Synthesis of a Path-Planning Algorithm for Autonomous Robots Moving in a Game Environment during Collision Avoidance

Lisowski, Józef

doi:10.3390/electronics10060675

Cited by 21 publications

(11 citation statements)

References 26 publications

(27 reference statements)

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“…This distributed collision avoidance idea has also been widely used in the field of robot collision avoidance. For example, [34] formulate the robot control as multi-step matrix game and optimized the collision avoidance task by dual linear programming. In terms of collision risk analysis, [35,36] proposed a real-time collision risk assessment method.…”

Section: Literature Review 21 Intelligent Collision Avoidance Methodsmentioning

confidence: 99%

A COLREGs-Compliant Collision Avoidance Decision Approach Based on Deep Reinforcement Learning

Wang

Huang

Liu

et al. 2022

JMSE

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It is crucial to develop a COLREGs-compliant intelligent collision avoidance system for the safety of unmanned ships during navigation. This paper proposes a collision avoidance decision approach based on the deep reinforcement learning method. A modified collision avoidance framework is developed that takes into consideration the characteristics of different encounter scenarios. Hierarchical reward functions are established to assign reward values to constrain the behavior of the agent. The collision avoidance actions of the agent under different encounter situations are evaluated on the basis of the COLREGs to ensure ship safety and compliance during navigation. The deep Q network algorithm is introduced to train the proposed collision avoidance decision framework, while various simulation experiments are performed to validate the developed collision avoidance model. Results indicate that the proposed method can effectively perform tasks that help ships avoid collisions in different encounter scenarios. The proposed approach is a novel attempt for intelligent collision avoidance decisions of unmanned ships.

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Section: Literature Review 21 Intelligent Collision Avoidance Methodsmentioning

confidence: 99%

A COLREGs-Compliant Collision Avoidance Decision Approach Based on Deep Reinforcement Learning

Wang

Huang

Liu

et al. 2022

JMSE

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“…Li et al [23] applied this method to multi-ship collision avoidance, predicting ship trajectories based on ship dynamics, giving different candidate rudder angles, evaluating the collision risk by each rudder angle, and then using optimization strategies to find the most effective collision avoidance plan for ships. Lisowski [24] used a multistep matrix game model to address the problem of autonomous robots' collision avoidance. However, in this research, all ships were controlled by a system decision module, and each agent had no independent decision-making intelligence.…”

Section: Literature Review 21 Ship Collision Avoidance Methodsmentioning

confidence: 99%

A Novel Ship Collision Avoidance Awareness Approach for Cooperating Ships Using Multi-Agent Deep Reinforcement Learning

Chen

et al. 2021

JMSE

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Ships are special machineries with large inertias and relatively weak driving forces. Simulating the manual operations of manipulating ships with artificial intelligence (AI) and machine learning techniques becomes more and more common, in which avoiding collisions in crowded waters may be the most challenging task. This research proposes a cooperative collision avoidance approach for multiple ships using a multi-agent deep reinforcement learning (MADRL) algorithm. Specifically, each ship is modeled as an individual agent, controlled by a Deep Q-Network (DQN) method and described by a dedicated ship motion model. Each agent observes the state of itself and other ships as well as the surrounding environment. Then, agents analyze the navigation situation and make motion decisions accordingly. In particular, specific reward function schemas are designed to simulate the degree of cooperation among agents. According to the International Regulations for Preventing Collisions at Sea (COLREGs), three typical scenarios of simulation, which are head-on, overtaking and crossing, are established to validate the proposed approach. With sufficient training of MADRL, the ship agents were capable of avoiding collisions through cooperation in narrow crowded waters. This method provides new insights for bionic modeling of ship operations, which is of important theoretical and practical significance.

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“…In similar research domains, there are studies focusing on path planning splines [20], arcs [21] or polylines for small unmanned craft to following, the vessel will navigate along the expected routes when it is flexible enough with quality maneuverability, thus the curve planning is more fitting to small robots. Jozef [22] made effective progress on path-planning by game theory, the proposed synthesis can facilitate a mobile robot passing dozens of encounters from other robots. While large ships generally have difficulty in keeping catch of an arbitrary path.…”

Section: Literature Reviewmentioning

confidence: 99%

A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships

Zhou

Zhang

Wang

2021

JMSE

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Large ships are typically with large inertia and longtime delay in motion, in prevailing collision avoidance methods, their maneuverability is generally neglected, there could be a dangerous situation if the system fails to control the ship course as ordered in a timely manner. This paper proposes a coordination system which consists of two algorithms for avoiding risk and then returning to scheduled waypoint. The avoiding risk algorithm are based on VO (velocity obstacle) method, the returning algorithm is derived from LOS (light of sight) guidance. For better performance, the ship model for simulation is a nonlinear Norrbin Model, with the controller improved by CGSA (closed loop gain shaping algorithm) method from traditional PID control, COLREGS (Convention on the International Regulations for Preventing Collisions at Sea) constrains are considered. To test the effectiveness of the proposed system, a series of complex scenarios including Imazu problem are applied.

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Synthesis of a Path-Planning Algorithm for Autonomous Robots Moving in a Game Environment during Collision Avoidance

Cited by 21 publications

References 26 publications

A COLREGs-Compliant Collision Avoidance Decision Approach Based on Deep Reinforcement Learning

A COLREGs-Compliant Collision Avoidance Decision Approach Based on Deep Reinforcement Learning

A Novel Ship Collision Avoidance Awareness Approach for Cooperating Ships Using Multi-Agent Deep Reinforcement Learning

A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships

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