Yuanchang Liu

Bio: Yuanchang Liu is an academic researcher from University College London. The author has contributed to research in topics: Obstacle avoidance & Motion planning. The author has an hindex of 1, co-authored 1 publications receiving 26 citations.

Learn to Navigate: Cooperative Path Planning for Unmanned Surface Vehicles Using Deep Reinforcement Learning

Abstract: Unmanned surface vehicle (USV) has witnessed a rapid growth in the recent decade and has been applied in various practical applications in both military and civilian domains. USVs can either be deployed as a single unit or multiple vehicles in a fleet to conduct ocean missions. Central to the control of USV and USV formations, path planning is the key technology that ensures the navigation safety by generating collision free trajectories. Compared with conventional path planning algorithms, the deep reinforcement learning (RL) based planning algorithms provides a new resolution by integrating a high-level artificial intelligence. This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments. For single USV planning, with the primary aim being to calculate a shortest collision avoiding path, the designed RL path planning algorithm is able to solve other complex issues such as the compliance with vehicle motion constraints. The USV formation maintenance algorithm is capable of calculating suitable paths for the formation and retain the formation shape robustly or vary shapes where necessary, which is promising to assist with the navigation in environments with cluttered obstacles. The developed three sets of algorithms are validated and tested in computer-based simulations and practical maritime environments extracted from real harbour areas in the UK.

Path Planning of Coastal Ships Based on Optimized DQN Reward Function

Abstract: Path planning is a key issue in the field of coastal ships, and it is also the core foundation of ship intelligent development. In order to better realize the ship path planning in the process of navigation, this paper proposes a coastal ship path planning model based on the optimized deep Q network (DQN) algorithm. The model is mainly composed of environment status information and the DQN algorithm. The environment status information provides training space for the DQN algorithm and is quantified according to the actual navigation environment and international rules for collision avoidance at sea. The DQN algorithm mainly includes four components which are ship state space, action space, action exploration strategy and reward function. The traditional reward function of DQN may lead to the low learning efficiency and convergence speed of the model. This paper optimizes the traditional reward function from three aspects: (a) the potential energy reward of the target point to the ship is set; (b) the reward area is added near the target point; and (c) the danger area is added near the obstacle. Through the above optimized method, the ship can avoid obstacles to reach the target point faster, and the convergence speed of the model is accelerated. The traditional DQN algorithm, A* algorithm, BUG2 algorithm and artificial potential field (APF) algorithm are selected for experimental comparison, and the experimental data are analyzed from the path length, planning time, number of path corners. The experimental results show that the optimized DQN algorithm has better stability and convergence, and greatly reduces the calculation time. It can plan the optimal path in line with the actual navigation rules, and improve the safety, economy and autonomous decision-making ability of ship navigation.

A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case

Abstract: Autonomous surfaces vehicles (ASVs) excel at monitoring and measuring aquatic nutrients due to their autonomy, mobility, and relatively low cost. When planning paths for such vehicles, the task of patrolling with multiple agents is usually addressed with heuristics approaches, such as Reinforcement Learning (RL), because of the complexity and high dimensionality of the problem. Not only do efficient paths have to be designed, but addressing disturbances in movement or the battery’s performance is mandatory. For this multiagent patrolling task, the proposed approach is based on a centralized Convolutional Deep Q-Network, designed with a final independent dense layer for every agent to deal with scalability, with the hypothesis/assumption that every agent has the same properties and capabilities. For this purpose, a tailored reward function is created which penalizes illegal actions (such as collisions) and rewards visiting idle cells (cells that remains unvisited for a long time). A comparison with various multiagent Reinforcement Learning (MARL) algorithms has been done (Independent Q-Learning, Dueling Q-Network and multiagent Double Deep Q-Learning) in a case-study scenario like the Ypacarai lake in Asuncion (Paraguay). The training results in multiagent policy leads to an average improvement of 15% compared to lawn mower trajectories and a 6% improvement over the IDQL for the case-study considered. When evaluating the training speed, the proposed approach runs three times faster than the independent algorithm.

A survey on deep reinforcement learning architectures, applications and emerging trends

Abstract: From a future perspective and with the current advancements in technology, deep reinforcement learning (DRL) is set to play an important role in several areas like transportation, automation, finance, medical and in many more fields with less human interaction. With the popularity of its fast-learning algorithms there is an exponential increase in the opportunities for handling dynamic environments without any explicit programming. Additionally, DRL sophisticatedly handles real-world complex problems in different environments. It has grasped great attention in the areas of natural language processing (NLP), speech recognition, computer vision and image classification which has led to a drastic increase in solving complex problems like planning, decision-making and perception. This survey provides a comprehensive analysis of DRL and different types of neural network, DRL architectures, and their real-world applications. Recent and upcoming trends in the field of artificial intelligence (AI) and its categories have been emphasized and potential challenges have been discussed.

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Abstract: Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motion-planning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment. The conventional methods of DRL are categorized to value-based, policy-based and actor-critic-based algorithms, and the corresponding theories and applications are surveyed. Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems. According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.