In this paper, we study the kinematic effects of number of legs in 6-DOF UPS parallel manipulators. A group of 3-, 4-, and 6-legged mechanisms are evaluated in terms of the kinematic performance indices, workspace, singular configurations, and forward kinematic solutions. Results show that the optimum number of legs varies due to priorities in kinematic measures in different applications. The non-symmetric Wide-Open mechanism enjoys the largest workspace, while the well-known Gough–Stewart (3–3) platform retains the highest dexterity. Especially, the redundantly actuated 4-legged mechanism has several important advantages over its non-redundant counterparts and different architectures of Gough–Stewart platform. It has dramatically less singular configurations, a higher manipulability, and at the same time less sensitivity. It is also shown that the forward kinematic problem has 40, 16, and 1 solution(s), respectively for the 6-, 3-, and the 4-legged mechanisms. Superior capabilities of the 4-legged mechanism make it a perfect candidate to be used in more challenging 6-DOF applications in assembly, manufacturing, biomedical, and space technologies.

Mirage is a camera pose estimation method that analytically solves pose parameters in linear time for multi-camera systems. It utilizes a reference camera pose to calculate the pose by minimizing the 2D projection error between reference and actual pixel coordinates. Previously, Mirage has been successfully applied to trajectory tracking (visual servoing) problem. In this study, a comprehensive evaluation of Mirage is performed by particularly focusing on the area of camera pose estimation. Experiments have been performed using simulated and real data on noisy and noise-free environments. The results are compared with the state-of-the-art techniques. Mirage outperforms other methods by generating fast and accurate results in all tested environments.

Trajectory tracking is one of the main control problems in the context of Wheeled Mobile Robots (WMRs). Control of underactuated systems has been focused by many researchers during past few years. In this paper, tracking control of a Tractor–Trailer Wheeled Mobile Robot (TTWMR) has been discussed. TTWMR includes a differential drive WMR towing a passive spherical wheeled trailer. Spherical wheels in contrast with standard wheels make the robot highly underactuated with severe non-linearities. Underactuation is due to the use of spherical wheeled trailer to increase robots' maneuverability and degrees of freedom. In fact, standard wheels are subjected to non-holonomic constraints due to pure rolling and non-slip conditions, which reduce robot maneuverability. In this paper, after introducing the robot, kinematics and kinetics models are obtained. Then, based on a physical intuition, a novel control algorithm is developed for the robot, i.e. Lyapunov-PID control algorithm. Subsequently, singularity avoidance of the proposed algorithm is discussed and the stability of the algorithm is analyzed. Finally, simulation and experimental results are presented which reveal the effectiveness of the proposed algorithm.

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.

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.

We present an analytical method for the concurrent calculation of optimal parallel compliant elements and frequency of reference trajectories for serial manipulators performing cyclic tasks. In this approach, we simultaneously shape and exploit the robot's natural dynamics by finding a set of compliant elements and task frequency that result in minimization of an energy-based cost function. The cost function is the integral of a weighted squared norm of the generalized forces. We prove that the generalized force needed for tracking the reference trajectory is a linear function of compliance coefficients and a quadratic function of task frequency. Therefore, the cost function is quadratic with respect to stiffness coefficients and quartic with respect to the task frequency. These properties lead to a well-posed optimization problem with a closed-form solution. Using three case studies, we elucidate the properties of our method.

Robotic hands use rolling contact to manipulate a grasped object to a desired location, even when the finger and the palm linkage mechanisms lack degrees of freedom. This paper presents a systematic approach to the forward and inverse kinematics of in-hand manipulation. The moving frame method in differential geometry is integrated into the product of exponential formula to establish a pure geometric framework of the kinematics of a robot hand. The forward and inverse kinematics of a multifingered hand are obtained in terms of the joint rates and contact trajectories. A two-fingered planar robot hand and a three-fingered spatial robot hand are used to demonstrate the proposed approach. The proposed formulation amounts to solving a univariate polynomial, providing an alternative to the existing ones that require numerical integration.

This paper focuses on the problem of robust time-optimal trajectory planning of robotic manipulators to track a given path under a probabilistic limited actuation, that is, the probability for the actuation to be limited is no less than a given bound κ. We give a general and practical method to reduce the probabilistic constraints to a set of deterministic constraints and show that the deterministic constraints are equivalent to a set of linear constraints under certain conditions. As a result, the original problem is reduced to a linear optimal control problem which can be solved with linear programming in polynomial time. In the case of κ = 1, the original problem is proved to be equivalent to the linear optimal control problem. Overall, a very general, practical, and efficient algorithm is given to solve the above problem and numerical simulation results are used to show the effectiveness of the method.