scholarly journals Uncertain estimation-based motion-planning algorithms for mobile robots

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 51
Author(s):  
Zoltán Bálint Gyenes ◽  
Emese Gincsainé Szádeczky-Kardoss
Keyword(s):  
Motion Planning ◽  
Mobile Agents ◽  
Dynamic Environment ◽  
Target Position ◽  
Free Motion ◽  
Artificial Potential Field ◽  
Moving Obstacles ◽  
Other Information ◽  
Planning Algorithms ◽  
Velocity Obstacle

Collision-free motion planning for mobile agents is a challenging task, especially when the robot has to move towards a target position in a dynamic environment. The main aim of this paper is to introduce motion-planning algorithms using the changing uncertainties of the sensor-based data of obstacles. Two main algorithms are presented in this work. The first is based on the well-known velocity obstacle motion-planning method. In this method, collision-free motion must be achieved by the algorithm using a cost-function-based optimisation method. The second algorithm is an extension of the often-used artificial potential field. For this study, it is assumed that some of the obstacle data (e.g. the positions of static obstacles) are already known at the beginning of the algorithm (e.g. from a map of the enviroment), but other information (e.g. the velocity vectors of moving obstacles) must be measured using sensors. The algorithms are tested in simulations and compared in different situations.

Motion Planning of Mobile Robots for Occluded Obstacles

2018 ◽  
Vol 30 (3) ◽  
pp. 485-492
Author(s):  
Satoshi Hoshino ◽  
◽  
Tomoki Yoshikawa
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Dynamic Environments ◽  
Simulation Experiments ◽  
Moving Obstacles ◽  
Moving Direction ◽  
Velocity Obstacle

Motion planning of mobile robots for occluded obstacles is a challenge in dynamic environments. The occlusion problem states that if an obstacle suddenly appears from the occluded area, the robot might collide with the obstacle. To overcome this, we propose a novel motion planner, the Velocity Obstacle for occlusion (VOO). The VOO is based on a previous motion planner, the Velocity Obstacle (VO), which is effective for moving obstacles. In the proposed motion planner, information uncertainties about occluded obstacles, such as position, velocity, and moving direction, are quantitatively addressed. Thus, the robot based on the VOO is able to move not only among observed obstacles, but also among the occluded ones. Through simulation experiments, the effectiveness of the VOO for the occlusion problem is demonstrated by comparison with the VO.

Task-Constrained Optimal Motion Planning of Redundant Robots Via Sequential Expanded Lagrangian Homotopy

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Audelia G. Dharmawan ◽  
Shaohui Foong ◽  
Gim Song Soh
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Computational Time ◽  
Mobile Manipulator ◽  
Optimal Motion ◽  
Task Constraints ◽  
Moving Obstacles ◽  
Time Motion ◽  
Optimal Motion Planning

Real-time motion planning of robots in a dynamic environment requires a continuous evaluation of the determined trajectory so as to avoid moving obstacles. This is even more challenging when the robot also needs to perform a task optimally while avoiding the obstacles due to the limited time available for generating a new collision-free path. In this paper, we propose the sequential expanded Lagrangian homotopy (SELH) approach, which is capable of determining the globally optimal robot's motion sequentially while satisfying the task constraints. Through numerical simulations, we demonstrate the capabilities of the approach by planning an optimal motion of a redundant mobile manipulator performing a complex trajectory. Comparison against existing optimal motion planning approaches, such as genetic algorithm (GA) and neural network (NN), shows that SELH is able to perform the planning at a faster rate. The considerably short computational time opens up an opportunity to apply this method in real time; and since the robot's motion is planned sequentially, it can also be adjusted to accommodate for dynamically changing constraints such as moving obstacles.

Collision-free motion planning of a virtual arm based on the FABRIK algorithm

Robotica ◽  
2016 ◽  
Vol 35 (6) ◽  
pp. 1431-1450 ◽  
Author(s):  
Songqiao Tao ◽  
Yumeng Yang
Keyword(s):  
Motion Planning ◽  
Inverse Kinematics ◽  
Target Position ◽  
Processing Technique ◽  
Free Motion ◽  
Local Optimum ◽  
Post Processing ◽  
Angle Space ◽  
Random Algorithm ◽  
Rotation Strategy

SUMMARYCollision-free motion planning of a virtual arm is an intractable task in high-interference environments. In this paper, an approach for collision-free motion planning of a virtual arm based on the forward and backward reaching inverse kinematics (FABRIK) algorithm is proposed. First, a random rotation strategy and local optimum-seeking technology are introduced to improve the FABRIK algorithm in order to avoid obstacles. The improvement FABRIK algorithm is used to design the final grasping posture of a virtual arm according the target position. Then, a bidirectional rapidly exploring random tree (Bi-RRT) algorithm is adopted to explore the process postures from a given initial posture to the final grasping posture. Different from the existing method, the proposed Bi-RRT algorithm in this paper plans the motions of a virtual arm in a seven-dimensional angle space, and the final grasping posture is automatically designed using the obstacle-avoidance FABRIK algorithm instead of the manual design. Finally, the post-processing technique is introduced to remove redundant nodes from the planned motions. This procedure has resolved the problem that the Bi-RRT algorithm is a random algorithm. The experimental results show the proposed method for collision-free motion planning of a virtual arm is feasible.

Intelligent Mobile Robot Navigation in an Uncertain Dynamic Environment

2013 ◽  
Vol 367 ◽  
pp. 388-392 ◽  
Author(s):  
Aydin Azizi ◽  
Farshid Entesari ◽  
Kambiz Ghaemi Osgouie ◽  
Mostafa Cheragh
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Dynamic Environment ◽  
Mobile Robot Navigation ◽  
Moving Target ◽  
Fuzzy Decision ◽  
Moving Obstacles ◽  
Navigation Algorithm ◽  
Modified Sensor ◽  
Velocity Obstacle

This paper presents a modified sensor-based online method for mobile robot navigation generating paths in dynamic environments. The core of the navigation algorithm is based on the velocity obstacle avoidance method and the guidance-based tracking algorithm. A fuzzy decision maker is designed to combine the two mentioned algorithms intelligently. Hence the robot will be able to decide intelligently in various situations when facing the moving obstacles and moving target. A noble noise cancellation algorithm using Neural Network is designed to navigate the robot in an uncertain dynamic environment safely. The results show that the robot can track a moving target while maneuvering safely in dynamic environment and avoids stationary and moving obstacles.

Obstacle avoidance motion planning for mobile robots in a dynamic environment with moving obstacles

Robotica ◽  
1997 ◽  
Vol 15 (5) ◽  
pp. 493-510 ◽  
Author(s):  
Chia-Pin Wu ◽  
Tsu-Tian Lee ◽  
Chau-Ren Tsai
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Function Method ◽  
Dynamic Environment ◽  
Vector Form ◽  
Distance Information ◽  
Moving Obstacles ◽  
Simulation Results ◽  
Safe Path

A new real-time obstacle avoidance method for mobile robots has been developed. This method, namely the vector-distance function method, permits the detection of obstacles (both moving and stationary) and generates a path that can avoid collisions. The proposed approach expresses the distance information in a vector form. Then the notion of weighting is introduced to describe relationship between sensors of mobile robots and the target to be reached. Furthermore, R-mode, L-mode and T-mode are introduced to generate a safe path for the mobile robot in a dynamic environment filled with both stationary and moving obstacles. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. Simulation results are included to demonstrate the applicability and effectiveness of the developed algorithm.

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