Motion Planning for Industrial Mobile Robots With Closed-Loop Stability Enhanced Prediction

2019 ◽  
Author(s):  
Jessica Leu ◽  
Masayoshi Tomizuka
Keyword(s):  
Motion Planning ◽  
Closed Loop ◽  
Dynamic Environments ◽  
Open Loop ◽  
Human Robot Interaction ◽  
Planning Problem ◽  
Prediction Horizon ◽  
Robot Systems ◽  
Planning Algorithm ◽  
Loop Stability

Abstract Real-time, safe, and stable motion planning in co-robot systems involving dynamic human robot interaction (HRI) remains challenging due to the time varying nature of the problem. One of the biggest challenges is to guarantee closed-loop stability of the planning algorithm in dynamic environments. Typically, this can be addressed if there exists a perfect predictor that precisely predicts the future motions of the obstacles. Unfortunately, a perfect predictor is not possible to achieve. In HRI environments in this paper, human workers and other robots are the obstacles to the ego robot. We discuss necessary conditions for the closed-loop stability of a planning problem using the framework of model predictive control (MPC). It is concluded that the predictor needs to be able to detect the obstacles’ movement mode change within a time delay allowance and the MPC needs to have a sufficient prediction horizon and a proper cost function. These allow MPC to have an uncertainty tolerance for closed-loop stability, and still avoid collision when the obstacles’ movement is not within the tolerance. Also, the closed-loop performance is investigated using a notion of M-convergence, which guarantees finite local convergence (at least M steps ahead) of the open-loop trajectories toward the closed-loop trajectory. With this notion, we verify the performance of the proposed MPC with stability enhanced prediction through simulations and experiments. With the proposed method, the robot can better deal with dynamic environments and the closed-loop cost is reduced.

High-DOF Robots in Dynamic Environments Using Incremental Trajectory Optimization

2014 ◽  
Vol 11 (02) ◽  
pp. 1441001 ◽  
Author(s):  
Chonhyon Park ◽  
Jia Pan ◽  
Dinesh Manocha
Keyword(s):  
Motion Planning ◽  
Trajectory Optimization ◽  
A Priori ◽  
Coupled System ◽  
Dynamic Environments ◽  
Planning Problem ◽  
Simulated Environments ◽  
Tightly Coupled ◽  
Planning Algorithm ◽  
Low Dimensional

We present a novel optimization-based motion planning algorithm for high degree-of-freedom (DOF) robots in dynamic environments. Our approach decomposes the high-dimensional motion planning problem into a sequence of low-dimensional sub-problems. We compute collision-free and smooth paths using optimization-based planning and trajectory perturbation for each sub-problem. The overall algorithm does not require a priori knowledge about global motion or trajectories of dynamic obstacles. Rather, we compute a conservative local bound on the position or trajectory of each obstacle over a short time and use the bound to incrementally compute a collision-free trajectory for the robot. The high-DOF robot is treated as a tightly coupled system, and we incrementally use constrained coordination to plan its motion. We highlight the performance of our planner in simulated environments on robots with tens of DOFs.

Efficient Human Motion Planning Using Recursive Dynamics and Optimal Control Techniques

1999 ◽  
Author(s):  
Janzen Lo ◽  
Dimitris Metaxas
Keyword(s):  
Optimal Control ◽  
Motion Planning ◽  
Closed Loop ◽  
High Efficiency ◽  
Human Motion ◽  
Lagrangian Formulation ◽  
Planning Problem ◽  
Tree Structures ◽  
Recursive Dynamics ◽  
Minimum Torque

Abstract We present an efficient optimal control based approach to simulate dynamically correct human movements. We model virtual humans as a kinematic chain consisting of serial, closed-loop, and tree-structures. To overcome the complexity limitations of the classical Lagrangian formulation and to include knowledge from biomechanical studies, we have developed a minimum-torque motion planning method. This new method is based on the use of optimal control theory within a recursive dynamics framework. Our dynamic motion planning methodology achieves high efficiency regardless of the figure topology. As opposed to a Lagrangian formulation, it obviates the need for the reformulation of the dynamic equations for different structured articulated figures. We use a quasi-Newton method based nonlinear programming technique to solve our minimum torque-based human motion planning problem. This method achieves superlinear convergence. We use the screw theoretical method to compute analytically the necessary gradient of the motion and force. This provides a better conditioned optimization computation and allows the robust and efficient implementation of our method. Cubic spline functions have been used to make the search space for an optimal solution finite. We demonstrate the efficacy of our proposed method based on a variety of human motion tasks involving open and closed loop kinematic chains. Our models are built using parameters chosen from an anthropomorphic database. The results demonstrate that our approach generates natural looking and physically correct human motions.

scholarly journals A sampling-based optimized algorithm for task-constrained motion planning

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984737 ◽  
Author(s):  
Kai Mi ◽  
Haojian Zhang ◽  
Jun Zheng ◽  
Jianhua Hu ◽  
Dengxiang Zhuang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Sampling Methods ◽  
Tracking Error ◽  
Joint Space ◽  
Planning Problem ◽  
Redundant Manipulators ◽  
Task Space ◽  
Constrained Motion ◽  
Smooth Path ◽  
Planning Algorithm

We consider a motion planning problem with task space constraints in a complex environment for redundant manipulators. For this problem, we propose a motion planning algorithm that combines kinematics control with rapidly exploring random sampling methods. Meanwhile, we introduce an optimization structure similar to dynamic programming into the algorithm. The proposed algorithm can generate an asymptotically optimized smooth path in joint space, which continuously satisfies task space constraints and avoids obstacles. We have confirmed that the proposed algorithm is probabilistically complete and asymptotically optimized. Finally, we conduct multiple experiments with path length and tracking error as optimization targets and the planning results reflect the optimization effect of the algorithm.

Stability boundary analysis of highly flexible aircraft with control saturation and structural flexibility

2020 ◽  
Vol 234 (6) ◽  
pp. 1195-1208
Author(s):  
Liang Xu ◽  
Yuping Lu ◽  
Boyi Chen ◽  
Haidong Shen ◽  
Zhen He
Keyword(s):  
Closed Loop ◽  
Stability Boundary ◽  
Open Loop ◽  
Loop System ◽  
Control Signal ◽  
Stable Region ◽  
Structural Flexibility ◽  
Flexible Aircraft ◽  
Control Saturation ◽  
Loop Stability

In this work, a method has been presented to analyze the influence of control saturation and structural flexibility on the stable radius of highly flexible aircraft. A dynamic model of aircraft is constructed followed by the analysis of kinetic characteristics. In this paper, the closed-loop stability boundary of highly flexible aircraft with open-loop instability is studied. The amplitude limit and bandwidth limit of the control signal are considered in the closed-loop stability boundary calculation. Our analysis shows that the boundary is related to the left eigenvector corresponding to the unstable poles and the amplitude constraint of the control signals. Stability of the boundary of feedback control system further reduces the limitation of the bandwidth of actuators. Focused on the phugoid instability of highly flexible aircraft, computational formulation of the closed-loop stable boundary is achieved. The Monte Carlo analysis has been employed to validate the stable region, under the LQR controller. Both the theory and simulations have nice correlations with each other which verify the stability of the closed-loop system, restricted by the open-loop system, and the influence of control signal bandwidth constraints.

scholarly journals Inverse kinematics-based motion planning for dual-arm robot with orientation constraints

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983685 ◽  
Author(s):  
Jiangping Wang ◽  
Shirong Liu ◽  
Botao Zhang ◽  
Changbin Yu
Keyword(s):  
Motion Planning ◽  
Joint Space ◽  
Inverse Kinematic ◽  
Planning Problem ◽  
Constraint Manifold ◽  
Wide Range ◽  
Planning Algorithm ◽  
Motion Paths ◽  
Dual Arm Robot ◽  
Dual Arm

This article proposes an efficient and probabilistic complete planning algorithm to address motion planning problem involving orientation constraints for decoupled dual-arm robots. The algorithm is to combine sampling-based planning method with analytical inverse kinematic calculation, which randomly samples constraint-satisfying configurations on the constraint manifold using the analytical inverse kinematic solver and incrementally connects them to the motion paths in joint space. As the analytical inverse kinematic solver is applied to calculate constraint-satisfying joint configurations, the proposed algorithm is characterized by its efficiency and accuracy. We have demonstrated the effectiveness of our approach on the Willow Garage’s PR2 simulation platform by generating trajectory across a wide range of orientation-constrained scenarios for dual-arm manipulation.

scholarly journals Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling

2020 ◽  
Vol 10 (9) ◽  
pp. 3180 ◽  
Author(s):  
Dongfang Dang ◽  
Feng Gao ◽  
Qiuxia Hu
Keyword(s):  
Motion Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamical Model ◽  
Planning Problem ◽  
Original Design ◽  
Automatic Driving ◽  
Wide Range ◽  
Planning Algorithm ◽  
Dynamical Coupling

Vehicles are highly coupled and multi-degree nonlinear systems. The establishment of an appropriate vehicle dynamical model is the basis of motion planning for autonomous vehicles. With the development of autonomous vehicles from L2 to L3 and beyond, the automatic driving system is required to make decisions and plans in a wide range of speeds and on bends with large curvature. In order to make precise and high-quality control maneuvers, it is important to account for the effects of dynamical coupling in these working conditions. In this paper, a new single-coupled dynamical model (SDM) is proposed to deal with the various dynamical coupling effects by identifying and simplifying the complicated one. An autonomous vehicle motion planning problem is then formulated using the nonlinear model predictive control theory (NMPC) with the SDM constraint (NMPC-SDM). We validated the NMPC-SDM with hardware-in-the-loop (HIL) experiments to evaluate improvements to control performance by comparing with the planners original design, using the kinematic and single-track models. The comparative results show the superiority of the proposed motion planning algorithm in improving the maneuverability and tracking performance.

Terminal Area Energy Management Trajectory Planning for an Unpowered Reusable Launch Vehicle with Gliding Limitations

2013 ◽  
Vol 446-447 ◽  
pp. 611-615
Author(s):  
Min Zhou ◽  
Jun Zhou ◽  
Jian Guo Guo
Keyword(s):  
Trajectory Planning ◽  
Closed Loop ◽  
Dynamic Pressure ◽  
Open Loop ◽  
Bank Angle ◽  
Reference Velocity ◽  
Reusable Launch Vehicle ◽  
Planning Algorithm ◽  
Constant Maximum ◽  
Terminal Area

RLVs' gliding capability, determined by its maximum dive and maximum range, provided a significant restriction in TAEM trajectory planning in this paper. The maximum-dive trajectory was generated based on Eq.(3) for a constant maximum dynamic pressure. In the guidance, it was optimized to be Eq.(13) for the open-loop command of bank angle in HAC segment. The simplified closed-loop command of angle of attack contained errors of altitude and path angle except the controlled velocity. Energy propagating as Eq.(8) calculated the reference velocity for the speed brake to track. Finally, an illustrative example was given to confirm the efficiency of the trajectory planning algorithm and optimized command. The simulation results in Fig.2 and Fig.3 indicate the proposed trajectory planning algorithm and guidance method are useful for the gliding capability limited RLV's TAEM with initial deviations.

Frequency Domain Stability Criteria for Distributed Parameter Systems

1970 ◽  
Vol 92 (2) ◽  
pp. 377-384 ◽  
Author(s):  
H. C. Khatri
Keyword(s):  
Frequency Domain ◽  
Stability Criterion ◽  
Closed Loop ◽  
Linear Time ◽  
Distributed Parameter Systems ◽  
Open Loop ◽  
Distributed Parameter ◽  
Stability Criteria ◽  
Domain Stability ◽  
Loop Stability

For distributed parameter systems, open-loop stability in the sense of bounded outputs for bounded inputs, and closed-loop asymptotic stability are considered. Frequency domain stability criteria for open and closed-loop distributed parameter systems are given. The closed-loop stability criterion is similar to V. M. Popov’s stability criterion for lumped systems. The criteria are limited to those linear, time-invariant systems whose dynamics can be described by a transfer function which is the ratio of the multiple transform of the output to the multiple transform of the input. The input may or may not be distributed. An example is given to illustrate the applications of the stability criteria.

scholarly journals Motion Planning of Robot Manipulators for a Smoother Path Using a Twin Delayed Deep Deterministic Policy Gradient with Hindsight Experience Replay

2020 ◽  
Vol 10 (2) ◽  
pp. 575 ◽  
Author(s):  
MyeongSeop Kim ◽  
Dong-Ki Han ◽  
Jae-Han Park ◽  
Jung-Su Kim
Keyword(s):  
Motion Planning ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Robot Systems ◽  
Policy Gradient ◽  
Markov Decision ◽  
Experience Replay ◽  
Planning Algorithm

In order to enhance performance of robot systems in the manufacturing industry, it is essential to develop motion and task planning algorithms. Especially, it is important for the motion plan to be generated automatically in order to deal with various working environments. Although PRM (Probabilistic Roadmap) provides feasible paths when the starting and goal positions of a robot manipulator are given, the path might not be smooth enough, which can lead to inefficient performance of the robot system. This paper proposes a motion planning algorithm for robot manipulators using a twin delayed deep deterministic policy gradient (TD3) which is a reinforcement learning algorithm tailored to MDP with continuous action. Besides, hindsight experience replay (HER) is employed in the TD3 to enhance sample efficiency. Since path planning for a robot manipulator is an MDP (Markov Decision Process) with sparse reward and HER can deal with such a problem, this paper proposes a motion planning algorithm using TD3 with HER. The proposed algorithm is applied to 2-DOF and 3-DOF manipulators and it is shown that the designed paths are smoother and shorter than those designed by PRM.

Motion planning for multiple non-holonomic robots: a geometric approach

Robotica ◽  
2008 ◽  
Vol 26 (4) ◽  
pp. 525-536 ◽  
Author(s):  
Elias K. Xidias ◽  
Nikos A. Aspragathos
Keyword(s):  
Genetic Algorithm ◽  
Euclidean Space ◽  
Motion Planning ◽  
Optimization Problem ◽  
Geometric Approach ◽  
Dynamic Environments ◽  
Planning Problem ◽  
Geometrical Approach ◽  
Moving Obstacles ◽  
Geometric Entity

SUMMARYIn this paper, a geometrical approach is developed to generate simultaneously optimal (or near-optimal) smooth paths for a set of non-holonomic robots, moving only forward in a 2D environment cluttered with static and moving obstacles. The robots environment is represented by a 3D geometric entity called Bump-Surface, which is embedded in a 4D Euclidean space. The multi-motion planning problem (MMPP) is resolved by simultaneously finding the paths for the set of robots represented by monoparametric smooth C2 curves onto the Bump-Surface, such that their inverse images onto the initial 2D workspace satisfy the optimization motion-planning criteria and constraints. The MMPP is expressed as an optimization problem, which is solved on the Bump-Surface using a genetic algorithm. The performance of the proposed approach is tested through a considerable number of simulated 2D dynamic environments with car-like robots.

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