Statement of problem of unknown environment recognizing, navigating and path planning by agent

We consider the NP-hard problem of multirobot informative path planning in the presence of communication constraints, where the objective is to collect higher amounts of information of an ambient phenomenon. We propose a novel approach that uses continuous region partitioning into Voronoi components to efficiently divide an initially unknown environment among the robots based on newly discovered obstacles enabling improved load balancing between robots. Simulation results show that our proposed approach is successful in reducing the initial imbalance of the robots’ allocated free regions while ensuring close-to-reality spatial modeling within a reasonable amount of time.

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Robot Motion Planning in an Unknown Environment with Danger Space

Electronics ◽

10.3390/electronics8020201 ◽

2019 ◽

Vol 8 (2) ◽

pp. 201 ◽

Cited By ~ 9

Author(s):

Hadi Jahanshahi ◽

Mohsen Jafarzadeh ◽

Naeimeh Najafizadeh Sari ◽

Viet-Thanh Pham ◽

Van Van Huynh ◽

...

Keyword(s):

Path Planning ◽

Motion Planning ◽

Markov Decision Processes ◽

Free Space ◽

Optimal Path ◽

Humanoid Robots ◽

Decision Processes ◽

Inference System ◽

Unknown Environment ◽

Markov Decision

This paper discusses the real-time optimal path planning of autonomous humanoid robots in unknown environments regarding the absence and presence of the danger space. The danger is defined as an environment which is not an obstacle nor free space and robot are permitted to cross when no free space options are available. In other words, the danger can be defined as the potentially risky areas of the map. For example, mud pits in a wooded area and greasy floor in a factory can be considered as a danger. The synthetic potential field, linguistic method, and Markov decision processes are methods which have been reviewed for path planning in a free-danger unknown environment. The modified Markov decision processes based on the Takagi–Sugeno fuzzy inference system is implemented to reach the target in the presence and absence of the danger space. In the proposed method, the reward function has been calculated without the exact estimation of the distance and shape of the obstacles. Unlike other existing path planning algorithms, the proposed methods can work with noisy data. Additionally, the entire motion planning procedure is fully autonomous. This feature makes the robot able to work in a real situation. The discussed methods ensure the collision avoidance and convergence to the target in an optimal and safe path. An Aldebaran humanoid robot, NAO H25, has been selected to verify the presented methods. The proposed methods require only vision data which can be obtained by only one camera. The experimental results demonstrate the efficiency of the proposed methods.

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