Two-phase A*: A real-time global motion planning method for non-holonomic unmanned ground vehicles

2020 ◽  
pp. 095440702094839
Author(s):  
Kai Zhang ◽  
Yi Yang ◽  
Mengyin Fu ◽  
Meiling Wang
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Optimal Path ◽  
Computation Time ◽  
Planning Method ◽  
Global Motion ◽  
Two Phase ◽  
Holonomic Constraints ◽  
Planning Cycle ◽  
Indoor And Outdoor Environments

This paper presents a search-based global motion planning method, called the two-phase A*, with an adaptive heuristic weight. This method is suitable for planning a global path in real time for a car-like vehicle in both indoor and outdoor environments. In each planning cycle, the method first estimates a proper heuristic weight based on the hardness of the planning query. Then, it finds a nearly optimal path subject to the non-holonomic constraints using an improved A* with a weighted heuristic function. By estimating the heuristic weight dynamically, the two-phase A* is able to adjust the optimality level of its path based on the hardness of the planning query. Therefore, the two-phase A* sacrifices little planning optimality, and its computation time is acceptable in most situations. The two-phase A* has been implemented and tested in the simulations and real-world experiments over various task environments. The results show that the two-phase A* can generate a nearly optimal global path dynamically, which satisfies the non-holonomic constraints of a car-like vehicle and reduces the total navigation time.

A Classifier of Shoulder Movements for a Wearable EMG-Based Device

2017 ◽  
Vol 02 (02) ◽  
pp. 1740003
Author(s):  
Giuseppina Gini ◽  
Lisa Mazzon ◽  
Simone Pontiggia ◽  
Paolo Belluco
Keyword(s):  
Real Time ◽  
Computation Time ◽  
Body Part ◽  
Time Constraints ◽  
Pattern Recognition Method ◽  
Noninvasive Method ◽  
Recognition Method ◽  
Two Phase ◽  
The Real ◽  
User Intentions

Prostheses and exoskeletons need a control system able to rapidly understand user intentions; a noninvasive method is to deploy a myoelectric system, and a pattern recognition method to classify the intended movement to input to the controller. Here we focus on the classification phase. Our first aim is to recognize nine movements of the shoulder, a body part seldom considered in the literature and difficult to treat since the muscles involved are deep. We show that our novel sEMG two-phase classifier, working on a signal window of 500[Formula: see text]ms with 62[Formula: see text]ms increment, has a 97.7% accuracy for nine movements and about 100% accuracy on five movements. After developing the classifier using professionally collected sEMG data from eight channels, our second aim is to implement the classifier on a wearable device, composed by the Intel Edison board and a three-channel experimental portable acquisition board. Our final aim is to develop a complete classifier for dynamic situations, considering the transitions between movements and the real-time constraints. The performance of the classifier, using three channels, is about 96.9%, the classification frequency is 62[Formula: see text]Hz, and the computation time is 16[Formula: see text]ms, far less than the real-time constraint of 300[Formula: see text]ms.

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.

2A2-D22 A Global Motion Planning Method for Humanoid Robots

2010 ◽  
Vol 2010 (0) ◽  
pp. _2A2-D22_1-_2A2-D22_3 ◽  
Author(s):  
Hiroyuki TSUKAGOSHI ◽  
Eiichi YOSHIDA ◽  
Kazuhito YOKOI
Keyword(s):  
Motion Planning ◽  
Humanoid Robots ◽  
Planning Method ◽  
Global Motion

Motion planning approach for car-like robots in unstructured scenario

2021 ◽  
pp. 014233122199439
Author(s):  
Xuehao Sun ◽  
Shuchao Deng ◽  
Tingting Zhao ◽  
Baohong Tong
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Optimal Path ◽  
Conversion Function ◽  
Elastic Band ◽  
Emergency Situations ◽  
Time Motion ◽  
Planning Approach

When a car-like robot travels in an unstructured scenario, real-time motion planning encounters the problem of unstable motion state in obstacle avoidance planning. This paper presents a hybrid motion planning approach based on the timed-elastic-band (TEB) approach and artificial potential field. Different potential fields in an unstructured scenario are established, and the real-time velocity of the car-like robot is planned by using the conversion function of the virtual potential energy of the superimposed potential field and the virtual kinetic energy of the robot. The optimized TEB approach plans the local optimal path and solves the problems related to the local minimum region and non-reachable targets. The safety area of the dynamic obstacle is constructed to realize turning or emergency stop obstacle avoidance, thereby effectively ensuring the safety of the car-like robot in emergency situations. The simulation experiments show that the proposed approach has superior kinematic characteristics and satisfactory obstacle avoidance planning effects and can improve the motion comfort and safety of the car-like robot. In the practical test, the car-like robot moves stably in a dynamic scenario, and the proposed approach satisfies the actual application requirements.

scholarly journals Optimized Dynamic Routing in Multimedia Vehicular Networks

2020 ◽  
Vol 02 (03) ◽  
pp. 174-182
Author(s):  
Dr. Joy Iong Zong Chen ◽  
Dr. S. Smys
Keyword(s):  
Ant Colony Optimization ◽  
Real Time ◽  
Vehicular Networks ◽  
Optimal Path ◽  
Dynamic Network ◽  
Computation Time ◽  
Dynamic Routing ◽  
Ant Colony ◽  
Intelligent Transport System ◽  
Routing Strategy

Intelligent transport system is one of thriving research domain and in particular multimedia vehicular networks gains more attention. The deployment of multimedia services in vehicle ad-hoc networks (VANETs) provides real time support to users and provides better traffic management with high safety measures. Most of the vehicles has multimedia applications such as weather sensors, real time map applications etc., and it requires adequate resources to process. Resource allocation to a static network is simple and various routing models are evolved. In case of dynamic network like VANETs, routing is still in progress in order to obtain a better model. Routing directly related to quality of services of network and user, it is essential to develop a better dynamic routing strategy for multimedia wireless networks. The proposed work aims to provide an optimized dynamic routing strategy for multimedia networks. For efficient routing, k-means clustering is used to organize the clusters and exchanges the routing information and inverted ant colony optimization is used to obtain the optimal path for multimedia access. Proposed model is experimentally verified and compared to conventional ant colony optimization and grey wolf optimization in terms of parameters such as end to end delay, throughput, target used, average computation time and efficiency.

Energy-optimal motion planning of a biped pole-climbing robot with kinodynamic constraints

2018 ◽  
Vol 45 (3) ◽  
pp. 343-353 ◽  
Author(s):  
Xuefeng Zhou ◽  
Li Jiang ◽  
Yisheng Guan ◽  
Haifei Zhu ◽  
Dan Huang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Optimal Path ◽  
Planning Method ◽  
Outdoor Environment ◽  
Planning Problem ◽  
Climbing Robot ◽  
Optimal Motion ◽  
Climbing Robots ◽  
Content Type ◽  
Optimal Motion Planning

Purpose Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment. Design/methodology/approach First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively. Findings Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper. Research limitations/implications Only one step climbing motion planning is considered in this method. Practical implications With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint. Originality/value The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

Vision-based smooth obstacle avoidance motion trajectory generation for autonomous mobile robots using Bézier curves

2016 ◽  
Vol 231 (3) ◽  
pp. 541-554 ◽  
Author(s):  
Kenneth Renny Simba ◽  
Naoki Uchiyama ◽  
Mohammad Aldibaja ◽  
Shigenori Sano
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Optimal Path ◽  
Trajectory Generation ◽  
Computation Time ◽  
Autonomous Mobile Robots ◽  
Bezier Curves ◽  
Bézier Curves ◽  
Time Motion

This paper proposes an obstacle avoidance trajectory generation method that provides a smooth trajectory in real time. The trajectory is generated from an environmental top-view image, where a fisheye lens is used to capture a wide area at low height. Corners of the obstacles are detected and corrected using the log-polar transform and are used to generate a simple configuration space that reduces the computation time. An optimal path is computed by using the A[Formula: see text] algorithm and replaced by a smooth trajectory generated based on piecewise quintic Bézier curves. Based on the established goal and visual information, a method for generating the first and second derivatives at the start and end points of each Bézier segment is proposed to generate a continuous curvature trajectory. The method is simple and easy to implement and has an average computation time of 1.17s on a PC (CPU: 1.4 GHz) for a workspace containing five to six obstacles. Experimental results verify that the proposed method is effective for real-time motion planning of autonomous mobile robots.

Sign in / Sign up

Export Citation Format

Share Document