Velocity Planning for Astronaut Virtual Training Robot with High-Order Dynamic Constraints

Robotica ◽  
2020 ◽  
Vol 38 (12) ◽  
pp. 2121-2137 ◽  
Author(s):  
Lan Wang ◽  
Lingjie Lin ◽  
Ying Chang ◽  
Da Song
Keyword(s):  
Target Object ◽  
High Order ◽  
Planning Time ◽  
Virtual Training ◽  
Object A ◽  
Dynamic Constraints ◽  
Velocity Planning ◽  
Cooperative Training ◽  
Planning Algorithm ◽  
Damping Torque

SUMMARYIn order to improve the training efficiency and establish a multi-person cooperative training simulation system, including “virtual human,” in the process of virtual reality-based astronaut training, it is necessary to plan the velocity at which astronauts carry the target object. A velocity planning algorithm, combining a traditional six-stage acceleration/deceleration algorithm, based on a time-discrete model with high-order dynamic constraints, considering the elastic damping torque of the space suit, is proposed. The described algorithm is verified on MATLAB to prove its feasibility. Compared to other algorithms, the planning time of the proposed algorithm is significantly reduced.

Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation

2011 ◽  
Vol 142 ◽  
pp. 12-15
Author(s):  
Ping Feng
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Traffic Condition ◽  
Planning Time ◽  
Dynamic Path Planning ◽  
Short Period ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm ◽  
Chaos Genetic Algorithm

The paper puts forward the dynamic path planning algorithm based on improving chaos genetic algorithm by using genetic algorithms and chaos search algorithm. In the practice of navigation, the algorithm can compute at the best path to meet the needs of the navigation in such a short period of planning time. Furthermore,this algorithm can replan a optimum path of the rest paths after the traffic condition in the sudden.

Efficient time-optimal feedrate planning under dynamic constraints for a high-order CNC servo system

2013 ◽  
Vol 45 (12) ◽  
pp. 1538-1546 ◽  
Author(s):  
Jian-Xin Guo ◽  
Ke Zhang ◽  
Qiang Zhang ◽  
Xiao-Shan Gao
Keyword(s):  
Servo System ◽  
High Order ◽  
Dynamic Constraints ◽  
Feedrate Planning ◽  
Time Optimal

scholarly journals Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

2021 ◽  
Vol 10 (5) ◽  
pp. 439-448
Author(s):  
Hrishikesh Dey ◽  
Rithika Ranadive ◽  
Abhishek Chaudhari
Keyword(s):  
Path Planning ◽  
Velocity Profile ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Autonomous Driving ◽  
Navigation Algorithm ◽  
Velocity Planning ◽  
Planning Algorithm ◽  
Path Planner

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.

scholarly journals Trajectory planning of multiple coordinating robots using genetic algorithms

Robotica ◽  
1996 ◽  
Vol 14 (2) ◽  
pp. 227-234 ◽  
Author(s):  
Shudong Sun ◽  
A.S. Morris ◽  
A.M.S. Zalzala
Keyword(s):  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Degrees Of Freedom ◽  
Coordinate Space ◽  
Object A ◽  
Dynamic Constraints ◽  
Order Polynomial ◽  
Cartesian Coordinate ◽  
Three Degrees Of Freedom

SUMMARYThe paper focuses on the problem of trajectory planning of multiple coordinating robots. When multiple robots collaborate to manipulate one object, a redundant system is formed. There are a number of trajectories that the system can follow. These can be described in Cartesian coordinate space by an nth order polynomial. This paper presents an optimisation method based on the Genetic Algorithms (GAs) which chooses the parameters of the polynomial, such that the execution time and the drive torques for the robot joints are minimized. With the robot's dynamic constraints taken into account, the pitimised trajectories are realisable. A case study with two planar-moving robots, each having three degrees of freedom, shows that the method is effective.

A Method to Obtain the Position of Space Object Based on Monocular Vision and Laser Ring

2011 ◽  
Vol 308-310 ◽  
pp. 1619-1626
Author(s):  
Nan Yin ◽  
Xing Long Zhu ◽  
Xin Zhao ◽  
Shang Gao
Keyword(s):  
Light Source ◽  
Target Object ◽  
Closed Curve ◽  
Monocular Vision ◽  
Least Square ◽  
Virtual Object ◽  
Space Object ◽  
Object A ◽  
Curve Equation ◽  
Laser Ring

When the cylindrical laser shines on the target object, a spot can be obtained, which the edge is a closed curve, marked as C1. The imaging of the curve C1 on the image surface of CCD is a closed curve C2 too. Coordinate system is established to describe the position relationship among camera, image and light source, and to analyze the principle for monocular vision and laser ring to get the information about the object depth. In order to solve the problem and make the above principle clear, the key is to work out the expression for the curve C2 on the image surface of CCD. In order to calculate the closed curve C2 expression, the curve C2 will firstly be divided into two parts, the upper curve and the lower one. According to least-square polynomial, discrete points on the curves of two parts are drawn out, constraints are established and the curve equations are fitted. Then, to verify practicality of this method, a virtual model scene will be created, through which relevant data describing edge of virtual CCD image and that of a virtual spot when the virtual light source alights on the virtual object will be obtained. At last, closed curve equation will be fitted in accordance with data describing edge of virtual image; the position of space object will be fixed by making use of light source equation and closed curve equation; and a contrast will be made between the calculated value and data of the spot edge to prove whether a method to obtain the position of space objects based on monocular vision and laser ring is feasible.

Three-Dimensional Object Surface Reconstruction Based on Camera Calibration and SIFT

2015 ◽  
Vol 719-720 ◽  
pp. 1191-1197 ◽  
Author(s):  
Jun Zhang ◽  
Long Ye ◽  
Qin Zhang ◽  
Jing Ling Wang
Keyword(s):  
3D Reconstruction ◽  
Camera Calibration ◽  
Image Matching ◽  
Three Dimensional ◽  
Target Object ◽  
Object A ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Dimensional Object

This paper is focused on camera calibration, image matching, both of which are the key issues in three-dimensional (3D) reconstruction. In terms of camera calibration firstly, we adopt the method based on the method proposed by Zhengyou Zhang. In addition to this, it is selective for us to deal with tangential distortion. In respect of image matching, we use the SIFT algorithm, which is invariant to image translation, scaling, rotation, and partially invariant to illumination changes and to affine or 3D projections. It performs well in the follow-up matching the corresponding points. Lastly, we perform 3D reconstruction of the surface of the target object. A Graphical User Interface is designed to help us to realize the key function of binocular stereo vision, with better visualization. Apparently, the entire GUI brings convenience to the follow-up work.

Predictive Motion Planning for Autonomous Vehicles With Geometric Constraints via Convex Optimization

2020 ◽  
Author(s):  
Xiaoyuan Zhu ◽  
Jian Chen ◽  
Yan Ma ◽  
Jianqiang Deng ◽  
Yuexuan Wang
Keyword(s):  
Decision Making ◽  
Motion Planning ◽  
Autonomous Vehicles ◽  
Linear Constraints ◽  
Geometric Constraints ◽  
Dynamic Constraints ◽  
The Road ◽  
Moving Obstacles ◽  
Planning Algorithm ◽  
Decision Module

Abstract In this paper, we propose an MPC-based motion planning algorithm, including a decision-making module, an obstacle-constraints generating module, and an MPC-based planning module. The designed decision module effectively distinguishes between structured and unstructured roads and processes them separately, so that the algorithm is more robust in different environments. Besides, the movement of obstacles is considered in the decision-making and obstacle constraints generating module. By processing obstacles with lateral and longitudinal speed separately, obstacle avoidance can be done in scenarios with moving obstacles, including moving obstacles crossing the road. Instead of treating the vehicle as a mass point, we explicitly consider the geometric constraints by modeling the vehicle as three intersecting circles when generating obstacle constraints. This ensures that the vehicle is collision-free in motion planning, especially when the vehicle turns. For non-convex obstacle constraints, we propose an algorithm that generates up to two alternative linear constraints to convexify the obstacle constraints for improving computational efficiency. In MPC, we consider the vehicle kino-dynamic constraints and two generated linear constraints. Therefore, the proposed method can achieve better real-time performance and can be applied to more complicated traffic scenarios with moving obstacles. Simulation results in three different scenarios show that motion planning can achieve satisfactory performance in both structured and unstructured roads with moving obstacles.

A flexible velocity planning algorithm for high-speed mounter with both efficiency and precision

2021 ◽  
Vol 43 (12) ◽  
Author(s):  
Guo-qing Hu ◽  
Jian-wei Ma ◽  
Yi-ming Zuo ◽  
Yun-feng Wang ◽  
Hui-teng Yan ◽  
...  
Keyword(s):  
High Speed ◽  
Velocity Planning ◽  
Planning Algorithm

When rank counts — dominant dogs learn better from a human demonstrator in a two-action test

Behaviour ◽  
2012 ◽  
Vol 149 (1) ◽  
pp. 111-132 ◽  
Author(s):  
Péter Pongrácz ◽  
Petra Bánhegyi ◽  
Ádám Miklósi
Keyword(s):  
Social Learning ◽  
Social Rank ◽  
Target Object ◽  
The Other ◽  
Control Group ◽  
Object A ◽  
Companion Dogs ◽  
Initial Preference ◽  
The Social ◽  
Human Demonstration

AbstractDogs can learn effectively from a human demonstrator in detour tests as well as in different kinds of manipulative tasks. In this experiment we used a novel two-action device from which the target object (a ball) was obtained by tilting a tube either by pulling a rope attached to the end of the tube, or by directly pushing the end of the tube. Tube tilting was relatively easy for naïve companion dogs; therefore, the effect of the human demonstration aimed to alter or increase the dogs’ initial preference for tube pushing (according to the behaviour shown by naïve dogs in the absence of a human demonstrator). Our results have shown that subjects preferred the demonstrated action in the two-action test. After having witnessed the tube pushing demonstration, dogs performed significantly more tube pushing than the dogs in the rope pulling demonstration group. In contrast, dogs that observed the rope pulling demonstration, performed significantly more similar actions than the subjects of the other demonstration group. The ratio of rope pulling was significantly higher in the rope pulling demonstration group, than in the No Demo (control) group. The overall success of solving the task was also influenced by the social rank of the dog among its conspecific companions at home. Independently of the type of demonstration, dominant dogs solved the task significantly more often than the subordinate dogs did. There was no such difference in the No Demo group. This experiment has shown that a simple two-action device that does not require excessive pre-training, can be suitable for testing social learning in dogs. However, effects of social rank should be taken into account when social learning in dogs is being studied and tested, because dominant and subordinate dogs perform differently after observing a demonstrator.

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