Collision free trajectory planning for hybrid manipulators

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped by providing three converging options named as fast, medium, and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using six sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options.

Inverse and forward kinematics and workspace analysis of a novel 5-DOF (3T2R) parallel–serial (hybrid) manipulator

The proposed study provides a solution of the inverse and forward kinematic problems and workspace analysis for a five-degree-of-freedom parallel–serial manipulator, in which the parallel kinematic chain is made in the form of a tripod and the serial kinematic chain is made in the form of two carriages displaced in perpendicular directions. The proposed manipulator allows to realize five independent movements—three translations and two rotations motion pattern (3T2R). Analytical relationships between the coordinates of the end-effector and five controlled movements provided by manipulator’s drives (generalized coordinates) were determined. The approach of reachable workspace calculation was defined with respect to available design constraints of the manipulator based on the obtained algorithms of the inverse and forward kinematics. Case studies are considered based on the obtained algorithms of inverse and forward kinematics. For the inverse kinematic problem, the solution is obtained in accordance with the given laws of position and orientation change of the end-effector, corresponding to the motion along a spiral-helical trajectory. For the forward kinematic problem, various assemblies of the manipulator are obtained at the same given values of the generalized coordinates. An example of reachable workspace designing finalizes the proposed study. Dimensions and extreme values of the end-effector orientation angles are calculated.

From a 3D Passive Biped Walker to a 3D Passivity-Based Controlled Robot

Asymptotically stable control of biped robots, especially based on reproducing passive periodic motions, have become of interest nowadays. In this paper, firstly, a three-dimensional (3D) stable passive biped walker which is a compass gait one with flat feet, compliant ankles and particular arrangement of moments of inertia has been presented. Then, a passivity-based control of the related biped robot based on elaborating 3D form of potential energy shaping method has been applied. In other words, by adding minimal actuations to the aforementioned passive walker, its passive periodic gait that belongs to a particular slope has been reproduced on any arbitrary surface such as the level ground. Simulation results support the effectiveness of the proposed approach.

Path planning for minimally-invasive knee surgery using a hybrid optimization procedure

The aim of this study was to develop a procedure for medical tool path planning in minimally-invasive knee surgery. The collision-free paths for the tool were obtained using the control locations method with a hybrid optimization strategy. The tool and knee elements were described with surface meshes. The knee model allowed for bones displacement and variable incision size and location. The proposed procedure was proven to be effective in path planning for minimally-invasive surgery. It can serve as a valuable aid in surgery planning and may also be used in systems for autonomous or semi-autonomous knee surgery.

Modified Levenberg–Marquardt Algorithm for Backpropagation Neural Network Training in Dynamic Model Identification of Mechanical Systems

For modeling a dynamic system in practice, it often faces the difficulty in improving the accuracy of the constructed analytical model, since some components of the dynamic model are often ignored deliberately due to the difficulty of identification. It is also unwise to apply the neural network to approximate the entire dynamic system as a black box, when the comprehensive knowledge of most components of the dynamics of a large system are available. This paper proposes a method that utilizes the backpropagation (BP) neural network to identify the unknown components of the dynamic system based on the experimental front-end inputs–outputs data of the entire system. It can avoid the difficulty in getting the direct training data for the unknown components, and brings great benefits in the practical application, since to get the front-end inputs–outputs data of the entire dynamic system is easier and cost-effective. In order to train such neural network for the unknown components of dynamics, a modified Levenberg–Marquardt algorithm, which can utilize the front-end inputs–outputs data of the entire dynamic system, has been developed in the paper. Three examples from different application points of view are presented in the paper, and the results show that the proposed modified Levenberg–Marquardt algorithm is efficient to train the neural network for the unknown components of the system based on the data of entire system. The constructed dynamics model, in which the unknown components are substituted by the neural network, can satisfy the requisite accuracy successfully in the computation.