Stereo Camera Based Real-Time Local Path-Planning for Mobile Robots

The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.

Download Full-text

Weighted virtual tangential vector algorithm for local path planning of mobile robots

Electronics Letters ◽

10.1049/el.2012.3893 ◽

2013 ◽

Vol 49 (4) ◽

pp. 255-256 ◽

Cited By ~ 6

Author(s):

Kyung Woon Kwak ◽

Kyung‐Soo Kim ◽

Soohyun Kim

Keyword(s):

Path Planning ◽

Mobile Robots ◽

Local Path Planning ◽

Tangential Vector ◽

Local Path

Download Full-text

2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots

Journal of Advanced Transportation ◽

10.1155/2020/8867937 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Xuexi Zhang ◽

Jiajun Lai ◽

Dongliang Xu ◽

Huaijun Li ◽

Minyue Fu

Keyword(s):

Path Planning ◽

Mobile Robots ◽

Real Time ◽

Obstacle Avoidance ◽

Basic System ◽

Rescue Robot ◽

Planning Algorithm ◽

Local Path ◽

Slam Algorithm ◽

Path Planning Algorithm

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.

Download Full-text

1D virtual force field algorithm for reflexive local path planning of mobile robots

Electronics Letters ◽

10.1049/el.2014.2787 ◽

2014 ◽

Vol 50 (20) ◽

pp. 1429-1430 ◽

Cited By ~ 2

Author(s):

Tok‐Son Choe ◽

Jin‐Bae Park ◽

Sang‐Hyun Joo ◽

Yong‐Woon Park

Keyword(s):

Path Planning ◽

Force Field ◽

Mobile Robots ◽

Local Path Planning ◽

Virtual Force ◽

Local Path

Download Full-text

Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

Sensors ◽

10.3390/s21165547 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5547

Author(s):

Younes Al Younes ◽

Martin Barczyk

Keyword(s):

Path Planning ◽

Motion Planning ◽

Real Time ◽

Nonlinear Model ◽

Feedback Linearization ◽

Autonomous Vehicle ◽

Flight Test ◽

Local Path Planning ◽

Planning Approach ◽

Local Path

Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.

Download Full-text