Abstract
Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots. A multi-robot system would be a good option for such a transportation task because it can carry massive objects and provide better system reliability and redundancy when compared to a single robot system. This paper proposes an intelligent and decentralized approach for the multi-robot system using a genetic fuzzy system to perform an object transportation mission that not only minimizes the total travel distance of the multi-robot system but also guarantees the stability of the whole system in a rough terrain environment. The proposed fuzzy inference system determines the multi-robot system’s input for transporting an object to a target position and is tuned in the training process by a genetic algorithm with an artificially generated structured environment employing multiple scenarios. It validates the optimality of the proposed approach by comparing the training results with the results obtained by solving the formulated optimal control problem subject to path inequality constraints. It highlights the performance of the proposed approach by applying the trained fuzzy inference systems to operate the multi-robot system in unstructured environments.
Highlights
Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots
To validate the performance of the proposed models, the trained fuzzy inference systems (FISs) models are applied to the testing environment shown in Figure 6b using the testing conditions listed in Table 3, and the results are displayed in Figures 11–13 and Table 6
The robots for all cases successfully transport the object to the designated target position without collision, as shown in Figures 11a, 12a and 13a, and the total travel distances of the robots in each case are obtained as 352.40 m, 420.82 m, and 325.98 m, respectively, as listed in Figures 11c, 12c and 13c is observed similar to the time histories of the robots’ positions
Summary
As the interests in space and other planets increase, several space exploration missions including planetary missions are scheduled [1]. Human missions on Mars require construction of outposts and infrastructures and/or (re)locate such large structures or experimental devices to support a long-duration scientific expedition to extreme environments This leads to the necessity of means to support those activities, and the use of exploration-assisting robots would be one of the good approaches. The use of deterministic AI allows an agent to respond to uncertainties or even damages, but re-parameterization of the underlying problem with complexity may be a challenge For this reason, several studies based on stochastic AI have been actively investigated for an object transportation problem using multiple robots. Preliminary work [16], this work proposes a decentralized approach for an MRS using fuzzy inference systems (FISs) trained by a genetic algorithm (GA) in order to perform a collaborative task with a near-optimal navigation solution in an unstructured environment.
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