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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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Due to costly space projects, affordable flight models and test prototypes are of incomparable importance in academic and research applications, for example, data acquisition and subsystems testing. In this regard, CanSat could be used as a low-cost, high-tech, and lightweight model. CanSat carrier launch system is a simple second-order aerospace system. Aerospace systems require the highest level of effective controller performance. Adding second-order integral and second-order derivative terms to proportional–integral–derivative controller leads to the elimination of steady-state errors and yields to a faster systems convergence. Moreover, sliding mode control is considered as a robust controller that has appropriate features to track. Thus, this article tends to present an adaptive hybrid of higher order proportional–integral–derivative and sliding mode control optimized by multi-objective genetic algorithm to control a CanSat carrier launch system.

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