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Robot navigation in a safe way for complex and crowded situations is studied in this work. When facing complex environments with both static and dynamic obstacles, in existing works unicycle nonholonomic robots are prone to two extreme behaviors, one is to fall into dead ends formed by obstacles, and the other is to not complete the navigation task in time due to excessive collision avoidance.As a result, we propose the R-SARL framework, which is based on a deep reinforcement learning algorithm and where we augment the reward function to avoid collisions. In particular, we estimate unsafe interactions between the robot and obstacles in a look-ahead distance and penalize accordingly, so that the robot can avoid collisions in advance and reach its destination safely.Furthermore, we penalize frequent excessive detours to reduce the timeout and thus improve the efficiency of navigation.We test our method in various challenging and complex crowd navigation tasks. The results show that our method improves navigation performance and outperforms state-of-the-art methods.
Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. Ho
It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like social force model (SFM) or optimal reciprocal collision avoidance (ORCA
Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are known and well-defined. In addition, the performance of previous meth
This paper proposes an end-to-end deep reinforcement learning approach for mobile robot navigation with dynamic obstacles avoidance. Using experience collected in a simulation environment, a convolutional neural network (CNN) is trained to predict pr
We present a novel method for safely navigating a robot in unknown and uneven outdoor terrains. Our approach trains a novel Deep Reinforcement Learning (DRL)-based network with channel and spatial attention modules using a novel reward function to co