Real-time torque control of nonholonomic mobile robots with obstacle avoidance

SUMMARYA real-time obstacle avoidance algorithm is proposed for autonomous mobile robots. The algorithm is sensor-based and consists of a H-mode and T-mode. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. It will be shown that the algorithm is more efficient and more robust than other sensor-based algorithms. In addition, the algorithm will guarantee a solution for the obstacle avoidance problem. Since the algorithm only takes up a small computational time, it can be implemented in real time.

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Low-cost, real-time obstacle avoidance for mobile robots

2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC) ◽

10.1109/ccwc.2017.7868418 ◽

2017 ◽

Cited By ~ 2

Author(s):

Shawn Ricardo ◽

Doina Bein ◽

Anand Panagadan

Keyword(s):

Mobile Robots ◽

Real Time ◽

Obstacle Avoidance ◽

Low Cost

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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.

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