported to demonstrate the effectiveness of the proposed solution.

This paper presents a novel kinematic approach for controlling the end-effector of a continuum robot for in-situ repair/inspection in restricted and hazardous environments. Forward and inverse kinematic (IK) models have been developed to control the last segment of the continuum robot for performing multi-axis processing tasks using the last six Degrees of Freedom (DoF). The forward kinematics (FK) is proposed using a combination of Euler angle representation and homogeneous matrices. Due to the redundancy of the system, different constraints are proposed to solve the IK for different cases; therefore, the IK model is solved for bending and direction angles between (−π/2 to +π/2) radians. In addition, a novel method to calculate the Jacobian matrix is proposed for this type of hyper-redundant kinematics. The error between the results calculated using the proposed Jacobian algorithm and using the partial derivative equations of the FK map (with respect to linear and angular velocity) is evaluated. The error between the two models is found to be insignificant, thus, the Jacobian is validated as a method of calculating the IK for six DoF.