Real-Time Robot Capability Analysis

2005 ◽  
Author(s):  
Chalongrath Pholsiri ◽  
Chetan Kapoor ◽  
Delbert Tesar
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Real Time ◽  
Computationally Efficient ◽  
New Approach ◽  
Ellipsoid Method ◽  
Capability Analysis ◽  
Computationally Intensive ◽  
Planar Robots

Robot Capability Analysis (RCA) is a process in which force/motion capabilities of a manipulator are evaluated. It is very useful in both the design and operational phases of robotics. Traditionally, ellipsoids and polytopes are used to both graphically and numerically represent these capabilities. Ellipsoids are computationally efficient but tend to underestimate while polytopes are accurate but computationally intensive. This article proposes a new approach to RCA called the Vector Expansion (VE) method. The VE method offers accurate estimates of robot capabilities in real time and therefore is very suitable in applications like task-based decision making or online path planning. In addition, this method can provide information about the joint that is limiting a robot capability at a given time, thus giving an insight as to how to improve the performance of the robot. This method is then used to estimate capabilities of 4-DOF planar robots and the results discussed and compared with the conventional ellipsoid method. The proposed method is also successfully applied to the 7-DOF Mitsubishi PA10-7C robot.

An Efficient Neural Network Model for Path Planning of Car-like Robots in Dynamic Environment

2000 ◽  
Vol 4 (3) ◽  
pp. 220-229 ◽  
Author(s):  
Simon X. Yang ◽  
◽  
Max Meng ◽  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Lyapunov Stability Theory ◽  
Network Size ◽  
Computationally Efficient ◽  
Neural Network Approach ◽  
The Neural Network ◽  
The Stability

In this paper, an effcient neural network approach to real-time path planning with obstacle avoidance of holonomic car-like robots in a dynamic environment is proposed. The dynamics of each neuron in this biologically inspired, topologically organized neural network is characterized by a shunting equation or an additive equation. The state space of the neural network is the configuration space of the robot. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The real-time collision-free path is planned through the dynamic neural activity landscape of the neural network without explicitly searching over neither the free workspace nor the collision paths, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore it is computationally efficient. The stability of the neural network is proven by both qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency are demonstrated through simulation studies.

Building and Improving Tactical Agents in Real Time through a Haptic-Based Interface

2015 ◽  
Vol 24 (4) ◽  
pp. 383-403 ◽  
Author(s):  
Gary Stein ◽  
Avelino J. Gonzalez
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Motor Skill ◽  
Road Network ◽  
Force Feedback ◽  
Haptic Device ◽  
Human Interaction ◽  
Learning Approach ◽  
New Approach ◽  
High Bandwidth

AbstractThis article describes and evaluates an approach to create and/or improve tactical agents through direct human interaction in real time through a force-feedback haptic device. This concept takes advantage of a force-feedback joystick to enhance motor skill and decision-making transfer from the human to the agent in real time. Haptic devices have been shown to have high bandwidth and sensitivity. Experiments are described for this new approach, named Instructional Learning. It is used both as a way to build agents from scratch as well as to improve and/or correct agents built through other means. The approach is evaluated through experiments that involve three applications of increasing complexity – chasing a fleer (Chaser), shepherding a flock of sheep into a pen (Sheep), and driving a virtual automobile (Car) through a simulated road network. The results indicate that in some instances, instructional learning can successfully create agents under some circumstances. However, instructional learning failed to build and/or improve agents in other instances. The Instructional Learning approach, the experiments, and their results are described and extensively discussed.

scholarly journals Safe Path Planning of Mobile Robot in a known Dynamic Environment

2019 ◽  
Vol 9 (2) ◽  
pp. 2434-2439
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Dynamic Environment ◽  
Mobile Robot Navigation ◽  
Matlab Simulation ◽  
A Algorithm ◽  
New Approach ◽  
Probability Of Collision ◽  
Safe Path

Path planning in mobile robot navigation is an advanced method of calculating the safe and obstacle free path in static and dynamic environments are involved between source point to destination. Real time path planning method defines that how a robot can make a decision when some unknown obstacle gets encountered in the path of navigation for a dynamic situation. At the point when an obstruction comes in the way of route, the robot must choose another and safe way to advance towards the objective by evading any impact. This study is focused on exploring the algorithm that gives the safe and shortest path when an obstacle changes the environment. By using A* algorithm in MATLAB simulation the probability of collision with obstacle and robot get increased. In this simulation work a new approach of path planning has been found by placing the virtual obstacles in the environment. A new obstacle get influence in the path of navigation, using virtual obstacle boundary around the new obstacle a short and safe path get evaluated which is collision free or low risk path . The purpose for this paper is to create a dependable and smooth direction in a real time domain with impediments and to manage the robot towards the target without hitting the obstacles also considering the size of the robot

scholarly journals A New Real-Time Path Planning Method Based on the Belief Space

2013 ◽  
Vol 2013 ◽  
pp. 1-14
Author(s):  
Yu-xin Zhao ◽  
Xin-an Wu ◽  
Yan Ma
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Basic Belief ◽  
Complex Environment ◽  
New Approach ◽  
Time Path ◽  
Local Path Planning ◽  
Simulation Results ◽  
Local Path ◽  
Belief Model

A new approach of real-time path planning based on belief space is proposed, which solves the problems of modeling the real-time detecting environment and optimizing in local path planning with the fusing factors. Initially, a double-safe-edges free space is defined for describing the sensor detecting characters, so as to transform the complex environment into some free areas, which can help the robots to reach any positions effectively and safely. Then, based on the uncertainty functions and the transferable belief model (TBM), the basic belief assignment (BBA) spaces of each factor are presented and fused in the path optimizing process. So an innovative approach for getting the optimized path has been realized with the fusing the BBA and the decision making by the probability distributing. Simulation results indicate that the new method is beneficial in terms of real-time local path planning.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

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