Abstract
Exploration in unknown dynamic environments is a challenging problem in an AI system, and current techniques tend to produce irrational exploratory behaviours and fail in obstacle avoidance. To this end, we present a three-tiered hierarchical and modular spatial exploration model that combines the intrinsic motivation integrated deep reinforcement learning (DRL) and rule-based real-time obstacle avoidance approach. We address the spatial exploration problem in two levels on the whole. On the higher level, a DRL based global module learns to determine a distant but easily reachable target that maximizes the current exploration progress. On the lower level, another two-level hierarchical movement controller is used to produce locally smooth and safe movements between targets based on the information of known areas and free space assumption. Experimental results on diverse and challenging 2D dynamic maps show that the proposed model achieves almost 90% coverage and generates smoother trajectories compared with a state-of-the-art IM based DRL and some other heuristic methods on the basis of avoiding obstacles in real time.
Highlights
Spatial cognitive behaviour modelling is the basic content of human cognitive behaviour modelling, and is one of the hottest topics in the field of neuroscience and computer science
Put the set of collision-avoidance velocities VA which is calculated by the Self-adaptive Finite-time Velocity Obstacle (SFVO) algorithm (Algorithm 1) VA = CAτA| B (VB ), the observation (Ot ) of the agent and the sequence of key points into evaluation function ( f ), and calculate an optimal velocity of the agent vopt ← f (VA, K1, K2, ..., Kn |Ot, OMt, Pt )
This paper proposed a three-tiered hierarchical autonomous spatial exploration model, IRHE-SFVO, that combines a high-level exploration strategy (GEM) and a low-level module (LMM) including a planning phase and a controlling phase
Summary
Spatial cognitive behaviour modelling is the basic content of human cognitive behaviour modelling, and is one of the hottest topics in the field of neuroscience and computer science. The agent in an AI system needs to explore the environment to gain enough information about the spatial structure. The possible applications include, for example, search and rescue (SAR) missions, intelligence, surveillance and reconnaissance (ISR), and planetary exploration. It is important to design an efficient and effective exploration strategy in unknown spaces. Autonomous spatial exploration falls into two main categories: traditional rule-based exploration and intelligent machine-learning-based exploration. The rule-based exploration methods such as frontier-based method [1] is simple, convenient and efficient
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