Collision-free Path For Robot Research Articles

GANsを用いた多自由度ロボットの任意の最適化基準に基づく軌道計画

GANsを用いた多自由度ロボットの任意の最適化基準に基づく軌道計画

A virtual-physical collision detection interface for AR-based interactive teaching of robot

Abstract At present, online lead-through and offline programming methods are widely used in programming of industrial robots. However, both methods have some drawbacks for unskilled shopworkers. This paper presents an Augmented Reality (AR)-based interactive robot teaching programming system, which virtually projected the robot onto the physical industrial environment. The unskilled shopworkers can use Handheld Teaching Device (HTD) to move end-effector of virtual robot to follow endpoint of the HTD. In this way, the path of the virtual robot can be planned or tested interactively. In addition, collisions detection between virtual robot and physical environment is key to test the feasibility of robot path. So, a method for detecting virtual-physical collisions is presented in this paper by comparing the depth values of corresponding pixels in depth image acquired by Kinect and computer-generated image in order to get collision-free paths of the virtual robot. The Quadtree model is used to accelerate the collision detection process and get distance between virtual model and physical environment. Using the AR-based interactive robot teaching programming system presented in this paper, all workers even unskilled ones in robot programming, can quickly and effectively get the collision-free robot path.

Collision-free Robot Path Planning under Multiple Obstacle Conditions with Latent Space of cGANs

Collision-free Robot Path Planning under Multiple Obstacle Conditions with Latent Space of cGANs

Heuristic D* Algorithm Based on Particle Swarm Optimization for Path Planning of Two-Link Robot Arm in Dynamic Environment

Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved. In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO) technique. Moreover, this work focuses on computational part of motion planning in completely changing dynamic environment at every motion sample domains. Since the environment type that discussed here is a known dynamic environment, the solution approach can be off-line. The main advantage of the off-line planning is that a global optimal path solution is always obtained, which is able to overcome all the difficulties caused by the dynamic behavior of the obstacles. A mixing approach of robot path planning using the heuristic method D* algorithm based on optimization technique is used. The heuristic D* method is chosen for finding the shortest path. Furthermore, to insure the path length optimality and for enhancing the final path, PSO technique has been utilized. The robot type has been used here is the two-link robot arm which represents a more difficult case than the mobile robot. Simulation results are given to show the effectiveness of the proposed method which clearly shows a completely safe and short path.

Process Simulation and Automatic Path Planning of Adhesive Joining

Adhesive joining is frequently used in the automotive industry. In the pursuit of reducing weight, adhesive joining is important due to the possibility of joining different types of materials. The process is often automatised in order to reduce cycle time. In this paper we aim to present a novel framework that includes detailed process simulation and automatic generation of collision free robot paths and in this way improve the quality of the joint and reduce both cycle time and processing time. To verify the simulations, the properties of the adhesive bead have been compared to experiments with good agreement.

Layered Approach for Three Dimensional Collision Free Robot Path Planning using Genetic Algorithm

Robot Path Planning (RPP) is solved using Genetic Algorithm (GA) principle for collision-free navigation for the three dimensional static space to find the optimal path. In this paper, Layered Approach is employed where the whole three dimensional space is considered as layers of two dimensional spaces to accomplish the RPP to reach the target by avoiding obstacles and find the shortest path. The quality of the path is ensured by nearest neighbourhood approach. Implementation of the principle of GA to solve RPP is effective where the environment contains huge number of solution paths compared to the classical methods to obtain the shortest path from source to target. This approach is tested for different number of layers and the results are tabled. The path generated and optimal path obtained by the implementation of this approach has been compared with the cost of the optimal path obtained manually.

Optimisation of robotised sealing stations in paint shops by process simulation and automatic path planning

Application of sealing materials is done in order to prevent water leakage into cavities of the car body, and to reduce noise. The complexity of the sealing spray process is characterised by multi-phase and free surface flows, multi-scale phenomena, and large moving geometries, which poses great challenges for mathematical modelling and simulation. The aim of this paper is to present a novel framework that includes detailed process simulation and automatic generation of collision free robot paths. To verify the simulations, the resulting width, thickness and shape of applied material on test plates as a function of time and spraying distance have been compared to experiments. The agreement is in general very good. The efficient implementation makes it possible to simulate application of one meter of sealing material in less than an hour on a standard computer, and it is therefore feasible to include such detailed simulations in the production preparation process and off-line programming of the sealing robots.

A Parallelized Matrix-Multiplication Implementation of Neural Network for Collision Free Robot Path Planning

paper covers the problem of Collision Free Path Planning for an autonomous robot and proposes a solution to it through the Back Propagation Neural Network. The solution is transformed into a composition of matrix-multiplication operations, which is a classic example of problems that can be efficiently parallelized. This paper finally proposes a parallel implementation of this matrix-multiplication method which, in itself, encapsulates the neural network that implements the collision free path planning for an autonomous robot.

Haptic-aided robot path planning based on virtual tele-operation

The motivation of this work arises from the fact that it is very difficult for an automatic path planning method, e.g. probabilistic roadmap ( PRM) to generate an optimized path, and sometimes it even fails for an automatic method to find a collision-free path, due to failure of discovering critical robot configurations. However, these critical configurations might be plain to an operator. Therefore, the advantages of human's intuition are exploited to facilitate the robot path planning process in this research. The objective of this paper is to combine the advantages of human's intuition or experiences and the huge computational power of computers to develop a semi-automatic robot path planner that can generate an user-preferred collision-free robot path. In the path planning process, the user interaction is made easy by the development of a haptically controlled virtual robot. By virtual tele-robot manipulation, a user can define or modify critical robot configurations based on which collision-free robot path can be automatically generated.

Eine neue Methodik zur Erhöhung der Leistungsfähigkeit Evolutionärer Algorithmen durch die Integration lokaler Suchverfahren

Article Eine neue Methodik zur Erhöhung der Leistungsfähigkeit Evolutionärer Algorithmen durch die Integration lokaler Suchverfahren was published on April 1, 2004 in the journal at - Automatisierungstechnik (volume 52, issue 4).

A Framework for Real-time Path Planning in Changing Environments

We present a new method for generating collision-free paths for robots operating in changing environments. Our approach is closely related to recent probabilistic roadmap approaches. These planners use preprocessing and query stages, and are aimed at planning many times in the same environment. In contrast, our preprocessing stage creates a representation of the configuration space that can be easily modified in real time to account for changes in the environment, thus facilitating real-time planning. As with previous approaches, we begin by constructing a graph that represents a roadmap in the configuration space, but we do not construct this graph for a specific workspace. Instead, we construct the graph for an obstacle-free workspace, and encode the mapping from workspace cells to nodes and arcs in the graph. When the environment changes, this mapping is used to make the appropriate modifications to the graph, and plans can be generated by searching the modified graph. In this paper, we first discuss the construction of the roadmap, including how random samples of the configuration space are generated using an importance sampling approach and how these samples are connected to form the roadmap. We then discuss the mapping from the workspace to the configuration space roadmap, explaining how the mapping is generated and how it can be encoded efficiently using compression schemes that exploit redundancy in the mapping. We then introduce quantitative robustness measures and show how these can be used to enhance the robustness of the roadmap to changes in the environment. Finally, we evaluate an implementation of our approach for serial-link manipulators with up to 20 joints.

A Framework for Real-time Path Planning in Changing Environments

We present a new method for generating collision-free paths for robots operating in changing environments. Our approach is closely related to recent probabilistic roadmap approaches. These planners use preprocessing and query stages, and are aimed at planning many times in the same environment. In contrast, our preprocessing stage creates a representation of the configuration space that can be easily modified in real time to account for changes in the environment, thus facilitating real-time planning. As with previous approaches, we begin by constructing a graph that represents a roadmap in the configuration space, but we do not construct this graph for a specific workspace. Instead, we construct the graph for an obstacle-free workspace, and encode the mapping from workspace cells to nodes and arcs in the graph. When the environment changes, this mapping is used to make the appropriate modifications to the graph, and plans can be generated by searching the modified graph. In this paper, we first discuss the construction of the roadmap, including how random samples of the configuration space are generated using an importance sampling approach and how these samples are connected to form the roadmap. We then discuss the mapping from the workspace to the configuration space roadmap, explaining how the mapping is generated and how it can be encoded efficiently using compression schemes that exploit redundancy in the mapping. We then introduce quantitative robustness measures and show how these can be used to enhance the robustness of the roadmap to changes in the environment. Finally, we evaluate an implementation of our approach for serial-link manipulators with up to 20 joints.

Planning near-minimum-length collision-free paths for robots

This paper describes an algorithm for the automatic generation of near-minimum-length collision-free paths for robots. Obstacles are assumed to have polygonal cross sections. This algorithm requires very small data storage. Expansion of obstacles and shrinkage of robots to a point are used to simplify the analysis of the robot collision detection problem. The robot path is considered to be composed of straight line segments. Collision detection criteria between the robot path and the obstacles are discussed. A scheme to search for near-minimum length collision-free paths is presented. This scheme has the benefit of being concise and suitable for real-time implementation, especially for robots working in cluttered environments. Examples to show the above ideas are included.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Path planning for a mobile robot

Two problems for path planning of a mobile robot are considered. The first problem is to find a shortest-time, collision-free path for the robot in the presence of stationary obstacles in two dimensions. The second problem is to determine a collision-free path (greedy in time) for a mobile robot in an environment of moving obstacles. The environment is modeled in space-time and the collision-free path is found by a variation of the A* algorithm. >

Two-Phase Path Planning for Robots With Six or More Joints

This paper presents a new method for planning collision-free paths for robots with any number of joints. It is particularly well suited for use with kinematically redundant robots. The algorithm is general in that it does not impose restrictions on the geometry, motion, or payload of the robot. It does not try to locate an optimal path. Instead, it attempts to locate a reasonable path while mapping a minimal amount of configuration space (c-space). The method involves iteratively modifying a connected path between the initial and goal configurations to avoid all intervening obstacles. Information from the world model is used to guide path modification. This approach is of particular value in high-dimensional cases for which exhaustive searches are impractical. In the worst case, the algorithm maps a straight-line path in c-space to the goal and the surfaces of the interfering obstacles along this path. An example for a seven-degree-of-freedom robot is included.