Abstract
An autonomous robot is often in a situation to perform tasks or missions in an initially unknown environment. A logical approach to doing this implies discovering the environment by the incremental principle defined by the applied exploration strategy. A large number of exploration strategies apply the technique of selecting the next robot position between candidate locations on the frontier between the unknown and the known parts of the environment using the function that combines different criteria. The exploration strategies based on Multi-Criteria Decision-Making (MCDM) using the standard SAW, COPRAS and TOPSIS methods are presented in the paper. Their performances are evaluated in terms of the analysis and comparison of the influence that each one of them has on the efficiency of exploration in environments with a different risk level of a “bad choice” in the selection of the next robot position. The simulation results show that, due to its characteristics related to the intention to minimize risk, the application of TOPSIS can provide a good exploration strategy in environments with a high level of considered risk. No significant difference is found in the application of the analyzed MCDM methods in the exploration of environments with a low level of considered risk. Also, the results confirm that MCDM-based exploration strategies achieve better results than strategies when only one criterion is used, regardless of the characteristics of the environment. The famous D* Lite algorithm is used for path planning.
Highlights
The autonomous exploration of unknown environments is one of the most important tasks in mobile robotics
The exploration strategies based on Multi-Criteria Decision-Making (MCDM) using the standard SAW, Compressed Proportional Assessment (COPRAS) and TOPSIS methods are presented in the paper
To evaluate the implementation of different MCDM methods in exploration strategies, the autonomous exploration of the unknown complex environments was simulated in Matlab
Summary
The autonomous exploration of unknown environments is one of the most important tasks in mobile robotics. The first approach involves a prior knowledge of the environment, based on which off-line algorithms are used to define the exploration strategy. In this approach, the path of the robot is determined in advance (a predefined path). The second approach is applied when the environment is completely unknown or when there is insufficient information to effectively implement an off-line algorithm In this case, exploration is much more challenging, usually implying taking incremental steps to realize it, where in each step a robot has to choose its location, move to it and make a new observation of the environment at that particular location. The goal of exploration and coverage can be the same—to search certain items of interest, but the exploration problem is harder to solve, because the robot first has to map an unknown environment
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