Collision avoidance

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

Collision prediction and avoidance amidst moving objects for trajectory planning applications

Abstract: A methodology for computing a collision-free trajectory for mobile robots amidst moving objects is presented. This planner is based on a technique for computing the minimum translational distance between two mobile objects. This distance is then used for predicting and avoiding a collision. The computation of this distance is based on the application of the GJK algorithm to a particular subset of the Minkowski difference set of the involved objects. This subset states the separation or penetration distance between two objects along their given motions. When a collision is predicted, a collision-free intermediate temporal-position is generated avoiding such a collision.

Advanced collision warning systems

Abstract: The paper outlines the framework under which Jaguar is carrying out its collision warning and avoidance research. It shows the history of the work, the progression from conventional cruise control to adaptive cruise control and then on to collision warning and intervention. The approach used by Jaguar to develop and check on the user needs/requirements for collision warning systems is described. The strategies being adopted for the warnings and intervention are discussed, as are issues related to the sensor and scenario prediction. The planned trials to evaluate the system and the possible saleable product are then briefly discussed.

A railway collision avoidance system exploiting ad-hoc inter-vehicle communications and galileo

Abstract: The introduction of the European global navigation satellite system GALILEO allows also for a modernization of automatic train control technology. This is advisable because of the still enormous amount of collisions between trains or other kinds of obstacles (construction vehicles, construction workers, pedestrians), even if comprehensive and complex technology is extensively deployed in the infrastructure which should help to avoid such collisions. Experiences from the aeronautical Traffic Alert and Collision Avoidance System (TCAS) as well as the maritime Automatic Identification System (AIS) have shown that the probability of collisions can be significantly reduced with collision avoidance support systems, which do hardly require infrastructure components. In this article, we introduce our “RCAS” approach consisting only of mobile ad-hoc components, i.e. without the necessity of extensions of the railway infrastructure. Each train determines its position, direction and speed using GALILEO and broadcasts this information, complemented with other important information such as dangerous goods classifications in the region of its current location. This information can be received and evaluated by other trains, which may – if a potential collision is detected – lead to traffic alerts and resolution advisories up to direct interventions (usually applying the brakes).

A collision avoidance model based on the Lobula giant movement detector (LGMD) neuron of the locust

Abstract: In insects, we can find very complex and compact neural structures that are task specific. These neural structures allow them to perform complex tasks such as visual navigation, including obstacle avoidance, landing, self-stabilization, etc. Obstacle avoidance is fundamental for successful navigation, and it can be combined with more systems to make up more complex behaviors. In this paper, we present a model for collision avoidance based on the Lobula giant movement detector (LGMD) cell of the locust. This is a wide-field visual neuron that responds to looming stimuli and that can trigger avoidance reactions whenever a rapidly approaching object is detected. Here, we present result based on both an offline study of the model and its application to a flying robot.

Receding horizon particle swarm optimisation-based formation control with collision avoidance for non-holonomic mobile robots

Abstract: This study proposes a novel model predictive control (MPC) based on receding horizon particle swarm optimisation (RHPSO) for formation control of non-holonomic mobile robots by incorporating collision avoidance and control input minimisation and guaranteeing asymptotic stability. In most conventional MPC approaches, the collision avoidance constraint is imposed by the 2-norm of a relative position vector at each discrete time step. Thus, multi-robot formation control problem can be formulated as a constrained non-linear optimisation problem. In general, traditional optimisation techniques suitable for addressing constrained non-linear optimisation problems take a longer computation time with an increase in the number of constraints. The traditional approaches therefore suffer from the computational complexity problem corresponding to an increase in the prediction horizon. To address this problem without a significant increase in computational complexity, a novel strategy for collision avoidance is proposed to incorporating a particle swarm optimisation. In addition, the stability conditions are derived in simplified forms that can be satisfied by selecting appropriate constant values for control gains and weight parameters. Numerical simulations verify the effectiveness of the proposed RHPSO-based formation control.