Sampling-based path planning algorithms are powerful tools for computing disassembly motions. This paper presents a variant of the RRT algorithm particularly devised for the disassembly of objects with articulated parts. Configuration parameters generally play two different roles in this type of problems: some of them are essential for the disassembly task, while others only need to move if they hinder the progress of the disassembly process. The proposed method is based on such a partition of the configuration parameters. Results show a remarkable performance improvement compared to standard path planning techniques.

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Disassembly path planning for complex articulated objects

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed...

Machine learning for energy-water nexus: challenges and opportunities

Sampling-based methods are popular in the motion planning of robots, especially in high-dimensional spaces. Among the many such methods, the Rapidly-exploring Random Tree (RRT) algorithm has been widely used in multi-degree-of-freedom manipulators and has yielded good results. However, existing RRT planners have low exploration efficiency and slow convergence speed and have been unable to meet the requirements of the intelligence level in the Industry 4.0 mode. To solve these problems, a general autonomous path planning algorithm of Node Control (NC-RRT) is proposed in this paper based on the architecture of the RRT algorithm. Firstly, a method of gradually changing the sampling area is proposed to guide exploration, thereby effectively improving the search speed. In addition, the node control mechanism is introduced to constrain the extended nodes of the tree and thus reduce the extension of invalid nodes and extract boundary nodes (or near-boundary nodes). By changing the value of the node control factor, the random tree is prevented from falling into a so-called “local trap” phenomenon, and boundary nodes are selected as extended nodes. The proposed algorithm is simulated in different environments. Results reveal that the algorithm greatly reduces the invalid exploration in the configuration space and significantly improves planning efficiency. In addition, because this method can efficiently use boundary nodes, it has a stronger applicability to narrow environments compared with existing RRT algorithms and can effectively improve the success rate of exploration.

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Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm

The robust control of a mobile robot in a rough terrain environment is a challenging endeavour, since any reliance on favourable surface or environmental conditions will inevitably lead to task failure. This paper presents the preliminary development of a unified navigation system used to control a nonholonomic mobile robot in an a priori unknown outdoor domain. Accurate, high-resolution environmental data was gathered from a scanning laser rangefinder (ladar), which constitutes the robot's exteroceptive perception system. Using this data, a 3D tessellated environmental model was created that generically captures terrain traversability. By adapting the rapidly-exploring random tree approach to the tessellated model, an efficient kinodynamic path planning algorithm was devised that enables point-to-point trajectory traversal. This path planning strategy was found to be a computationally efficient method of producing robust and versatile path plans.

Path planning for a mobile robot in a rough terrain environment

In order to plan the robot path in 3D space efficiently, a modified Rapidly-exploring Random Trees based on heuristic probability bias-goal (PBG-RRT) is proposed. The algorithm combines heuristic probabilistic and bias-goal factor, which can get convergence quickly and avoid falling into a local minimum. Firstly, PBG-RRT is used to plan a path. After obtaining path points, path points are rarefied by the Douglas-Peucker algorithm while maintaining the original path characteristics. Then, a smooth trajectory suitable for the manipulator end effector is generated by Non-uniform B-spline interpolation. Finally, the effector is moving along the trajectory by inverse kinematics solving angle of joint. The above is a set of motion planning for the manipulator. Generally, 3D space obstacle avoidance simulation experiments show that the search efficiency of PBG-RRT is increased by 217%, while search time is dropped by 168% compared with P-RRT (Heuristic Probability RRT). After rarefying, the situation where the path oscillated around the obstacle is corrected effectively. And a smooth trajectory is fitted by spline interpolation. Ultimately, PBG-RRT is verified on the ROS (Robot Operating System) with the Robot-Anno manipulator. The results reveal that the validity and reliability of PBG-RRT are proofed in obstacle avoidance planning.

/pdf/a-heuristic-rapidly-exploring-random-trees-method-for-1o34d2oylg.pdf

A Heuristic Rapidly-Exploring Random Trees Method for Manipulator Motion Planning

With the development of modern manufacturing industry, the application scenarios of industrial robot are becoming more and more complex. Manual programming of industrial robot requires a great deal of effort and time. Therefore, an autonomous path planning is an important development direction of industrial robot. Among the path planning methods, the rapidly-exploring random tree (RRT) algorithm based on random sampling has been widely applied for a high-dimensional robotic manipulator because of its probability completeness and outstanding expansion. However, especially in the complex scenario, the existing RRT planning algorithms still have a low planning efficiency and some are easily fall into a local minimum. To tackle these problems, this paper proposes an autonomous path planning method for the robotic manipulator based on an improved RRT algorithm. The method introduces regression mechanism to prevent over-searching configuration space. In addition, it adopts an adaptive expansion mechanism to continuously improve reachable spatial information by refining the boundary nodes in joint space, avoiding repeatedly searching for extended nodes. Furthermore, it avoids the unnecessary iteration of the robotic manipulator forward kinematics solution and its time-consuming collision detection in Cartesian space. The method can rapidly plan a path to a target point and can be accelerated out of a local minimum area to improve path planning efficiency. The improved RRT algorithm proposed in this paper is simulated in a complex environment. The results reveal that the proposed algorithm can significantly improve the success rate and efficiency of the planning without losing other performance.

Path Planning of Industrial Robot Based on Improved RRT Algorithm in Complex Environments

In real industrial processes, both outliers and missing data are very common. Owing to the assumption that the data sampled from a normal process follow the Gaussian distribution, the regular data-driven process monitoring methods, such as the probabilistic partial least square (PPLS) method and the probabilistic principal component analysis method, are sensitive to outliers. By introducing heavy-tailed t distribution instead of Gaussian distribution to capture the distribution of normal data, the robust data-driven method can significantly reduce the influence of outliers on the development of the model. To reduce the influence of missing data, this paper proposes a process modeling and monitoring method with incomplete data based on the robust PPLS method. In the proposed method, to use more useful information in modeling, incomplete data along with complete data are employed in the parameter estimation using the maximum likelihood method; according to the robust PPLS model and the Bayes’ rule, the distributions of latent variables and missing data are derived, and subsequently, the expectation-maximization algorithm is used to achieve the parameter estimation. In addition, based on the conditional distribution of missing data, two monitoring indices are developed to evaluate the deviation of latent variables and residuals. A simulation case illustrates the application of the proposed method, and the results of application demonstrate its efficacy.

Junzhi Yu

Papers

Path Planning of Industrial Robot Based on Improved RRT Algorithm in Complex Environments

Process Modeling and Monitoring With Incomplete Data Based on Robust Probabilistic Partial Least Square Method