Collision avoidance

About: Collision avoidance is a research topic. Over the lifetime, 8014 publications have been published within this topic receiving 111414 citations.

Ship’s Trajectory Planning Based on Improved Multiobjective Algorithm for Collision Avoidance

Abstract: With vigorous development of the maritime trade, many intelligent algorithms have been proposed to avoid collisions due to resulting casualties and increased costs. According to the international regulations for preventing collisions at sea (COLREGs) and the self-evolution ability of the intelligent algorithm, the collision avoidance trajectory can be more consistent with the requirements of reality and maritime personnel. In this paper, the optimization of ship collision avoidance strategies is realized by both an improved multiobjective optimization algorithm NSGA-II and the ship domain under the condition of a wide sea area without any external disturbances. By balancing the safety and economy of ship collision avoidance, the avoidance angle and the time to the action point are used as the variables encoded by the algorithm, and the fuzzy ship domain is used to calculate the collision avoidance risk to achieve collision avoidance. The simulation results show that the proposed method can optimize the ship collision avoidance strategy and provide a reasonable scheme for ship navigation.

Intersection Collision Avoidance System Architecture

Abstract: In this preliminary paper we propose new intersection collision avoidance architecture. This system allows vehicles to establish secure links with roadside unit installed at the intersection before entering the furthest point where vehicles start to share their current state with the roadside unit. Early link establishment is chosen to maximize the opportunity of advanced notification for collision warnings.

Decentralized and Prioritized Navigation and Collision Avoidance for Multiple Mobile Robots

Abstract: We present an algorithm for the decentralised navigation of multiple mobile robots. Completely decentralised Navigation Functions are constructed, creating a potential field for each robot that gives rise to a feedback control law. The construction of the potential field incorporates limited sensing and explicit prioritisation in the form of priority classes. A non-circular sensing area creates asymmetrical sensing by reducing the influence of robots and obstacles behind each robot, introducing implicit priorities resembling “rules of the road”. Static and moving obstacles are also taken into account, as well as malfunctioning robots that are unable to maneuver. A decentralised feedback control law based on the gradient of the potential field ensures convergence and collision avoidance for all robots, while respecting a lower speed bound. Simulation results demonstrate the efficacy of the proposed algorithm.