Collision Avoidance of Flying Locusts: Steering Torques and Behaviour

Steering behaviour can be modulated by different optic flows during walking

Steering characteristics of microtunnelling in various deposits

Motorcycle riding simulation for use in subject studies has been researched in different steering configurations. The main reported issue is the high effort to fulfill the riding task, which has an influence on the results of the investigation. Steering a motorcycle can be fulfilled in push, pull and as a combination of it – which results in the so-called counter steering. The main approaches contain the classical angle- or force-based steering, with poor results in realistic feedback. Often, the lack of riding dynamics relations is mentioned in this context. We describe this novel development which allows all three (dynamic) interactions for use in riding simulation. Therefore, we summarize the state-of-the-art real time motorcycle dynamics and show which criteria can be used to determine counter-steering behavior. Also, a summary of steering advancements is given and classified into their configurations. For this purpose, from a methodical point of view, the description of driver-vehicle interaction for steer-by-wire applications is referred.Described is the development of sensor technology for a one, two or both hands steering force measurement, which is the novelty value for riding simulation. Included is the determination of the 2-dimensional force level using strain gauges, a software connection and the final validation of the technical setup on a test bench. The results are divided in a technical part and an evaluation of the rideability using objective driving dynamic dimensions. In the technical part, we show the quality of the steering- and support forces measurements. The calibration results show a deviation of 2.0 % measured with calibrated weights. Also, the bending linearity over the angle is evaluated and considered in the model, which had a high impact on the rideability. Finally, signal filters are used to optimize the signal-to-noise ratio at a latency of 20 milliseconds. This meets the requirements for use on a riding simulator. For the evaluation of the rideability we use the criteria presented in the introduction. For this, we developed a course with a lane change maneuver according to ISO 3888-2. First, we show the rideability based on the trajectory sequence and second on the phase curves: roll angle, steering angle and steering force. Comparing the curves with dynamics literature shows that the behavior fulfills the criteria and indicates a valid rideability. In addition, all riders were able to change lanes without falling or touching the edge of the road.This approach allows the investigation of steering behavior on a motorcycle riding simulator and for real ride application for investigation and validation. As already called for in the literature, we describe an approach that makes it possible to investigate rider steering behavior. Finally, we discuss the results and point to further research gaps for future work.

When sailing on cluttered water, effective collision avoidance is an essential safety requirement for unmanned surface vehicles (USVs). So that it can avoid multiple random ships effectively. This paper proposes a collision avoidance strategy for USVs based on the probabilistic game theory framework. First, a probability model is established combined with the USV’s kinematics for the collision avoidance problem. Then a greedy algorithm is designed under the framework of probability. It uses a cost function that searches possible movement paths to determine avoidance strategies for the USV. Compared with other collision avoidance methods, the method in this paper seeks to minimize the collision probability instead of guaranteeing no collision, because it is very difficult or impossible to completely avoid the collision in practice. Based on this, our method copes with kinematic constraints of the USV. The effectiveness of the algorithm is verified by simulation, and the simulation results show that the collision avoidance strategy proposed in this paper achieves a very good collision avoidance performance.

In practice, the energy of an agent is limited, therefore, saving energy is an important problem in the practical applications of multi‐agent systems. For this problem, the authors firstly proposes a novel collision avoidance function, which have much less energy cost than the normal potential field function. Secondly, to further reduce energy cost, the authors propose an unequal collision avoidance strategy, that is, according to the residual energy of neighbouring agents, high‐energy agent performs collision avoidance, while the low‐energy agent does not. Thirdly, the controller is designed based on the novel collision avoidance function and the unequal collision avoidance strategy. Besides the consensus and collision avoidance can be achieved, the proposed controller can also effectively extend the operation time/range of the whole system. Finally, the simulations verify the effectiveness and superiority of the proposed controller.

In order to improve road traffic safety, increasingly sophisticated and robust collision avoidance systems are being developed. When employed in safety-critical situations, however, the interaction between the human factors and these systems may increase the complexity of the task of driving. Due to these human factors, the ability of the driver to respond to various traffic dangers is considered to be a function of the level of automation, balance of control authority, and the innate ability of the driver. For the purpose of this study, a driving experiment was designed using two types of lane change collision avoidance systems. One was a haptic warning system that provides a steering force feedback to avoid hazardous lane change, and the other, a semi-autonomous system that provides an automatic action to prevent hazardous lane change. While drivers had the final authority over the haptic system, they were unable to override the automatic action. Both systems were examined in three conditions: i) hazard that can be detected only by the system, ii) hazard that can be detected only by the driver, and iii) combined hazards. The different support systems were applied to the different hazards resulting in significant differences in drivers’ reaction time and steering behavior. The drivers’ subjective post-hazard assessments were significantly affected by the type of encountered hazard.

Unmanned Aerial Vehicle (UAV) has received wide interest in recent years because of their promising potentials for several applications that include border surveillance, intrusion, detection, emergent item delivery, wildlife monitoring, rescue operations etc. The growing number of entrances of drones into airspace has emerged a significant interest in the collision and obstacle avoidance in autonomous operation. One of major challenges for safer deployment of UAVs is collision avoidance in order to make UAVs autonomous and self-sustainable. In today’s technological era, it is possible for UAVs to avoid obstacles and prevent collisions while navigation by implementing cutting edge computer vision algorithms and visual tracking systems. But it is challenging due to the limited payload in UAV that only permits vision based sensors such as camera, inertial measurement unit such for control and obstacle detection. This paper aims to summarize existing solutions of collision avoidance strategies used in the navigation of UAV. Collision avoidance strategies based on geometry, optimization, sense and avoid, vision-based and force field based methods are investigated in this study.

Aims: To enhance the safety of intelligent vehicle drivers and help them achieve active collision avoidance. Background: Existing research on intelligent vehicles’ active collision avoidance focuses on reference path optimization and stability tracking control. However, there is relatively little research on collision avoidance in emergency accident scenarios. Longitudinal emergency braking collision avoidance technology has been relatively mature, while steering collision avoidance still needs a long time to develop. The research on joint steering and braking collision avoidance in emergency situations is still limited. Objective: Modeling the longitudinal and lateral safety distances based on the safety conditions of adjacent lanes, two active collision avoidance strategies are designed, namely the steering strategy and combined lateral and longitudinal collision avoidance strategy, with the aim of reducing the risk of collision. Methods: In the steering strategy, five control points are first determined sequentially according to steering collision constraints. Then, a collision avoidance path is established based on the quartic Bezier curve. To enhance the tracking of this path, an LQR path-tracking controller is employed to reduce errors via feed-forward and feedback control. In the case of a combined lateral and longitudinal collision avoidance strategy, the LQR and PID controllers are first adjusted and optimized, and then the collision avoidance path is replanned. Besides, the safety risks associated with braking and steering are taken into consideration, and constraints are set on the braking deceleration accordingly. Results: The effectiveness of the designed steering strategy and combined lateral and longitudinal collision avoidance strategy is verified through simulation results obtained by using CarSIM and Matlab/Simulink. They can effectively avoid collisions in emergency situations. Conclusion: The proposed collision avoidance algorithm can avoid collision in emergency scenarios and enhance the safety of autonomous driving.

ABSTRACTThis paper proposes a new safety system named emergency steering assist (ESA), which consists of the autonomous steering subsystem and differential braking subsystem. The control system is developed to mediate the conflict objectives of vehicle stabilisation and rear-end collision avoidance in highway driving. Instead of predefining a collision-free trajectory in the entire process of collision avoidance, the process of collision avoidance is divided into two stages. In stage 1, where the vehicle is guided to enter the adjacent lane with a constant centripetal acceleration to avoid collision, the steering manoeuvre is determined by the feedforward controller based on the steering dynamics. In stage 2, where the vehicle is guided along the centreline of the adjacent lane, the steering manoeuvre is determined by the controller based on the theory of model predictive control. In addition, the differential braking subsystem is designed with a comprehensive treatment to the requirements of collision avoidance and vehicle stabilisation. Finally, the simulation results demonstrate that the proposed ESA system can effectively achieve better balance between an emergency collision avoidance manoeuvre and vehicle stabilisation at high speeds in different conditions. The hardware-in-the-loop experiment developed by the authors is used to validate the real-time performance and effectiveness of the proposed control scheme.

Ship predictive collision avoidance method based on an improved beetle antennae search algorithm

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.

Unmanned Aerials Vehicles (UAVs), most known as drones, recently gained a lot of interest in several domains of civil applications. With the number of operating drones expected to quickly rise in the next few years comes the need to develop efficient collision avoidance solutions. To ensure the applicability of the collision avoidance solutions, given the limitations of UAVs, they must rely on a reliable source of information, and must be computationally efficient. This paper aims at providing an implementation of the Velocity Obstacle collision avoidance method, based on the information transmitted by the other UAVs. Multi-agent based simulations using the simulation software AirSim and the multi-agent platform SARL are proposed to assess the validity of the proposed solution.

65 pp., 33 fig., 2 tab., 1982. Pb. DM 49.-. Ralph-Michael Böhmer Flow Cytometry and Cell Proliferation Kinetics Progress in Histochemistry and Cytochemistry Vol. 14, No 4 VI 1982 Gustav Fischer Verlag Stuttgart - New York VI

Collision avoidance ability is very important for autonomous agricultural vehicles, but the influence of different obstacles in agricultural environment is rarely taken into account. In this paper, a velocity control strategy for collision avoidance was proposed to adjust the velocity of autonomous agricultural vehicles according to the movement state and dangerous degree of the obstacles and the distance between the obstacles and the vehicles, thus to improve intelligence and safety of the vehicles. The control strategy involved two steps: collision prediction in dynamic environments with an improved obstacle space–time grid map, and velocity generator for collision avoidance with a cloud model. Simulations were conducted on the obstacle collision prediction and the designed cloud generator for velocity control respectively. Simulation results show that the proposed strategy can effectively predict collision with anti-disturbance ability for threat-free obstacles and rapid and accurate velocity output. And it realizes the real-time operation in dynamic environments with an average time of 0.2 s to predict collision. Additionally, field experiments including five trial schemes were performed to test the proposed velocity control strategy on an agricultural robot, where a haystack, a tractor and walking persons were regarded as static or dynamic obstacles. The results of the field experiments show that the proposed velocity control strategy has strong feasibility and effectiveness.

COLREGs-abiding hybrid collision avoidance algorithm based on deep reinforcement learning for USVs