scholarly journals Improved Distorted Configuration Space Path Planning and Its Application to Robot Manipulators

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6060
Author(s):  
Yangmin Xie ◽  
Rui Zhou ◽  
Yusheng Yang
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Path Length ◽  
Robot Manipulators ◽  
Computational Cost ◽  
Computation Time ◽  
Planning Strategy ◽  
Automatic Planning ◽  
Searching Method ◽  
Space Method

Real-time obstacle avoidance path planning is critically important for a robot when it operates in a crowded or cluttered workspace. At the same time, the computational cost is a big concern once the degree of freedom (DOF) of a robot is high. A novel path planning strategy, the distorted configuration space (DC-space) method, was proposed and proven to outperform the traditional search-based methods in terms of computational efficiency. However, the original DC-space method did not sufficiently consider the demands on automatic planning, convex space preservation, and path optimization, which makes it not practical when applied to the path planning for robot manipulators. The treatments for the problems mentioned above are proposed in this paper, and their applicability is examined on a three DOFs robot. The experiments demonstrate the effectiveness of the proposed improved distorted configuration space (IDCS) method on rapidly finding an obstacle-free path. Besides, the optimized IDCS method is presented to shorten the generated path. The performance of the above algorithms is compared with the classic Rapidly-exploring Random Tree (RRT) searching method in terms of their computation time and path length.

scholarly journals Mobile Robot Path Planning Based on a Generalized Wavefront Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Sifan Wu ◽  
Yu Du ◽  
Yonghua Zhang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Path Length ◽  
Computation Time ◽  
Target Point ◽  
Multiple Target ◽  
Robot Path Planning ◽  
Practical Applications ◽  
Real Time Applications ◽  
Robot Path

This study develops a generalized wavefront algorithm for conducting mobile robot path planning. The algorithm combines multiple target point sets, multilevel grid costs, logarithmic expansion around obstacles, and subsequent path optimization. The planning performances obtained with the proposed algorithm, the A∗ algorithm, and the rapidly exploring random tree (RRT) algorithm optimized using a Bézier curve are compared using simulations with different grid map environments comprising different numbers of obstacles with varying shapes. The results demonstrate that the generalized wavefront algorithm generates smooth and safe paths around obstacles that meet the required kinematic conditions associated with the actual maneuverability of mobile robots and significantly reduces the planned path length compared with the results obtained with the A∗ algorithm and the optimized RRT algorithm with a computation time acceptable for real-time applications. Therefore, the generated path is not only smooth and effective but also conforms to actual robot maneuverability in practical applications.

Investigation of Branch Accessibility with a Robotic Pruner for Pruning Apple Trees

2021 ◽  
Vol 64 (5) ◽  
pp. 1459-1474
Author(s):  
Azlan Zahid ◽  
Long He ◽  
Daeun Choi ◽  
James Schupp ◽  
Paul Heinemann
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Path Length ◽  
Computation Time ◽  
Tree Canopy ◽  
Random Tree ◽  
Target Point ◽  
List Type ◽  
Apple Trees ◽  
End Effector

HighlightsA branch accessibility simulation was performed for robotic pruning of apple trees.A virtual tree environment was established using a kinematic manipulator model and an obstacle model.Rapidly-exploring random tree (RRT) was combined with smoothing and optimization for improved path planning.Effects on RRT path planning of the approach angle of the end-effector and cutter orientation at the target were studied.Abstract. Robotic pruning is a potential solution to reduce orchard labor and associated costs. Collision-free path planning of the manipulator is essential for successful robotic pruning. This simulation study investigated the collision-free branch accessibility of a six rotational (6R) degrees of freedom (DoF) robotic manipulator with a shear cutter end-effector. A virtual environment with a simplified tall spindle tree canopy was established in MATLAB. An obstacle-avoidance algorithm, rapidly-exploring random tree (RRT), was implemented for establishing collision-free paths to reach the target pruning points. In addition, path smoothing and optimization algorithms were used to reduce the path length and calculate the optimized path. Two series of simulations were conducted: (1) performance and comparison of the RRT algorithm with and without smoothing and optimization, and (2) performance of collision-free path planning considering different approach poses of the end-effector relative to the target branch. The simulations showed that the RRT algorithm successfully avoided obstacles and allowed the manipulator to reach the target point with 23 s average path finding time. The RRT path length was reduced by about 28% with smoothing and by 25% with optimization. The RRT smoothing algorithm generated the shortest path lengths but required about 1 to 3 s of additional computation time. The lowest coefficient of variation and standard deviation values were found for the optimization method, which confirmed the repeatability of the method. Considering the different end-effector approach poses, the simulations suggested that successfully finding a collision-free path was possible for branches with no existing path using the ideal (perpendicular cutter) approach pose. This study provides a foundation for future work on the development of robotic pruning systems. Keywords: Agricultural robotics, Collision-free path, Manipulator, Path planning, Robotic pruning, Virtual tree environment.

scholarly journals Efficient robotic path planning algorithm based on artificial potential field

2021 ◽  
Vol 11 (6) ◽  
pp. 4840
Author(s):  
Elia Nadira Sabudin ◽  
Rosli Omar ◽  
Sanjoy Kumar Debnath ◽  
Muhammad Suhaimi Sulong
Keyword(s):  
Computational Complexity ◽  
Path Planning ◽  
Potential Field ◽  
Path Length ◽  
Computation Time ◽  
Cell Decomposition ◽  
Target Point ◽  
Planning Methods ◽  
Planning Algorithm ◽  
Path Planning Algorithm

<span lang="EN-US">Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG.</span>

scholarly journals Ellipsoidal Path Planning for Unmanned Aerial Vehicles

2021 ◽  
Vol 11 (17) ◽  
pp. 7997
Author(s):  
Carlos Villaseñor ◽  
Alberto A. Gallegos ◽  
Gehova Lopez-Gonzalez ◽  
Javier Gomez-Avila ◽  
Jesus Hernandez-Barragan ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Mapping Algorithm ◽  
Planning Strategy ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Trained Neural Network ◽  
Path Planning Algorithm

The research in path planning for unmanned aerial vehicles (UAV) is an active topic nowadays. The path planning strategy highly depends on the map abstraction available. In a previous work, we presented an ellipsoidal mapping algorithm (EMA) that was designed using covariance ellipsoids and clustering algorithms. The EMA computes compact in-memory maps, but still with enough information to accurately represent the environment and to be useful for robot navigation algorithms. In this work, we develop a novel path planning algorithm based on a bio-inspired algorithm for navigation in the ellipsoidal map. Our approach overcomes the problem that there is no closed formula to calculate the distance between two ellipsoidal surfaces, so it was approximated using a trained neural network. The presented path planning algorithm takes advantage of ellipsoid entities to represent obstacles and compute paths for small UAVs regardless of the concavity of these obstacles, in a very geometrically explicit way. Furthermore, our method can also be used to plan routes in dynamical environments without adding any computational cost.

scholarly journals Comparison of different configuration space representations for path planning under combinatorial method

2019 ◽  
Vol 14 (1) ◽  
pp. 1 ◽  
Author(s):  
Sanjoy Kumar Debnath ◽  
Rosli Omar ◽  
Nor Badariyah Abdul Latip
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Autonomous Vehicle ◽  
Computation Time ◽  
Computational Time ◽  
Space Representation ◽  
Human Beings ◽  
Combinatorial Method ◽  
Algorithm Configuration ◽  
Representation Techniques

<span>The use of autonomous vehicle/robot has been adopted widely to replace human beings in performing dangerous missions in adverse environments. Keeping this in mind, path planning ensures that the autonomous vehicle must safely arrive to its destination with required criteria like lower computation time, shortest travelled path and completeness. There are few kinds of path planning strategies, such as combinatorial method, sampling based method and bio-inspired method. Among them, combinatorial method can accomplish couple of criteria without further adjustment in conventional algorithm. Configuration space provides detailed information about the position of all points in the system and it is the space for all configurations. Therefore, C-space denotes the actual free space zone for the movement of robot and guarantees that the vehicle or robot must not collide with the obstacle. This paper analyses different C-Space representation techniques under combinatorial method based on the past researches and their findings with different criteria such as optimality, completeness, safety, memory uses, real time and computational time etc. Visibility Graph has optimality which is a unique from other</span>

Reactive approach to on-line path planning for robot manipulators in dynamic environments

Robotica ◽  
2002 ◽  
Vol 20 (4) ◽  
pp. 375-384
Author(s):  
Margarita Mediavilla ◽  
José Luis González ◽  
Juan Carlos Fraile ◽  
José Ramón Perán
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Degrees Of Freedom ◽  
Robot Manipulators ◽  
Local Algorithms ◽  
Planning Stage ◽  
New Approach ◽  
On Line ◽  
Line Path

This paper describes a new approach to path planning of robot manipulators with many degrees of freedom. It is designed for on-line motion in dynamic and unpredictable environments. The robots react to moving obstacles using a local and reactive algorithm restricted to a subset of its configuration space. The lack of a long-term view of local algorithms (local minima problems) is solved using an off-line pre-planning stage that chooses the subset of the configuration space that minimises the probability of not finding collision free paths. The approach is implemented and tested on a system of three Scorbot-er IX five link robots.

scholarly journals COMPUTATIONALLY EFFICIENT PATH PLANNING ALGORITHM FOR AUTONOMOUS VEHICLE

2020 ◽  
Vol 83 (1) ◽  
pp. 133-143
Author(s):  
Sanjoy Kumar Debnath ◽  
Rosli Omar ◽  
Nor Badariyah Abdul Latip ◽  
Susama Bagchi ◽  
Elia Nadira Sabudin ◽  
...  
Keyword(s):  
Path Planning ◽  
Performance Assessment ◽  
Free Path ◽  
Path Length ◽  
Autonomous Vehicle ◽  
Computation Time ◽  
Visibility Graph ◽  
Computationally Efficient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper analyses an experimental path planning performance between the Iterative Equilateral Space Oriented Visibility Graph (IESOVG) and conventional Visibility Graph (VG) algorithms in terms of computation time and path length for an autonomous vehicle. IESOVG is a path planning algorithm that was proposed to overcome the limitations of VG which is slow in obstacle-rich environment. The performance assessment was done in several identical scenarios through simulation. The results showed that the proposed IESOVG algorithm was much faster in comparison to VG. In terms of path length, IESOVG was found to have almost similar performance with VG.  It was also found that IESOVG was complete as it could find a collision-free path in all scenarios.

Sign in / Sign up

Export Citation Format

Share Document