scholarly journals PGD Variational vademecum for robot motion planning. A dynamic obstacle case

2018 ◽  
Author(s):  
L. Hilario ◽  
N. Montés ◽  
E Nadal ◽  
M.C. Mora ◽  
A. Falco ◽  
...  
Keyword(s):  
Motion Planning ◽  
Laplace Equation ◽  
Potential Field ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Diffusion Term ◽  
Dynamic Obstacle ◽  
First Time ◽  
Robot Path ◽  
Motion Planning Problem

A fundamental robotics task is to plan collision-free motions for complex bodies from a start to a goal position among a set of static and dynamic obstacles. This problem is well known in the literature as motion planning (or the piano mover's problem). The complexity of the problem has motivated many works in the field of robot path planning. One of the most popular algorithms is the Artificial Potential Field technique (APF). This method defines an artificial potential field in the configuration space (C-space) that produces a robot path from a start to a goal position. This technique is very fast for RT applications. However, the robot could be trapped in a deadlock (local minima of the potential function). The solution of this problem lies in the use of harmonic functions in the generation of the potential field, which satisfy the Laplace equation. Unfortunately, this technique requires a numerical simulation in a discrete mesh, making useless for RT applications. In our previous work, it was presented for the first time, the Proper Generalized Decomposition method to solve the motion planning problem. In that work, the PGD was designed just for static obstacles and computed as a vademecum for all Start and Goal combinations. This work demonstrates that the PGD could be a solution for the motion planning problem. However, in a realistic scenario, it is necessary to take into account more parameters like for instance, dynamic obstacles. The goal of the present paper is to introduce a diffusion term into the Laplace equation in order to take into account dynamic obstacles as an extra parameter. Both cases, isotropic and non-isotropic cases are into account in order to generalize the solution.

Time-variant artificial potential field (TAPF): a breakthrough in power-optimized motion planning of autonomous space mobile robots

Robotica ◽  
2014 ◽  
Vol 34 (5) ◽  
pp. 1128-1150 ◽  
Author(s):  
Matin Macktoobian ◽  
Mahdi Aliyari Shoorehdeli
Keyword(s):  
Motion Planning ◽  
Potential Field ◽  
Structural Parameters ◽  
Potential Fields ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Test Bed ◽  
Smart Control ◽  
Fuzzy Techniques ◽  
Motion Planning Problem

SUMMARYIn this paper, a novel scheme is presented to conquer the motion-planning problem for autonomous space robots. Minimizing the consumed energy of atomic batteries within the daily planetary missions of robot on the planet is taken into account, i.e., utilization of the generated solar power by its embedded photocells leads to saving energy of batteries for night missions. Aforementioned objective could be acquired by appropriate interaction of motion planning paradigm with shadows of obstacles. Modeling of the shadow with the proposed artificial potential field leads to generalize the concept of potential fields not only for static and dynamic obstacles but also for being confronted with the intrinsic time-variant phenomena such as shadows. With due attention to the noticeable computational complexity of the introduced strategy, fuzzy techniques are applied to optimize the sampling times effectively. To accomplish this objective, a smart control scheme based on the fuzzy logic is mounted to the primitive version of algorithm. Regarding the need to identify some structural parameters of obstacles, PIONEER™ mobile robot is designed as a test bed for the verification of simulated results. Investigation on empirical accomplishments shows that the goal-oriented definition of Time–Variant Artificial Potential Fields is able to resolve the motion-planning problem in planetary applications.

scholarly journals A New Online Random Particles Optimization Algorithm for Mobile Robot Path Planning in Dynamic Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Behrang Mohajer ◽  
Kourosh Kiani ◽  
Ehsan Samiei ◽  
Mostafa Sharifi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Potential Field ◽  
Optimal Path ◽  
Target Position ◽  
Path Selection ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Robot Path

A new algorithm named random particle optimization algorithm (RPOA) for local path planning problem of mobile robots in dynamic and unknown environments is proposed. The new algorithm inspired from bacterial foraging technique is based on particles which are randomly distributed around a robot. These particles search the optimal path toward the target position while avoiding the moving obstacles by getting help from the robot’s sensors. The criterion of optimal path selection relies on the particles distance to target and Gaussian cost function assign to detected obstacles. Then, a high level decision making strategy will decide to select best mobile robot path among the proceeded particles, and finally a low level decision control provides a control signal for control of considered holonomic mobile robot. This process is implemented without requirement to tuning algorithm or complex calculation, and furthermore, it is independent from gradient base methods such as heuristic (artificial potential field) methods. Therefore, in this paper, the problem of local mobile path planning is free from getting stuck in local minima and is easy computed. To evaluate the proposed algorithm, some simulations in three various scenarios are performed and results are compared by the artificial potential field.

Soccer Robot Path Planning Based on Evolutionary Artificial Field

2012 ◽  
Vol 562-564 ◽  
pp. 955-958 ◽  
Author(s):  
Zai Xin Liu ◽  
Long Xiang Yang ◽  
Jin Ge Wang
Keyword(s):  
Path Planning ◽  
Success Rate ◽  
Potential Field ◽  
Field Model ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Soccer Robot ◽  
Field Function ◽  
Robot Path

To improve the success rate of Soccer Robot Path Planning, artificial potential field is amended, autonomous potential field is presented to solve the path planning problem by analyzing shortcomings of the basic shooting algorithm, the autonomous potential field function centering on the soccer robot is constructed, and the robot’s movement in the new potential field is analyzed, the modified artificial potential field model and autonomous potential field model is contrasted, each vicinal potential energy of the modified artificial potential field model and autonomous potential field model is analyzed. The simulated results demonstrate that this method can optimize the path of a soccer robot, decrease the complexity, enhance the real time capability, perform the shooting action better, and improve the success rate of a soccer robot shooting a goal.

Route planning of intelligent bridge cranes based on an improved artificial potential field method

2021 ◽  
pp. 1-8
Author(s):  
Zhengyan Chang ◽  
Zhengwei Zhang ◽  
Qiang Deng ◽  
Zheren Li
Keyword(s):  
Potential Field ◽  
Autonomous Navigation ◽  
Route Planning ◽  
Simple Algorithm ◽  
Field Method ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Operating Characteristics ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The artificial potential field method is usually applied to the path planning problem of driverless cars or mobile robots. For example, it has been applied for the obstacle avoidance problem of intelligent cars and the autonomous navigation system of storage robots. However, there have been few studies on its application to intelligent bridge cranes. The artificial potential field method has the advantages of being a simple algorithm with short operation times. However, it is also prone to problems of unreachable targets and local minima. Based on the analysis of the operating characteristics of bridge cranes, a two-dimensional intelligent running environment model of a bridge crane was constructed in MATLAB. According to the basic theory of the artificial potential field method, the double-layer artificial potential field method was deduced, and the path and track fuzzy processing method was proposed. These two methods were implemented in MATLAB simulations. The results showed that the improved artificial potential field method could avoid static obstacles efficiently.

Motion Planning of a Nonholonomic Multibody System Using Genetic Algorithm

2003 ◽  
Author(s):  
Xin-Sheng Ge ◽  
Li-Qun Chen
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Nonholonomic System ◽  
Multibody System ◽  
Planning Problem ◽  
Control Approach ◽  
Velocity Constraints ◽  
Nonholonomic Motion Planning ◽  
Optimal Control Approach ◽  
Motion Planning Problem

The motion planning problem of a nonholonomic multibody system is investigated. Nonholonomicity arises in many mechanical systems subject to nonintegrable velocity constraints or nonintegrable conservation laws. When the total angular momentum is zero, the control problem of system can be converted to the motion planning problem for a driftless control system. In this paper, we propose an optimal control approach for nonholonomic motion planning. The genetic algorithm is used to optimize the performance of motion planning to connect the initial and final configurations and to generate a feasible trajectory for a nonholonomic system. The feasible trajectory and its control inputs are searched through a genetic algorithm. The effectiveness of the genetic algorithm is demonstrated by numerical simulation.

Motion planning problem with obstacle avoidance for step-2 bracket-generating systems

PAMM ◽  
2017 ◽  
Vol 17 (1) ◽  
pp. 799-800 ◽  
Author(s):  
Victoria Grushkovskaya ◽  
Alexander Zuyev
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Planning Problem ◽  
Motion Planning Problem

Solving the motion planning problem by using neural networks

Robotica ◽  
1994 ◽  
Vol 12 (4) ◽  
pp. 323-333 ◽  
Author(s):  
R.H.T. Chan ◽  
P.K.S. Tam ◽  
D.N.K. Leung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Planning ◽  
Configuration Space ◽  
Logic Gates ◽  
Moving Object ◽  
Planning Problem ◽  
Processing Unit ◽  
Configuration Point ◽  
Motion Planning Problem

SUMMARYThis paper presents a new neural networks-based method to solve the motion planning problem, i.e. to construct a collision-free path for a moving object among fixed obstacles. Our ‘navigator’ basically consists of two neural networks: The first one is a modified feed-forward neural network, which is used to determine the configuration space; the moving object is modelled as a configuration point in the configuration space. The second neural network is a modified bidirectional associative memory, which is used to find a path for the configuration point through the configuration space while avoiding the configuration obstacles. The basic processing unit of the neural networks may be constructed using logic gates, including AND gates, OR gates, NOT gate and flip flops. Examples of efficient solutions to difficult motion planning problems using our proposed techniques are presented.

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