Collision avoidance

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

Multiple sensor collision avoidance system for automotive applications using an IMM approach for obstacle tracking

Abstract: In this paper a multi-sensor collision avoidance system is presented for automotive applications. For obstacle detection and tracking, millimeter wave (MMW) radar and a far infrared (FIR) camera are chosen in order to provide object lists to the sensors' trackers respectively. The algorithm for track management, data association, filtering and prediction for both sensors is also presented, focusing on Kalman filtering. Thus, an interacting multiple model (IMM) filter is designed for coping with all possible modes in which a car may be. Finally, a distributed fusion architecture using a central track file for the objects' tracks is adopted and described analytically. The results of the work will be used, among others, in the European co-funded project "EUCLIDE".

Automatic collision avoidance of ships

Abstract: One of the key elements in automatic simulation of ship manoeuvring in confined waterways is route finding and collision avoidance. This paper presents a new practical method of automatic trajectory planning and collision avoidance based on an artificial potential field and speed vector. Collision prevention regulations and international navigational rules have been incorporated into the algorithm. The algorithm is fairly straightforward and simple to implement, but has been shown to be effective in finding safe paths for all ships concerned in complex situations. The method has been applied to some typical test cases and the results are very encouraging.

A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes.

Abstract: Avoiding collisions is one of the most basic needs of any mobile agent, both biological and technical, when searching around or aiming toward a goal. We propose a model of collision avoidance inspired by behavioral experiments on insects and by properties of optic flow on a spherical eye experienced during translation, and test the interaction of this model with goal-driven behavior. Insects, such as flies and bees, actively separate the rotational and translational optic flow components via behavior, i.e. by employing a saccadic strategy of flight and gaze control. Optic flow experienced during translation, i.e. during intersaccadic phases, contains information on the depth-structure of the environment, but this information is entangled with that on self-motion. Here, we propose a simple model to extract the depth structure from translational optic flow by using local properties of a spherical eye. On this basis, a motion direction of the agent is computed that ensures collision avoidance. Flying insects are thought to measure optic flow by correlation-type elementary motion detectors. Their responses depend, in addition to velocity, on the texture and contrast of objects and, thus, do not measure the velocity of objects veridically. Therefore, we initially used geometrically determined optic flow as input to a collision avoidance algorithm to show that depth information inferred from optic flow is sufficient to account for collision avoidance under closed-loop conditions. Then, the collision avoidance algorithm was tested with bio-inspired correlation-type elementary motion detectors in its input. Even then, the algorithm led successfully to collision avoidance and, in addition, replicated the characteristics of collision avoidance behavior of insects. Finally, the collision avoidance algorithm was combined with a goal direction and tested in cluttered environments. The simulated agent then showed goal-directed behavior reminiscent of components of the navigation behavior of insects.

Reinforcement Learning-based Collision Avoidance and Optimal Trajectory Planning in UAV Communication Networks

Abstract: In this paper, we propose a reinforcement learning approach of collision avoidance and investigate optimal trajectory planning for unmanned aerial vehicle (UAV) communication networks Specifically, each UAV takes charge of delivering objects in the forward path and collecting data from heterogeneous ground IoT devices in the backward path We adopt reinforcement learning for assisting UAVs to learn collision avoidance without knowing the trajectories of other UAVs in advance In addition, for each UAV, we use optimization theory to find out a shortest backward path that assures data collection from all associated IoT devices To obtain an optimal visiting order for IoT devices, we formulate and solve a no-return traveling salesman problem Given a visiting order, we formulate and solve a sequence of convex optimization problems to obtain line segments of an optimal backward path for heterogeneous ground IoT devices We use analytical results and simulation results to justify the usage of the proposed approach Simulation results show that the proposed approach is superior to a number of alternative approaches