scholarly journals Contact-Rich Trajectory Generation in Confined Environments Using Iterative Convex Optimization

2020 ◽  
Author(s):  
Wei-Ye Zhao ◽  
Suqin He ◽  
Chengtao Wen ◽  
Changliu Liu
Keyword(s):  
Convex Optimization ◽  
Motion Planning ◽  
Real Time ◽  
Trajectory Generation ◽  
Robot Kinematics ◽  
Planning Problem ◽  
Robot Arm ◽  
Task Constraints ◽  
Robot Arms ◽  
Highly Nonlinear

Abstract Applying intelligent robot arms in dynamic uncertain environments (i.e., flexible production lines) remains challenging, which requires efficient algorithms for real time trajectory generation. The motion planning problem for robot trajectory generation is highly nonlinear and nonconvex, which usually comes with collision avoidance constraints, robot kinematics and dynamics constraints, and task constraints (e.g., following a Cartesian trajectory defined on a surface and maintain the contact). The nonlinear and nonconvex planning problem is computationally expensive to solve, which limits the application of robot arms in the real world. In this paper, for redundant robot arm planning problems with complex constraints, we present a motion planning method using iterative convex optimization that can efficiently handle the constraints and generate optimal trajectories in real time. The proposed planner guarantees the satisfaction of the contact-rich task constraints and avoids collision in confined environments. Extensive experiments on trajectory generation for weld grinding are performed to demonstrate the effectiveness of the proposed method and its applicability in advanced robotic manufacturing.

Collision-free motion planning for two articulated robot arms using minimum distance functions

Robotica ◽  
1990 ◽  
Vol 8 (2) ◽  
pp. 137-144 ◽  
Author(s):  
C. Chang ◽  
M. J. Chung ◽  
Z. Bien
Keyword(s):  
Motion Planning ◽  
Minimum Distance ◽  
Three Dimensional ◽  
Distance Functions ◽  
Free Motion ◽  
Planning Problem ◽  
Robot Arm ◽  
Robot Arms ◽  
Nonlinear Minimization ◽  
Articulated Robot

SummaryThis paper presents a collision-free motion planning method of two articulated robot arms in a three dimensional common work space. Each link of a robot arm is modeled by a cylinder ended by two hemispheres, and the remaining wrist and hand is modeled by a sphere. To describe the danger of collision between two modeled objects, minimum distance functions, which are defined by the Euclidean norm, are used. These minimum distance functions are used to describe the constraints that guarantee no collision between two robot arms. The collision-free motion planning problem is formulated as a pointwise constrained nonlinear minimization problem, and solved by a conjugate gradient method with barrier functions. To improve the minimization process, a simple grid technique is incorporated. Finally, a simulation study is presented to show the significance of the proposed method.

Multi-Car Convex Feasible Set Algorithm in Trajectory Planning

2020 ◽  
Author(s):  
Jing Huang ◽  
Changliu Liu
Keyword(s):  
Convex Optimization ◽  
Quadratic Programming ◽  
Real Time ◽  
Trajectory Planning ◽  
Autonomous Driving ◽  
Curse Of Dimensionality ◽  
Planning Problem ◽  
Feasible Set ◽  
Planning Algorithm ◽  
Vehicle Trajectory

Abstract Trajectory planning is an essential module for autonomous driving. To deal with multi-vehicle interactions, existing methods follow the prediction-then-plan approaches which first predict the trajectories of others then plan the trajectory for the ego vehicle given the predictions. However, since the true trajectories of others may deviate from the predictions, frequent re-planning for the ego vehicle is needed, which may cause many issues such as instability or deadlock. These issues can be overcome if all vehicles can form a consensus by solving the same multi-vehicle trajectory planning problem. Then the major challenge is how to efficiently solve the multi-vehicle trajectory planning problem in real time under the curse of dimensionality. We introduce a novel planner for multi-vehicle trajectory planning based on the convex feasible set (CFS) algorithm. The planning problem is formulated as a non-convex optimization. A novel convexification method to obtain the maximal convex feasible set is proposed, which transforms the problem into a quadratic programming. Simulations in multiple typical on-road driving situations are conducted to demonstrate the effectiveness of the proposed planning algorithm in terms of completeness and optimality.

Computer Aided Synthesis of Piecewise Rational Motions for Spherical 2R and 3R Robot Arms

2008 ◽  
Vol 130 (11) ◽  
Author(s):  
Anurag Purwar ◽  
Zhe Jin ◽  
Q. J. Ge
Keyword(s):  
Motion Planning ◽  
Spline Interpolation ◽  
Robot Arm ◽  
Spherical Robot ◽  
Kinematic Constraints ◽  
Robot Arms ◽  
Revolute Joints ◽  
Computer Aided ◽  
Planar Robot ◽  
Circular Interpolation

This paper deals with the problem of synthesizing smooth piecewise rational spherical motions of an object that satisfies the kinematic constraints imposed by a spherical robot arm with revolute joints. This paper brings together the kinematics of spherical robot arms and recently developed freeform rational motions to study the problem of synthesizing constrained rational motions for Cartesian motion planning. The kinematic constraints under consideration are workspace related constraints that limit the orientation of the end link of robot arms. This paper extends our previous work on synthesis of rational motions under the kinematic constraints of planar robot arms. Using quaternion kinematics of spherical arms, it is shown that the problem of synthesizing the Cartesian rational motion of a 2R arm can be reduced to that of circular interpolation in two separate planes. Furthermore, the problem of synthesizing the Cartesian rational motion of a spherical 3R arm can be reduced to that of constrained spline interpolation in two separate planes. We present algorithms for the generation of C1 and C2 continuous rational motion of spherical 2R and 3R robot arms.

Computer Aided Synthesis of Piecewise Rational Motions for Planar 2R and 3R Robot Arms

2006 ◽  
Vol 129 (10) ◽  
pp. 1031-1036 ◽  
Author(s):  
Zhe Jin ◽  
Q. J. Ge
Keyword(s):  
Motion Planning ◽  
Spline Interpolation ◽  
Circular Ring ◽  
Robot Arm ◽  
Kinematic Constraints ◽  
Robot Arms ◽  
Revolute Joints ◽  
Computer Aided ◽  
Planar Robot ◽  
Circular Interpolation

This paper deals with the problem of synthesizing piecewise rational motions of an object that satisfies kinematic constraints imposed by a planar robot arm with revolute joints. This paper brings together the kinematics of planar robot arms and the recently developed freeform rational motions to study the problem of synthesizing constrained rational motions for Cartesian motion planning. Through the use of planar quaternions, it is shown that for the case of a planar 2R arm, the problem of rational motion synthesis can be reduced to that of circular interpolations in two separate planes and that for the case of a planar 3R arm, the problem can be reduced to a combination of circular interpolation in one plane and a constrained spline interpolation in a circular ring on another plane. Due to the limitation of circular interpolation, only C1 continuous rational motions are generated that satisfy the kinematic constraints exactly. For applications that require C2 continuous motions, this paper presents a method for generating C2 continuous motions that approximate the kinematic constraints for planar 2R and 3R robot arms.

scholarly journals Real-Time Trajectory Generation for Haptic Feedback Manipulators in Virtual Cockpit Systems

2018 ◽  
Vol 18 (4) ◽  
Author(s):  
Shiyu Zhang ◽  
Shuling Dai
Keyword(s):  
Power Consumption ◽  
Nonlinear Optimization ◽  
Real Time ◽  
Haptic Feedback ◽  
Optimization Problems ◽  
Trajectory Generation ◽  
Feedback System ◽  
Planning Problem ◽  
Motion Time ◽  
On Line

To obtain real-time interactions in the virtual cockpit system (VCS), a real-time trajectory generation method based on dynamical nonlinear optimization and regression prediction for the haptic feedback manipulator (HFM) is presented in this paper. First, a haptic feedback system based on servoserial manipulator is constructed. Then, the trajectory planning problem for the HFM is formulated as a nonlinear optimization problem to balance the motion time and power consumption and ensure the safety of physical human–robot interactions (pHRI). Multiple optimization problems are solved to generate the optimal database off-line. Finally, the classified multivariate (CM) regression method is presented to learn the database and generate optimal trajectories with arbitrary initial and objective positions on-line. Results show that trajectories with rapidity, safety, and lower power consumption can be generated in real-time by this method, which lay a basis of haptic interactions in the VCS.

Task-Constrained Optimal Motion Planning of Redundant Robots Via Sequential Expanded Lagrangian Homotopy

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Audelia G. Dharmawan ◽  
Shaohui Foong ◽  
Gim Song Soh
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Computational Time ◽  
Mobile Manipulator ◽  
Optimal Motion ◽  
Task Constraints ◽  
Moving Obstacles ◽  
Time Motion ◽  
Optimal Motion Planning

Real-time motion planning of robots in a dynamic environment requires a continuous evaluation of the determined trajectory so as to avoid moving obstacles. This is even more challenging when the robot also needs to perform a task optimally while avoiding the obstacles due to the limited time available for generating a new collision-free path. In this paper, we propose the sequential expanded Lagrangian homotopy (SELH) approach, which is capable of determining the globally optimal robot's motion sequentially while satisfying the task constraints. Through numerical simulations, we demonstrate the capabilities of the approach by planning an optimal motion of a redundant mobile manipulator performing a complex trajectory. Comparison against existing optimal motion planning approaches, such as genetic algorithm (GA) and neural network (NN), shows that SELH is able to perform the planning at a faster rate. The considerably short computational time opens up an opportunity to apply this method in real time; and since the robot's motion is planned sequentially, it can also be adjusted to accommodate for dynamically changing constraints such as moving obstacles.

Real-time motion planning for robot manipulators in unknown environments using infrared sensors

Robotica ◽  
2007 ◽  
Vol 25 (2) ◽  
pp. 201-211 ◽  
Author(s):  
Shuguo Wang ◽  
Jin Bao ◽  
Yili Fu
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Collision Detection ◽  
Detection Method ◽  
Computation Time ◽  
Robot Arm ◽  
Infrared Sensors ◽  
Geometry Model ◽  
Time Motion ◽  
Series Of Experiments

SUMMARYThis paper deals with sensor-based motion planning method for a robot arm manipulator operating among unknown obstacles of arbitrary shape. It can be applied to online collision avoidance with no prior knowledge of the obstacles. Infrared sensors are used to build a description of the robot's surroundings. This approach is based on the configuration space but the construction of the C-obstacle surface is avoided. The point automation is confined on some planes with square grids in the C-space. A path-searching algorithm based on square grids is used to guide the automation maneuvering around the C-obstacles on the selected planes. To avoid the construction of the C-obstacle surface, the robot geometry model is expanded, and the static collision detection method is used. Hence, the computation time is reduced and the algorithm can work in real time. The effectiveness of the proposed method is verified by a series of experiments.

scholarly journals Optimization of Multi-Intelligent Robot Control System Based on Wireless Communication Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bin Li
Keyword(s):  
Control System ◽  
Remote Control ◽  
Motion Planning ◽  
Real Time ◽  
Wireless Network ◽  
Pid Controller ◽  
Network Control ◽  
Robot Arm ◽  
Network Control System ◽  
The Real

The robot is a very complex multi-input multioutput nonlinear system. It has time-varying, strong coupling, and nonlinear dynamic characteristics, and its control is very complicated. Due to the inaccuracy of measurement and modeling, coupled with changes in the load and the influence of external disturbances, it is actually impossible to obtain an accurate and complete dynamic model of the robot. We must face the existence of various uncertain factors of the robot. This paper analyzes the real-time communication protocol in the wireless network control system and confirms that the main way to improve the real-time performance of the wireless network control system is to implement the real-time media access control (MAC) protocol. This paper studies robots from the perspective of control and mainly discusses how to use artificial immune algorithms to design robust nonlinear proportion integral derivative (PID) controllers. A nonlinear PID controller is used to replace the classic PID controller. The nonlinear link can be adjusted with the change of the error, so as to achieve the purpose of improving the adaptability and robustness to obtain satisfactory tracking performance. We carried out selective compliance assembly robot arm (SCARA) robot remote control experiment, dual robot following experiment, SCARA and ABB robot collisionless motion planning experiment, and multirobot intelligent collaborative assembly experiment. The experimental results show that the C/S mode remote control system has good practicability and can complete remote tasks; the P2P communication system has good information transmission effects and can realize real-time information sharing between robots; the collision-free motion planning algorithm enables the dual robots to complete obstacle avoidance tasks well in complex operating environments; the functional modules of the system can closely cooperate to complete the tasks in coordination, and the multirobot system has a certain degree of intelligence.

Development of Autonomous Mobile Robot for Obstacle Avoidance

1993 ◽  
Vol 5 (5) ◽  
pp. 481-486 ◽  
Author(s):  
Masafumi Uchida ◽  
◽  
Syuichi Yokoyama ◽  
Hideto Ide ◽  
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Potential Method ◽  
Working Environment ◽  
Planning Problem ◽  
Autonomous Mobile Robot ◽  
The Real

The potential method is superior for solving the problem of motion planning; however, it must address the problem of the real-time generation of potential field. Obstacle avoidance is a motion planning problem. In a previous study, we investigated the real-time generation of potential field. Based on parallel processing with element group, we proposed the system by Sensory Point Moving (SPM) method. As a result of computer simulation, it was confirmed that the SPM method is effective for generating an obstacle avoidance path in 2-D and a more complex working environment like a 3-D one. In this paper, we discuss the development of autonomous mobile robot for obstacle avoidance based on the SPM method.

MASSIVELY PARALLEL IDA* SEARCH

1993 ◽  
Vol 02 (02) ◽  
pp. 163-180 ◽  
Author(s):  
DIANE J. COOK ◽  
GARY LYONS
Keyword(s):  
Artificial Intelligence ◽  
Motion Planning ◽  
Heuristic Search ◽  
Massively Parallel ◽  
Search Process ◽  
Planning Problem ◽  
Robot Arm ◽  
Speed Up ◽  
Arm Motion ◽  
Computational Bottleneck

Heuristic search is a fundamental component of Artificial Intelligence applications. Because search routines are frequently also a computational bottleneck, numerous methods have been explored to increase the efficiency of search. Recently, researchers have begun investigating methods of using parallel MIMD and SIMD hardware to speed up the search process. In this paper, we present a massively-parallel SIMD approach to search named MIDA* search. The components of MIDA* include a very fast distribution algorithm which biases the search to one side of the tree, and an incrementally-deepening depthfirst search of all the processors in parallel. We show the results of applying MIDA* to instances of the Fifteen Puzzle problem and to the robot arm motion planning problem. Results reveal an efficiency of 74% and a speedup of 8553 and 492 over serial and 16-processor MIMD algorithms, respectively, when finding a solution to the Fifteen Puzzle problem that is close to optimal.

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