scholarly journals A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without Occlusions

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 185408-185421
Author(s):  
Chang Chen ◽  
Hua Zhu ◽  
Lei Wang ◽  
Yu Liu
Keyword(s):  
Mobile Robots ◽  
Unknown Environments

scholarly journals Edge-weighted consensus-based formation control strategy with collision avoidance

Robotica ◽  
2014 ◽  
Vol 33 (2) ◽  
pp. 332-347 ◽  
Author(s):  
Riccardo Falconi ◽  
Lorenzo Sabattini ◽  
Cristian Secchi ◽  
Cesare Fantuzzi ◽  
Claudio Melchiorri
Keyword(s):  
Mobile Robots ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Control Strategy ◽  
Formation Control ◽  
Weighted Graphs ◽  
Wheeled Robots ◽  
Unknown Environments ◽  
Multi Robot

SUMMARYIn this paper, a consensus-based control strategy is presented to gather formation for a group of differential-wheeled robots. The formation shape and the avoidance of collisions between robots are obtained by exploiting the properties of weighted graphs. Since mobile robots are supposed to move in unknown environments, the presented approach to multi-robot coordination has been extended in order to include obstacle avoidance. The effectiveness of the proposed control strategy has been demonstrated by means of analytical proofs. Moreover, results of simulations and experiments on real robots are provided for validation purposes.

scholarly journals Learning Innovative Routes for Mobile Robots in Dynamic Partially Unknown Environments

10.5772/5787 ◽  
2005 ◽  
Vol 2 (3) ◽  
pp. 21
Author(s):  
Kristo Heero ◽  
Alvo Aabloo ◽  
Maarja Kruusmaa
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Shortest Path ◽  
Dynamic Environments ◽  
Distance Transform ◽  
Test Results ◽  
Approach To Learning ◽  
Planning Strategy ◽  
Unknown Environments ◽  
Planning Strategies

This paper examines path planning strategies in partially unknown dynamic environemnts and introduces an approach to learning innovative routes. The approach is verified against shortest path planning with a distance transform algorithm, local and global replanning and suboptimal route following in unknown, partially unknown, static and dynamic environments. We show that the learned routes are more reliable and when traversed repeatedly the robot's behaviour becomes more predictable. The test results also suggest that the robot's behaviour depends on knowledge about the environemnt but not about the path planning strategy used.

NRR: a nonholonomic random replanner for navigation of car-like robots in unknown environments

Robotica ◽  
2014 ◽  
Vol 32 (7) ◽  
pp. 1101-1123 ◽  
Author(s):  
Ellips Masehian ◽  
Hossein Kakahaji
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Random Tree ◽  
Local Motion ◽  
Goal Region ◽  
Unknown Environments ◽  
Effectiveness And Efficiency ◽  
Path Lengths

SUMMARYIn this paper, a new sensor-based approach called nonholonomic random replanner (NRR) is presented for motion planning of car-like mobile robots. The robot is incrementally directed toward its destination using a nonholonomic rapidly exploring random tree (RRT) algorithm. At each iteration, the robot's perceived map of the environment is updated using sensor readings and is used for local motion planning. If the goal was not visible to the robot, an approximate path toward the goal is calculated and the robot traces it to an extent within its sensor range. The robot updates its motion to goal through replanning. This procedure is repeated until the goal lies within the scope of the robot, after which it finds a more precise path by sampling in a tighter Goal Region for the nonholonomic RRT. Three main replanning strategies are proposed to decide when to perform a visibility scan and when to replan a new path. Those are named Basic, Deliberative and Greedy strategies, which yield different paths. The NRR was also modified for motion planning of Dubin's car-like robots. The proposed algorithm is probabilistically complete and its effectiveness and efficiency were tested by running several simulations and the resulting runtimes and path lengths were compared to the basic RRT method.

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