Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks. Then we systematically compare and analyze the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation. Next, we describe the development of DRL-based navigation. Last, we discuss the challenges and some possible solutions regarding DRL-based navigation.

In this paper, a flexible spacecraft attitude control scheme that guarantees vibration suppression and prescribed performance on transient-state behavior is proposed. Here, parametric uncertainty, external disturbance, unmeasured elastic vibration, actuator saturation, and even configuration misalignment are considered. To guarantee prescribed performance bounds on the transient- and steady-state control errors, a performance constrained control law is formulated with an error transformed function. An elastic modal observer is employed to estimate the unmeasured flexible modal variables, and a command filter is adopted to avoid the tedious analytical computations of time derivatives of virtual control inherent in the control design. Subsequently, a novel auxiliary system is developed to compensate the adverse effects of the actuator saturation constraints, and a compensation term is integrated into the control law to tackle the configuration misalignment. A comparative simulation study is carried out to illustrate the effectiveness and advantages of the proposed approach.