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.

In order to solve general kinematics modeling problems and numerical stability problems of numerical methods for spatial parallel linkage mechanisms, a general modeling method and its numerical solving algorithm is proposed. According to the need for avoiding direct singular configurations, valid joint variable space and valid forward kinematics solutions (VKSs) are defined. Taking numerical convergence near singular points into account, the pseudo-arc length homotopy continuation algorithm is given to solve the kinematics model. Finally as an example, the joint variable space of the general Stewart platform mechanism is analyzed, which is proved to be divided into subspaces by direct singular surfaces. And then, forward kinematics solutions of 200 testing points are solved separately using the pseudo-arc length homotopy continuation algorithm, the Newton homotopy continuation algorithm and the Newton–Raphson algorithm (NRA). Comparison of the results shows that the proposed method is convergent to the same solution branch with the initial configuration on all the testing points, while the other two algorithms skip to other solution branches on some near singular testing points.

Energy expenditure (EE) is an important criterion for design and control of biped walking robots. However, the cause-effect analyses enabled by total EE, which is lumped over a time duration and all system degrees-of-freedom (DOFs), are limited. In this study, robotic gait energetics is evaluated through a DOF-based instrumentation system designed for instantaneous evaluation of bidirectional current and applied voltage at each joint actuator. The instrumentation system includes a dual-module arrangement of buffers and attenuators, and accommodates and synchronizes the voltage and current measurements from multiple actuators. For illustrative purposes, this system is implemented at each DC servomotor in a biped robot, DARwIn-OP, to analyze the electrical EE rates for walking at various speeds. In addition, a DOF-based model of instantaneous human EE rate is employed to enable quantitative characterization of robotic walking EE relative to that of humans. The robot's instantaneous lower-body EE rates are consistent with its periodic walking cycle, and their relative trends between single and double support phases are analogous to those of humans. The robotic cost of transport (COT) curve as a function of normalized speed is also consistent with the human COT in terms of its convexity. Conversely, the contrasting distributions of EE throughout the robot and human DOFs and the robotic COT curve's considerably larger magnitudes, smaller speed ranges, and higher sensitivity to speed illustrate the energetic consequences of stable but inefficient static walking in the biped robot relative to the more efficient dynamic walking of humans. These energetic characteristics enable the identification of the joints and gait cycle phases associated with inefficiency in biped robotic gait, and reflect the noticeable differences in the system parameters (rigid and flat versus segmented feet) and gait control strategies (bent versus straight knees, instants of peak ankle actuator torques, static versus dynamic balance stability). The proposed general instrumentation provides a quantitative approach to benchmarking human gait as well as general guidelines for the development of energy-efficient walking robots.

We propose a new class of modular-robotic structures, intended to produce forces which scale with the number of modules. We adopt the concept of a spherical catom and extend it by a new connection type which is relatively strong but static. We examine analytically and numerically the mechanical properties of two collective-actuator designs. The simulations are based on the discrete element method (DEM), with friction and elastic deformations taken into account. One of the actuators is shown to generate forces proportional to its volume. This property seems necessary for building modular structures of useful strength and dimensions.

This paper presents a novel method for generating three-dimensional optimal trajectories for a vehicle or body that moves forward at a constant speed and steers in both horizontal and vertical directions. The vehicle's dynamics limit the body-frame pitch and yaw rates; additionally, the climb and decent angles of the vehicle are also bounded. Given the above constraints, the path planning problem is solved geometrically by building upon the two-dimensional Dubins curves and then Pontryagin's Maximum Principle is used to validate that the proposed solution lies within the family of candidate time-optimal trajectories. Finally, given the severe boundedness constraints on the vertical motion of the system, the robustness of the proposed path planning method is validated by naturally extending it to remain applicable to high-altitude final configurations.

This paper studies bifurcations in the solution structure of an optimal control problem for mobile robotic formation control. In particular, this paper studies a group of mobile robots operating in a two-dimensional environment. Each robot has a predefined initial state and final state and we compute an optimal path between the two states for every robot. The path is optimized with respect to two factors, the control effort and the deviation from a desired “formation,” and a bifurcation parameter gives the relative weight given to each factor. Using an asymptotic analysis, we show that for small values of the bifurcation parameter (corresponding to heavily weighting the control effort) a single unique solution is expected, and that as the bifurcation parameter becomes large (corresponding to heavily weighting maintaining the formation) a large number of solutions is expected. Between the asymptotic extremes, a numerical investigation indicates a solution bifurcation structure with a cascade of increasing numbers of solutions, reminiscent, but not the same as, period-doubling bifurcations leading to chaos in dynamical systems. Furthermore, we show that if the system is symmetric, the bifurcation structure possesses symmetries, and also present a symmetry-breaking example of a non-holonomic system. Knowledge and understanding of the existence and structure of bifurcations in the solutions of this type of formation control problem are important for robotics engineers because common optimization approaches based on gradient-descent are only likely to converge to the single nearest solution, and a more global study provides a deeper and more comprehensive understanding of the nature of this important problem in robotics.

Path planning can be difficult and time consuming for inchworm robots especially when operating in complex 3D environments such as steel bridges. Confined areas may prevent a robot from extensively searching the environment by limiting its mobility. An approach for real-time path planning is presented. This approach first uses the concept of line-of-sight (LoS) to find waypoints from the start pose to the end node. It then plans smooth, collision-free motion for a robot to move between waypoints using a 3D-F2 algorithm. Extensive simulations and experiments are conducted in 2D and 3D scenarios to verify the approach.

This paper introduces the methodology of the dimensional synthesis for a 3UPS-PRU parallel robot. The dimensional synthesis of the 3UPS-PRU parallel robot is proposed considering the maximum input velocity of actuating joints as the objective function and constraints on the installation dimension, robot dimension, joint rotation angle and interference. The objective of the dimensional synthesis is to minimize the maximum input velocity of actuating joints when the moving platform translates along the z-axis in the maximum linear velocity and rotates about an arbitrary axis in the maximum angular velocity in the desired workspace. The constraint on the robot dimension is included in the dimensional synthesis of the 3UPS-PRU parallel robot when pursuing the kinematic property to meet the miniaturization principle with the reduced building cost. An example of the dimensional synthesis of a 3UPS-PRU parallel robot is presented with the maximum linear velocity and angular velocity required for the moving platform in the desired workspace.

When the end-effector of a robotic arm grasps different payload masses, the output of joint motion will vary. By using a model reference adaptive control approach, the payload variation effect can be solved. This paper describes the design for a hybrid controller for serial robotic manipulators by combining a PID controller and a model reference adaptive controller (MRAC) in order to further improve the accuracy and joint convergence speed performance. The convergence performance of the PID controller, the MRAC and the PID+MRAC hybrid controller for 1-DOF, 2-DOF and subsequently 3-DOF manipulators is compared. The comparison results show that the convergence speed and its performance for the MRAC and the PID+ MRAC controllers is better than that of the PID controller, and the convergence performance for the hybrid control is better than that of the MRAC control.

This paper presents a formal framework for achieving multi-contact bipedal robotic walking, and realizes this methodology experimentally on two robotic platforms: AMBER2 and Assume The Robot Is A Sphere (ATRIAS). Inspired by the key feature encoded in human walking—multi-contact behavior—this approach begins with the analysis of human locomotion and uses it to motivate the construction of a hybrid system model representing a multi-contact robotic walking gait. Human-inspired outputs are extracted from reference locomotion data to characterize the human model or the spring-loaded invert pendulum (SLIP) model, and then employed to develop the human-inspired control and an optimization problem that yields stable multi-domain walking. Through a trajectory reconstruction strategy motivated by the process that generates the walking gait, the mathematical constructions are successfully translated to the two physical robots experimentally.

We present a real time 3D SLAM system for texture-less scenes using only depth information provided by a low cost RGB-D sensor. The proposed method is based on a novel informative sampling scheme that extracts points carrying the most useful 3D information for registration. The aim of the proposed sampling technique is to informatively sample a point cloud into a subset of points based on their 3D information. The flatness of a point is measured by applying a rank order statistics based robust segmentation method to surface normals in its local vicinity. The extracted keypoints from sequential frames are then matched and a rank order statistics based robust estimator is employed to refine the matches and estimate a rigid-body transformation between the frames. Experimental evaluations show that the proposed keypoint extraction method is highly repeatable and outperforms the state of the art methods in terms of accuracy and repeatability. We show that the performance of the registration algorithm is also comparable to other well-known methods in texture-less environments.

This paper deals with the stiffness modeling, analysis and comparison of a Biglide parallel grinder with two alternative modular parallelograms. It turns out that the Cartesian stiffness matrix of the manipulator has the property that it can be decoupled into two homogeneous matrices, corresponding to the translational and rotational aspects, through which the principal stiffnesses and the associated directions are identified by means of the eigenvalue problem, allowing the evaluation of the translational and rotational stiffness of the manipulator either at a given pose or the overall workspace. The stiffness comparison of the two alternative Biglide machines reveals the (dis)advantages of the two different spatial modular parallelograms.

This paper presents a novel control algorithm for electrically driven robot manipulators. The proposed control law is simple and model-free based on the voltage control strategy with the decentralized structure and only joint position feedback. It works for both repetitive and non-repetitive tasks. Recently, some control approaches based on the uncertainty estimation using the Fourier series have been presented. However, the proper value for the fundamental period duration has been left as an open problem. This paper addresses this issue and intuitively shows that in order to perform repetitive tasks; the least common multiple (LCM) of fundamental period durations of the desired trajectories of the joints is a proper value for the fundamental period duration of the Fourier series expansion. Selecting the LCM results in the least tracking error. Moreover, the truncation error is compensated by the proposed control law to make the tracking error as small as possible. Adaptation laws for determining the Fourier series coefficients are derived according to the stability analysis. The case study is an SCARA robot manipulator driven by permanent magnet DC motors. Simulation results and comparisons with a voltage-based controller using adaptive neuro-fuzzy systems show the effectiveness of the proposed control approach in tracking various periodic trajectories. Moreover, the experimental results on a real SCARA robot manipulator verify the successful practical implementation of the proposed controller.

In this paper, a control approach called Artificial Neural Tissue (ANT) is applied to multirobot excavation for lunar base preparation tasks including clearing landing pads and burying of habitat modules. We show for the first time, a team of autonomous robots excavating a terrain to match a given three-dimensional (3D) blueprint. Constructing mounds around landing pads will provide physical shielding from debris during launch/landing. Burying a human habitat modules under 0.5 m of lunar regolith is expected to provide both radiation shielding and maintain temperatures of −25 °C. This minimizes base life-support complexity and reduces launch mass. ANT is compelling for a lunar mission because it does not require a team of astronauts for excavation and it requires minimal supervision. The robot teams are shown to autonomously interpret blueprints, excavate and prepare sites for a lunar base. Because little pre-programmed knowledge is provided, the controllers discover creative techniques. ANT evolves techniques such as slot-dozing that would otherwise require excavation experts. This is critical in making an excavation mission feasible when it is prohibitively expensive to send astronauts. The controllers evolve elaborate negotiation behaviors to work in close quarters. These and other techniques such as concurrent evolution of the controller and team size are shown to tackle problem of antagonism, when too many robots interfere reducing the overall efficiency or worse, resulting in gridlock. Although many challenges remain with this technology, our work shows a compelling pathway for field testing this approach.

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.

Light-weight manipulators are used in industrial tasks mounted on mobile platforms to improve flexibility. However, such mountings introduce compliance affecting the tasks. This work deals with such scenarios by designing a controller that also takes into account compliant environments. The controller allows the tracking of a target force using the estimation of the environment stiffness (EKF) and the estimation of the base position (KF), compensating the robot base deformation. The closed-loop stability has been analyzed. Observers and the control law have been validated in experiments. An assembly task is considered with a standard industrial non-actuated mobile platform. Control laws with and without base compensation are compared.

We propose a novel class of unsupervised learning-based algorithms that extend the conditional restricted Boltzmann machine to predict, in real-time, a lower limb exoskeleton wearer's intended movement type and future trajectory. During training, our algorithm automatically clusters unlabeled exoskeletal measurement data into movement types. Our predictor then takes as input a short time series of measurements, and outputs in real-time both the movement type and the forward trajectory time series. Physical experiments with a prototype exoskeleton demonstrate that our method more accurately and stably predicts both movement type and the forward trajectory compared to existing methods.

The modules of parallel tool heads with 2R1T degrees of freedom (DOFs), i.e., two rotational DOFs and one translational DOF, have become so important in the field of machine tools that corresponding research studies have attracted extensive attention from both academia and industry. A 3-PUU (P represents a prismatic joint, U represents a universal joint) parallel mechanism with 2R1T DOFs is proposed in this paper, and a detailed discussion about its architecture, geometrical constraints, and mobility characteristics is presented. Furthermore, on the basis of its special geometrical constraint, we derive and explicitly express the parasitic motion of the 3-PUU mechanism. Then, the inverse kinematics problem, the Jacobian matrix calculation and the forward kinematics problem are also investigated. Finally, with a simplified dynamics model, the inverse dynamics analysis for the mechanism is carried out with the Principle of Virtual Work, and corresponding results are compared with that of the 3-PRS mechanism. The above analyses illustrate that the 3-PUU parallel mechanism has good dynamics features, which validates the feasibility of applying this mechanism as a tool head module.

This paper investigates the active vertical motion of biped systems and its significance to the balance of biped robots, which have been commonly neglected by the use of a well-known model called the Linear Inverted Pendulum Model. The feasible step location is theoretically estimated by considering the active vertical movement on a simple point mass model. Based on the estimation, we present two new strategies, namely the flexion strategy and the extension strategy, to enable biped robots to restore balance through active upward and downward motions. The analytical results demonstrate that the robot is able to recover from much larger disturbances using our proposed methods. Simulations of the simple point mass model validate our analysis. Besides, prototype controllers that incorporate our proposed strategies have also been implemented on a simulated humanoid robot. Numerical simulations on both the simple point mass model and the realistic humanoid model prove the effectiveness of proposed strategies.

Lateral-pendulum unicycle robot is a type of single-wheeled mobile robot which utilizes an inverted pendulum mounted laterally for its stabilization and control. Even though this concept was first mentioned in the 1980s, it has not been sufficiently explored especially from the theoretical point of view. Therefore, this robot represents a niche which is still open for more thorough research. This paper presents four contributions to the research of this particular robot. First, the complete model of the robot dynamics is derived and this model can facilitate more accurate study of the robot's static and dynamic behaviors. Secondly, two important constants namely (1) lateral-statics boundary and (2) lateral-statics constant, which are useful for the lateral stabilization, are derived and analyzed. Thirdly, turning constant, required for the control of the turning motion, is derived and used to study the achievable path curvature in maneuvering control. Lastly, dynamics couplings during steady linear motion and steady circular motion are studied and the necessity of integral action in the lateral controller for circular motion is shown and explained. Throughout this paper, numerical simulation is used intensively and two case studies are presented to illustrate the concepts.