scholarly journals Multirobot Formation with Sensor Fusion-Based Localization in Unknown Environment

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1788
Author(s):  
Anh Vu Le ◽  
Koppaka Ganesh Sai Apuroop ◽  
Sriniketh Konduri ◽  
Huy Do ◽  
Mohan Rajesh Elara ◽  
...  
Keyword(s):  
Path Planning ◽  
Sensor Fusion ◽  
Measurement Unit ◽  
Multiple Robots ◽  
Unknown Environment ◽  
Formation Tracking ◽  
Space Experiments ◽  
Planning Algorithm ◽  
Planning Technique ◽  
Path Planning Algorithm

Multirobot cooperation enhancing the efficiency of numerous applications such as maintenance, rescue, inspection in cluttered unknown environments is the interesting topic recently. However, designing a formation strategy for multiple robots which enables the agents to follow the predefined master robot during navigation actions without a prebuilt map is challenging due to the uncertainties of self-localization and motion control. In this paper, we present a multirobot system to form the symmetrical patterns effectively within the unknown environment deployed randomly. To enable self-localization during group formatting, we propose the sensor fusion system leveraging sensor fusion from the ultrawideband-based positioning system, Inertial Measurement Unit orientation system, and wheel encoder to estimate robot locations precisely. Moreover, we propose a global path planning algorithm considering the kinematic of the robot’s action inside the workspace as a metric space. Experiments are conducted on a set of robots called Falcon with a conventional four-wheel skid steering schematic as a case study to validate our proposed path planning technique. The outcome of our trials shows that the proposed approach produces exact robot locations after sensor fusion with the feasible formation tracking of multiple robots system on the simulated and real-world experiments.

Radom Intermediate Points Based Path Planning in Unknown Environment

2013 ◽  
Vol 325-326 ◽  
pp. 1688-1691
Author(s):  
Qi Lei Xu ◽  
Gong You Tang ◽  
Hai Lin Liu ◽  
Shao Ting Ge ◽  
Xue Yang
Keyword(s):  
Path Planning ◽  
Mobile Agent ◽  
Rotation Angle ◽  
Sensor Data ◽  
Unknown Environment ◽  
Current Path ◽  
Movement Distance ◽  
Planning Algorithm ◽  
Simulation Results ◽  
Path Planning Algorithm

This paper presents RandomBug, a novel real-time path planning algorithm, for an autonomous mobile agent in completely unknown environment. According to this algorithm, all the planned paths are described and stored in the form of vectors. When the agent moves along the planned paths, it only considers the rotation angle and the movement distance in a single direction. The algorithm combines range sensor data with a safety radius to determine the blocking obstacles and calculate the shorter path by choosing the random intermediate points. When there is obstacle blocking in the current path, the intermediate points will be calculated randomly and the planned path will be regenerated by inserting the selected random intermediate points. Simulation results are given to show the effectiveness of the proposed algorithm.

scholarly journals RandomBug: Novel Path Planning Algorithm in Unknown Environment

2014 ◽  
Vol 8 (1) ◽  
pp. 252-257 ◽  
Author(s):  
Qi-lei Xu
Keyword(s):  
Path Planning ◽  
Mobile Agent ◽  
Rotation Angle ◽  
Sensor Data ◽  
Unknown Environment ◽  
Current Path ◽  
Movement Distance ◽  
Planning Algorithm ◽  
Simulation Results ◽  
Path Planning Algorithm

This paper presents a novel real-time path planning algorithm for an autonomous mobile agent in completely unknown environment. In this algorithm, all the planned paths are described and stored in the form of vectors in the algorithm. Only the rotation angle and the movement distance in a single direction are considered when the autonomous moves along the planned paths. The algorithm combines range sensor data with a safety radius, which determines the blocking obstacles and calculates a shorter path by choosing the random intermediate points. These random intermediate points are be generated when blocking obstacles exist in the current path. Then the optimal intermediate points are selected and inserted into the current path to regenerate a new planned path. Simulation results are shown that the proposed algorithm is effective.

scholarly journals A Bi-Level Probabilistic Path Planning Algorithm for Multiple Robots with Motion Uncertainty

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jingchuan Wang ◽  
Ruochen Tai ◽  
Jingwen Xu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Conflict Avoidance ◽  
System Efficiency ◽  
Multiple Robots ◽  
Time Performance ◽  
Planning Algorithm ◽  
Probabilistic Path Planning ◽  
Simulation Results ◽  
Path Planning Algorithm

For improving the system efficiency when there are motion uncertainties among robots in the warehouse environment, this paper proposes a bi-level probabilistic path planning algorithm. In the proposed algorithm, the map is partitioned into multiple interconnected districts and the architecture of proposed algorithm is composed of topology level and route level generating from above map: in the topology level, the order of passing districts is planned combined with the district crowdedness to achieve the district equilibrium and reduce the influence of robots under motion uncertainty. And in the route level, a MDP method combined with probability of motion uncertainty is proposed to plan path for all robots in each district separately. At the same time, the number of steps for each planning is dependent on the probability to decrease the number of planning. The conflict avoidance is proved, and optimization is discussed for the proposed algorithm. Simulation results show that the proposed algorithm achieves improved system efficiency and also has acceptable real-time performance.

Collision-free motion planning of multiarm robots using evolutionary algorithms

1997 ◽  
Vol 211 (5) ◽  
pp. 373-384 ◽  
Author(s):  
A S Rana ◽  
A M S Zalzala
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Population Based ◽  
Operational Space ◽  
Planning Algorithm ◽  
Planning Technique ◽  
Pass Through ◽  
Population Based Search ◽  
Path Planning Algorithm ◽  
Path Modification

This paper presents an evolutionary algorithm for the collision-free path planning of multiarm robots. A global path planning technique is used where the paths are represented by a string of via-points that the robots have to pass through, connected together by cubic spline polynomials. Since the entire paths of the robots are considered for optimization, the problem of deadlock between the arms and the static obstacles does not occur. Repeated path modification is done through evolutionary techniques to find an optimized path with respect to length and collision among the robots and the obstacles and the robots themselves. The proposed algorithm departs from simple genetic algorithms in that floating point vector strings represent the chromosomes and customized operators are used to improve upon the performance of the search. Moreover, a local search is carried out on each individual in addition to the global population based search. The result is a highly efficient path-planning algorithm that can deal with complex problems easily. Simulation results are presented for collision-free paths planned for two planar arms and then for two 3 degree-of-freedom (DOF) PUMA®-like arms moving in three-dimensional operational space.

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