Collision avoidance

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

Infrastructure-cooperative algorithm for effective intersection collision avoidance

Abstract: To guarantee the road safety by avoiding collisions at the intersections is one of the major tasks of intelligent transportation systems (ITSs), which contributes to the minimal fatalities and property loss in crashes. This paper proposes an effective algorithm for infrastructure-cooperative intersection accident pre-warning system with the aid of vehicular communications. The proposed algorithm realizes accurate and efficient collision avoidances through five steps, i.e., defining variable, reasoning the vehicles evolution state, verifying safe driving behavior, assessing risk, and making decision. The critical factors are theoretically analyzed, and a vehicle state evolution model based on the Dynamic Bayesian Networks (DBNs) is established. The efficient risk assessment method based on identifying the dangerous driving behavior at intersection and different collision avoidance strategies are proposed according to the actual situation. Finally, extensive simulations are carried out to verify the performance of the proposal, and simulation results show that the proposed algorithm can effectively detect risk and accurately migrate the collision.

Self-collision avoidance motion control for human robot cooperation system using RoBE

Abstract: We have proposed a real-time self-collision avoidance control method for the robot which is used for human-robot cooperation. In this method, we represent the body of the robot by using elastic elements referred to as "RoBE (representation of body by elastic elements)". The self-collision avoidance motion could be realized based on a reaction force generated by the contacts between the elastic elements before the actual self-collision of the robot. In this paper, especially, we consider task constraints and environmental constraints during the self-collision avoidance motion, and propose two priority functions for robots to realize the several kinds of tasks in an environment based on the force/moment applied by a human. By using this control algorithm, we could apply the proposed control algorithm to any robot systems used for human-robot cooperation. The proposed motion control algorithm is implemented in a human-friendly robot, referred to as "MR Helper", and experiments are done for illustrating the validity of the proposed self-collision avoidance motion.

Ensuring safe obstacle avoidance in a shared-control system

Abstract: This paper describes the Bremen Autonomous Wheelchair project. It shows that common static fire strategies for ultrasonic sensors are inherently unsafe and proposes a new adaptive fire strategy which delivers a complete coverage of the environment of the robot. Furthermore, a new obstacle avoidance approach for shared-control robotic systems is described in detail.

Research on intelligent collision avoidance decision-making of unmanned ship in unknown environments

Abstract: To solve the problem of intelligent collision avoidance by unmanned ships in unknown environments, a deep reinforcement learning obstacle avoidance decision-making (DRLOAD) algorithm is proposed The problems encountered in unmanned ships’ intelligent avoidance decisions are analyzed, and the design criteria for a proposed decision algorithm are put forward Based on the Markov decision process, an intelligent collision avoidance model is established for unmanned ships The optimal strategy for an intelligent decision system is determined through the value function which maximizes the return for the mapping of the in unmanned ship’s state to behavior A reward function is specifically designed for obstacle avoidance, approaching a target and safety Finally, simulation experiments are carried out in multi-state obstacle environments, demonstrate the effectiveness of the proposed DRLOAD algorithm

Radar-based collision avoidance for unmanned surface vehicles

Abstract: Unmanned surface vehicles (USVs) have become a focus of research because of their extensive applications. To ensure safety and reliability and to perform complex tasks autonomously, USVs are required to possess accurate perception of the environment and effective collision avoidance capabilities. To achieve these, investigation into realtime marine radar target detection and autonomous collision avoidance technologies is required, aiming at solving the problems of noise jamming, uneven brightness, target loss, and blind areas in marine radar images. These technologies should also satisfy the requirements of real-time and reliability related to high navigation speeds of USVs. Therefore, this study developed an embedded collision avoidance system based on the marine radar, investigated a highly real-time target detection method which contains adaptive smoothing algorithm and robust segmentation algorithm, developed a stable and reliable dynamic local environment model to ensure the safety of USV navigation, and constructed a collision avoidance algorithm based on velocity obstacle (V-obstacle) which adjusts the USV’s heading and speed in real-time. Sea trials results in multi-obstacle avoidance firstly demonstrate the effectiveness and efficiency of the proposed avoidance system, and then verify its great adaptability and relative stability when a USV sailing in a real and complex marine environment. The obtained results will improve the intelligent level of USV and guarantee the safety of USV independent sailing.