scholarly journals Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4156
Author(s):  
Luís B. P. Nascimento ◽  
Dennis Barrios-Aranibar ◽  
Vitor G. Santos ◽  
Diego S. Pereira ◽  
William C. Ribeiro ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Configuration Space ◽  
Autonomous Robot ◽  
Breadth First Search ◽  
Propagation Process ◽  
Robot Systems ◽  
And Performance ◽  
Safe Path ◽  
High Clearance

The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.

scholarly journals Environment Mapping Robot Using Supersonic Sensor

1999 ◽  
Vol 11 (6) ◽  
pp. 468-472
Author(s):  
Masafumi Uchida ◽  
◽  
Tanaka Hisaya ◽  
Hideto Ide ◽  
Keyword(s):  
Computer Simulation ◽  
Path Planning ◽  
Autonomous Robot ◽  
Working Environment ◽  
Occupancy Grid ◽  
Environment Map ◽  
Planning Algorithm ◽  
And Performance ◽  
Path Planning Algorithm ◽  
Range Sensors

We studied an automapping algorithm for an autonomous robot having ultrasonic range sensors. A robot with a working environment map operates smoothly. The robot consisted of an automapping algorithm using ultrasonic range sensors and a path planning algorithm. Ultrasonic range sensors are basic, inexpensive, and compact. We proposed an automapping algorithm introducing a parameter, valid length, for a robot with ultrasonic range sensors. The map was based on an occupancy grid. Computer simulation confirmed the effectiveness of introducing valid length in mapping by an autonomous robot. We discuss proposed distinctions and performance.

scholarly journals A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 945 ◽  
Author(s):  
Iram Noreen ◽  
Amna Khan ◽  
Khurshid Asghar ◽  
Zulfiqar Habib
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Execution Time ◽  
Path Length ◽  
Optimal Path ◽  
Performance Comparison ◽  
Computational Time ◽  
And Performance ◽  
Commercial Applications ◽  
Space Requirements

With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the total number of turns. MEA* is an efficient variation of A* for optimal path-planning of mobile robots. RRT*-AB is a sampling-based planner with rapid convergence rate, and improved time and space requirements than other sampling-based methods such as RRT*. The purpose of this paper is the review and performance comparison of these planners based on metrics, i.e., path length, execution time, and memory requirements. All planners are tested in structured and complex unstructured environments cluttered with obstacles. Performance plots and statistical analysis have shown that MEA* requires less memory and computational time than other planners. These advantages of MEA* make it suitable for off-line applications using small robots with constrained power and memory resources. Moreover, performance plots of path length of MEA* is comparable to RRT*-AB with less execution time in the 2D environment. However, RRT*-AB will outperform MEA* in high-dimensional problems because of its inherited suitability for complex problems.

Path Planning for Mobile Robots in 3D Dynamic Environments

2011 ◽  
Vol 403-408 ◽  
pp. 1401-1404
Author(s):  
Li Jia Chen ◽  
He Jin ◽  
Jin Ke Bai ◽  
Hai Tao Mao
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Artificial Potential Field ◽  
Uniform Motion ◽  
Breadth First Search ◽  
Time Step ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Aiming at the robustness of the path planning of mobile robots in the 3D dynamic environment, an improved ARF (Artificial Potential Field) based path planning algorithm is proposed in this paper. Supposing that all the obstacles move regularly and the robot is on uniform motion in a grid 3D environment. Firstly, the algorithm computes the future statuses of the environment, such as the coordinate of all the obstacles and the goal, until a time step T in which there is at least one route between the start and goal. T is obtained by BFS (Breadth First Search) and environment configuration parameters. Secondly, because in every time step the environment can be consider as being static, ARF is used to determine the potential value of every space position in each time step. Finally, a route along the lowest potential values is found for the robot from the start to goal. Simulation results show that the algorithm makes the robot avoid obstacles effectively and reach the goal safely.

Safe Path Planning for Mobile Robots

Robotics 98 ◽  
1998 ◽  
Author(s):  
Alain Lambert ◽  
Nadine Le Fort Piat
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Safe Path

Comparison between Waveform and Bug Path Planning Algorithm for Mobile Robot

2014 ◽  
Vol 607 ◽  
pp. 774-777
Author(s):  
Swee Ho Tang ◽  
Che Fai Yeong ◽  
Eileen Lee Ming Su
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Execution Time ◽  
Path Length ◽  
Simulation Software ◽  
Breadth First Search ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Grid Based

Mobile robots frequently find themselves in a circumstance where they need to find a trajectory to another position in their environment, subject to constraints postured by obstacles and the capabilities of the robot itself. This study compared path planning algorithms for mobile robots to move efficiently in a collision free grid based static environment. Two algorithms have been selected to do the comparison namely wavefront algorithm and bug algorithm. The wavefront algorithm involves a breadth-first search of the graph beginning at the goal position until it reaches the start position. The bug algorithm uses obstacles borders as guidance toward a goal with restricted details about the environment. The algorithms are compared in terms of parameters such as execution time of the algorithm and planned path length by using Player/Stage simulation software. Results shown that wavefront algorithm is a better path planning algorithm compared to bug algorithm in static environment.

A Hybrid Fireworks Algorithm to Navigation and Mapping

2020 ◽  
pp. 213-232
Author(s):  
Tingjun Lei ◽  
Chaomin Luo ◽  
John E. Ball ◽  
Zhuming Bi
Keyword(s):  
Artificial Intelligence ◽  
Path Planning ◽  
Mobile Robots ◽  
Robot Navigation ◽  
Optimal Path ◽  
Autonomous Robot ◽  
Mobile Robot Navigation ◽  
Fireworks Algorithm ◽  
Starting Point ◽  
Autonomous Robot Navigation

In recent years, computer technology and artificial intelligence have developed rapidly, and research in the field of mobile robots has continued to deepen with development of artificial intelligence. Path planning is an essential content of mobile robot navigation of computing a collision-free path between a starting point and a goal. It is necessary for mobile robots to move and maneuver in different kinds of environment with objects and obstacles. The main goal of path planning is to find the optimal path between the starting point and the target position in the minimal possible time. A new firework algorithm (FWA) integrated with a graph theory, Dijkstra's algorithm developed for autonomous robot navigation, is proposed in this chapter. The firework algorithm is improved by a local search procedure that a LIDAR-based local navigator algorithm is implemented for local navigation and obstacle avoidance. The grid map is utilized for real-time intelligent robot mapping and navigation. In this chapter, both simulation and comparison studies of an autonomous robot navigation demonstrate that the proposed model is capable of planning more reasonable and shorter, collision-free paths in non-stationary and unstructured environments compared with other approaches.

Sign in / Sign up

Export Citation Format

Share Document