scholarly journals Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning

2021 ◽  
Vol 11 (24) ◽  
pp. 11712
Author(s):  
Michal Dobiš ◽  
Martin Dekan ◽  
Adam Sojka ◽  
Peter Beňo ◽  
František Duchoň

This paper presents novel extensions of the Stochastic Optimization Motion Planning (STOMP), which considers cartesian path constraints. It potentially has high usage in many autonomous applications with robotic arms, where preservation or minimization of tool-point rotation is required. The original STOMP algorithm is unable to use the cartesian path constraints in a trajectory generation because it works only in robot joint space. Therefore, the designed solution, described in this paper, extends the most important parts of the algorithm to take into account cartesian constraints. The new sampling noise generator generates trajectory samples in cartesian space, while the new cost function evaluates them and minimizes traversed distance and rotation change of the tool-point in the resulting trajectory. These improvements are verified with simple experiments and the solution is compared with the original STOMP. Results of the experiments show that the implementation satisfies the cartesian constraints requirements.

Author(s):  
Mostafa Bagheri ◽  
Peiman Naseradinmousavi ◽  
Rasha Morsi

In this paper, we present a novel nonlinear analytical coupled trajectory optimization of a 7-DOF Baxter manipulator validated through experimental work utilizing global optimization tools. The robotic manipulators used in network-based applications of industrial units and even homes, for disabled patients, spend significant lumped amount of energy and therefore, optimal trajectories need to be generated to address efficiency issues. We here examine both heuristic (Genetics) and gradient based (GlobalSearch) algorithms for a novel approach of “S-Shaped” trajectory (unlike conventional polynomials), to avoid being trapped in several possible local minima along with yielding minimal computational cost, enforcing operational time and torque saturation constraints. The global schemes are utilized in minimizing the lumped amount of energy consumed in a nominal path given in the collision-free joint space except an impact between the robot’s end effector and a target object for the nominal operation. Note that such robots are typically operated for thousands of cycles resulting in a considerable cost of operation. Due to the expected computational cost of such global optimization algorithms, step size analysis is carried out to minimize both the computational cost (iteration) and possibly cost function by finding an optimal step size. Global design sensitivity analysis is also performed to examine the effects of changes of optimization variables on the cost function defined.


Author(s):  
Khaled S. Hatamleh ◽  
Mohammad Al-Shabi ◽  
Qais A. Khasawneh ◽  
Mohammad Abo Al-Asal

Industrial robotic arms are widely used nowadays. Accuracy and efficiency that fulfill user’s requirements are achieved through robust controller. This paper investigates dynamics modeling and control of a four DOF (PRRR) robot that is dedicated to perform a Pick-and-Place move of a certain product. The arm is undergoing manufacturing process. Forward and inverse kinematics solutions are introduced to solve the joint space trajectories associated with the desired End Effector (EE) Cartesian space path. The performance of two controllers under the presence of model uncertainties is inspected through a simulation study; Non-Linear Feedback Control (NLFC) and Sliding Mode Control (SMC) are designed and tested over the required joint space trajectories and Cartesian space path. Results showed that NLFC achieved better results than SMC in terms of RMSE when model uncertainties were absent. However, when model uncertainties were introduced, SMC performance was more robust than NLFC. Simulation results are very encouraging towards using the SMC over the actual robotic arm.


2020 ◽  
Vol 39 (8) ◽  
pp. 983-1001
Author(s):  
Takayuki Osa

Existing motion planning methods often have two drawbacks: (1) goal configurations need to be specified by a user, and (2) only a single solution is generated under a given condition. In practice, multiple possible goal configurations exist to achieve a task. Although the choice of the goal configuration significantly affects the quality of the resulting trajectory, it is not trivial for a user to specify the optimal goal configuration. In addition, the objective function used in the trajectory optimization is often non-convex, and it can have multiple solutions that achieve comparable costs. In this study, we propose a framework that determines multiple trajectories that correspond to the different modes of the cost function. We reduce the problem of identifying the modes of the cost function to that of estimating the density induced by a distribution based on the cost function. The proposed framework enables users to select a preferable solution from multiple candidate trajectories, thereby making it easier to tune the cost function and obtain a satisfactory solution. We evaluated our proposed method with motion planning tasks in 2D and 3D space. Our experiments show that the proposed algorithm is capable of determining multiple solutions for those tasks.


2021 ◽  
Vol 102 (2) ◽  
Author(s):  
Sascha Kaden ◽  
Ulrike Thomas

AbstractA major task in motion planning is to find paths that have a high ability to react to external influences while ensuring a collision-free operation at any time. This flexibility is even more important in human-robot collaboration since unforeseen events can occur anytime. Such ability can be described as mobility, which is composed of two characteristics. First, the ability to manipulate, and second, the distance to joint limits. This mobility needs to be optimized while generating collision-free motions so that there is always the flexibility of the robot to evade dynamic obstacles in the future execution of generated paths. For this purpose, we present a Rapidly-exploring Random Tree (RRT), which applies additional costs and sampling methods to increase mobility. Additionally, we present two methods for the optimization of a generated path. Our first approach utilizes the built-in capabilities of the RRT*. The second method optimize the path with the stochastic trajectory optimization for motion planning (STOMP) approach with Gaussian Mixture Models. Moreover, we evaluate the algorithms in complex simulation and real environments and demonstrate an enhancement of mobility.


2017 ◽  
Vol 14 (04) ◽  
pp. 1750005 ◽  
Author(s):  
Hongcheng Xu ◽  
Xilun Ding

Human-likeness of an anthropomorphic arm is mainly exhibited by the main arm consisting of shoulder and elbow joints as well as upper arm and forearm. In this paper, we focus on a 4-DOF anthropomorphic arm and propose a novel approach to generate human-like motion, considering human physiological and psychological factors. According to the features of musculoskeletal system of human upper limb, the 3-DOF shoulder joint is simplified with two rotations: one is about a fixed axis through shoulder center and the other is about the axis of humerus. In addition with elbow joint and a special rotation about the shoulder-wrist line, they constitute the basic motion primitives (MPs) which are macroscopic compared with the joint space but microscopic compared with the Cartesian space. Therefore, planning motion based on MPs combines intuitiveness and flexibility. Two basic kinds of motion planning, point-to-point motion planning and linear trajectory planning, are conducted. The different psychological concerns, i.e., predicting the next step of configuration and relieving the brain burden, are taken into account. Simulations are performed to verify the feasibility and validation of the proposed approach. Comparisons between simulation results with the proposed method and that with traditional joint and Cartesian space methods prove the improvement in human-likeness.


2021 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Khawaja Fahad Iqbal ◽  
Akira Kanazawa ◽  
Silvia Romana Ottaviani ◽  
Jun Kinugawa ◽  
Kazuhiro Kosuge

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Masahiro Inagawa ◽  
Toshinobu Takei ◽  
Etsujiro Imanishi

AbstractMany cooking robots have been developed in response to the increasing demand for such robots. However, most existing robots must be programmed according to specific recipes to enable cooking using robotic arms, which requires considerable time and expertise. Therefore, this paper proposes a method to allow a robot to cook by analyzing recipes available on the internet, without any recipe-specific programming. The proposed method can be used to plan robot motion based on the analysis of the cooking procedure for a recipe. We developed a cooking robot to execute the proposed method and evaluated the effectiveness of this approach by analyzing 50 recipes. More than 25 recipes could be cooked using the proposed approach.


2015 ◽  
Vol 31 (2) ◽  
pp. 415-424 ◽  
Author(s):  
Takahiro Kagawa ◽  
Hironori Ishikawa ◽  
Takayuki Kato ◽  
ChangHyun Sung ◽  
Yoji Uno

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.


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