Avoidance Performance Research Articles

An improved measurement variable estimation model for positioning mobile robot

Abstract The localization and navigation technology are the key factors in the research of mobile robots. With the demand of smart manufacturing industry and the development of robotics technology, the importance of mobile robot has become increasingly prominent. Mobile robot positioning research is mostly based on odometry, however, it has cumulative errors that would affect the accuracy of positioning results. This paper describes an improved measurement model that suitable from 0° to 180° and used this model in the Extended Kalman Filter (EKF) and Unscented Kalman Filter(UKF) time update step respectively, the method can address the interference of kinematics model predicted position and heading angle, both of them are easily disturbed by noises and other factors. Designing a tracked mobile robot as experimental platform to collect the raw data, conducting experimental research including the performance of hardware platform and autonomous obstacle avoidance, the real-time and stability of remote data interaction, and the accuracy of optimal pose estimation. The simulation results have been verified the accuracy of the improved measurement model applied to UKF.

Formation Obstacle Avoidance: A Fluid-Based Solution

To enhance the mission success rate, the multi-agent system (MAS) often adopts a formation form to shuttle over complex terrain. Compared with obstacle avoidance in other multi-agent coordinated missions, multi-agent formation (MAF) obstacle avoidance is more challenging because agents must both avoid obstacles and subsequently return to their intended positions in the original formation at a uniform speed. Aiming at the three-dimensional complex obstacle environment, the fluid methodology is extended to the application of the formation issue for the first time. In this paper, an MAF obstacle avoidance framework built upon model-based interfered fluid dynamical system (MIFDS), which is the most representative algorithm in fluid methodology, is proposed. The proposed MIFDS is modified to be more in accordance with the kinematic performance of agents than traditional IFDS by introducing the kinematic model and constraints. Moreover, on the basis of MIFDS and the virtual leader formation structure, a novel original fluid speed adjustment strategy in MIFDS, which is the core to solve the formation issue, is designed to balance obstacle avoidance and formation reconfiguration. Finally, receding horizon control is introduced to further guarantee the performance of obstacle avoidance and motion safety. Simulations are used to demonstrate the effectiveness of the proposed method.

How do drivers avoid collisions? A driving simulator-based study

Introduction: Drivers' collision avoidance performance in an impending collision situation plays a decisive role for safety outcomes. This study explored drivers' collision avoidance performances in three typical collision scenarios that were right-angle collision, head-on collision, and collision with pedestrian. Method: A high-fidelity driving simulator was used to design the scenarios and conduct the experiment. 45 participants took part in the simulator experiment. Drivers' longitudinal/lateral collision avoidance performances and collision result were recorded. Results: Experimental results showed that brake only was the most common response among the three collision scenarios, followed by brake combining swerve in head-on and pedestrian collision scenarios. In right-angle collision scenario with TTC (time to collision) largest among three scenarios, no driver swerved, and meanwhile drivers who showed slow brake reaction tended to compensate the collision risk by taking a larger maximum deceleration rate within a shorter time. Swerve-toward-conflict was a prevalent phenomenon in head-on and pedestrian collision scenarios and significantly associated with collision risk. Drivers that swerved toward the conflict object had a shorter swerve reaction time than drivers that swerved away from conflict. Conclusions: Long brake reaction time and wrong swerve direction were the main factors leading to a high collision likelihood. The swerve-toward-conflict maneuver caused a delay in brake action and degraded subsequent braking performances. The prevalent phenomenon indicated that drivers tended to use an intuitive (heuristic) way to make decisions in critical traffic situations. Practical applications: The study generated a better understanding of collision development and shed lights on the design of future advanced collision avoidance systems for semi-automated vehicles. Manufactures should also engage more efforts in developing active steering assistance systems to assist drivers in collision avoidance.

Online Bidirectional Trajectory Planning for Mobile Robots in State-Time Space

In this paper, we propose a computationally efficient online local motion planning algorithm for mobile robots in unknown cluttered dynamic environments. The algorithm plans a trajectory incrementally up to the finite horizon in state-time space. Incremental planning method is capable of fast computation but has poor obstacle avoidance performance. To compensate for the drawbacks of incremental planning, a partial trajectory modification scheme is used that sets an interim goal and then plans a trajectory to pass through the interim goal. The interim goal is a temporary desired goal to prevent the robot from falling into the inevitable collision state. By using incremental planning and partial trajectory modification, it is possible to plan collision-free trajectory with small computation even in cluttered dynamic environment. To generate smooth trajectories around given waypoints and goal, we systematize bidirectional trajectory planning with three kinds of trajectories: 1) a forward trajectory from the current robot state; 2) a backward trajectory from the state of current target waypoint; 3) and a connecting trajectory between the forward and backward trajectories. A smooth trajectory is generated around the way point and goal by setting the state of the current target waypoint (or goal), while taking into account the positional relationship among the planned forward trajectory, the target waypoint and the next waypoint. Performances of the proposed algorithm are validated through extensive simulations and experiment with two types of mobile robots: 1) a holonomic mobile robot and 2) a differential drive mobile robot.

Longitudinal Collision Avoidance and Lateral Stability Adaptive Control System Based on MPC of Autonomous Vehicles

The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via auto brake, and it is one of the key technologies of autonomous vehicles. Moreover, the vehicle lateral stability is very crucial in emergency scenarios. Due to complex traffic conditions and various road frictions, emergency brake may cause a vehicle to lose its lateral stability. Therefore, this paper proposes a lateral-stability-coordinated collision avoidance control system (LSCACS) based on the model predictive control (MPC). First, the proposed LSCACS decides which control mode to be implemented based on vehicle dynamics states, including a normal driving mode, a full auto brake mode, and a brake and stability mode. The MPC is used in the upper controller to calculate the desired deceleration and additional yaw moment. The lower controller calculates the desired tire forces of four wheels and realizes them by certain wheel cylinder hydraulic pressures. The LSCACS is validated by hardware-in-the-loop (HIL) tests, and the results show LSCACS’s effectiveness and great performance of the collision avoidance and lateral stability.

Distributed RISE control for spacecraft formation reconfiguration with collision avoidance

In this paper, we investigate the distributed formation reconfiguration problem of multiple spacecraft with collision avoidance in the presence of external disturbances. Artificial potential function (APF) based virtual velocity controllers for the spacecraft are firstly constructed, which overcome the local minima problem through introducing auxiliary inputs weighted by bump functions. Then, based on the robust integral of the sign of the error (RISE) control methodology, a distributed continuous asymptotic tracking control protocol is proposed, accomplishing both formation reconfiguration and the collision avoidance among spacecraft and with obstacles. Furthermore, using tools from graph theory, Lyapunov analysis and backstepping technique, we show the stability and collision avoidance performance of the closed-loop multiple spacecraft system. Numerical simulations for a spacecraft formation are finally provided to validate the effectiveness of the proposed algorithm.

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

Leveraging the advancements in sensor and mapping technologies, the collision-free autonomous vehicle becomes possible in the future. In this article, a case study of collision avoidance by active steering control is presented and verified by a driver-in-the-loop platform. The proposed control system integrates a risk assessment algorithm and a hierarchical model predictive control approach to ensure a safe driving. First, a fuzzy logic is used to estimate the potential conflict. Besides, a nonlinear model predictive control is introduced in the upper layer of the model predictive controller to generate a collision-free trajectory. Furthermore, the lower layer determines the optimal steering angle based on the linear time-variant model predictive control to follow the replanning path. The performance of the controller has been evaluated in the real-time driver-in-the-loop test. The results show that the autonomous vehicle is able to avoid the collision with the surrounding vehicle that is operated by a real driver, and the performance of collision avoidance is improved by means of the risk assessment.

Active Virtual Impedance Control for Sound-Following Robots to Avoid Obstacles

A novel active virtual impedance algorithm is here proposed to help sound-following robots avoid obstacles while tracking a sound source. The tracking velocity of a mobile robot to a sound source is determined by virtual repulsive and attraction forces to avoid obstacles and to follow the sound source, respectively. Active virtual impedance is defined as a function of distances and relative velocities to the sound source and obstacles from the mobile robot, which is used to generate the tracking velocity of the mobile robot. Conventional virtual impedance methods have fixed coefficients for relative distances and velocities. However, in this research, the coefficients are dynamically adjusted to extend the obstacle avoidance performance to multiple obstacle environments. The relative distances and velocities are obtained using a microphone array consisting of three microphones in a row. The geometrical relationships of the microphones are utilized to estimate the relative position and orientation of the sound source with respect to the mobile robot, which carries the microphone array. The effectiveness of the proposed algorithm is demonstrated by experiments.

A pilot study of escape, avoidance, and approach behaviors in treated alcohol-dependent males

ABSTRACTAlcohol-dependent individuals are often reported to use behavioral strategies both to escape from and avoid negative affective states, and also to approach positive affective states. However, there has been little examination of how these individuals acquire and express these types of behaviors. In this study, male adults meeting the International Classification of Diseases–10th Revision (ICD–10) criteria for alcohol dependence recruited from an outpatient treatment clinic and healthy male controls were given a computer-based task to assess learning and performance of escape, avoidance, and approach behaviors. In this task, participants control a spaceship and can either gain points by shooting an enemy spaceship or hide in safe areas to escape or avoid on-screen aversive events. We found that patients with alcohol dependence exhibited greater escape and approach behaviors, tended to show greater avoidance behavior, and achieved higher total score on the computer task than healthy controls. This is the first demonstration of such behavioral differences in this population, supporting the overactivation of both positive and negative reinforcement systems in alcohol dependence, and suggesting that such behavioral biases are not limited to alcohol-related cues. The contribution of this work to behavioral assessment and therapeutic approaches, as well as possible future directions, are discussed.

Adaptive leader-following formation control with collision avoidance for a class of second-order nonlinear multi-agent systems

Combined with artificial potential field (APF) method, an adaptive leader-following formation control with collision avoidance strategy is developed for a class of second-order nonlinear multi-agent systems. Since nonlinear dynamic systems contain the inherent complexities and uncertainties, most formation control with collision avoidance objectives are focused on linear multi-agent systems. In order to solve the problems of unknown nonlinear dynamics, neural network (NN) is employed in the proposed formation protocol design. In any formation control, the higher probability of collision among agents is taken place in the initial stage. The proposed method effectively solves the problems by integrating APF method into leader-following formation strategy. Based on the Lyapunov stability theory and graph theory, the second-order nonlinear multi-agent systems can achieve an ideal formation pattern with the collision avoidance performance. The numerical simulations are carried out to further verify the performance of the proposed algorithm.

Beneficial effects of Spirulina platensis, voluntary exercise and environmental enrichment against adolescent stress induced deficits in cognitive functions, hippocampal BDNF and morphological remolding in adult female rats

Chronic exposure to stress during adolescent period has been demonstrated to impair cognitive functions and the dendritic morphology of pyramidal neurons in the rat hippocampal CA3 area. The present study investigated the combined protective effects of Spirulina platensis (SP), a supplement made from blue-green algae with neuroprotective properties, voluntary exercise (EX) and environmental enrichment (EE) against cognitive deficits, alternations in hippocampal BDNF levels, and abnormal neuronal remodeling in adult female rats (PND 60) induced by exposure to chronic restraint stress during adolescent period (PND 30–40). Rats were exposed to restraint stress (2 h/day for 10 days, PND 30–40). Then, the animals were subjected to treatment with SP (200 mg/kg/day), EX, EE and the combined treatments (SP + EX, and SP + EE) between PND 41 and 55 of age. Following the interventions, spatial learning and memory, passive avoidance performance, hippocampal dendritic morphology and BDNF levels were assessed. Results showed that plasma corticosterone levels increased at PND 40 and remained elevated at PND 55 and 70 in the stressed rats. Stressed rats showed deficits in spatial learning and memory and passive avoidance performance, decreased BDNF levels in the hippocampus, and reduced apical dendritic length and branch points of the CA3 pyramidal neurons. These deficits were alleviated by the SP, EX and EE, and the combined treatments, which accompanied with a decline in serum corticosterone in stressed animals. Some treatments even enhanced cognitive functions, and BDNF levels and neuroanatomical remodeling in the hippocampus of non-stressed animals. Our findings provide important evidences that physical activity, exposure to EE, and the SP treatment during adolescent period can protect against adolescent stress induced behavioral, biochemical and neuroanatomical impairments in adulthood.

LOW COST COLLISION AVOIDANCE SYSTEM ON HOLONOMIC AND NON- HOLONOMIC MOBILE ROBOTS

Technological advancement in industries necessitates development in autonomous mobile robots with obstacle avoidance for applications in industries, hospitals and educational environment. Holonomic motion allows the mobile robots to move instantaneously in any direction with no constraints while non-holonomic motion restricts the motion in limited directions. This study presents the comparison of obstacle avoidance performance using low cost sensors on holonomic and non-holonomic mobile robots. The robot prototypes are developed and implemented with stable control system for better mobility. Experimental results show that the omnidirectional mobile robots avoid collisions with limited space and less time while non-holonomic mobile platforms exhibits reduced computational complexity and efficient implementation. The developed mobile robots can be used for diverse task specification applications. Article DOI: https://dx.doi.org/10.20319/mijst.2019.51.1222 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

The Association between Corporate Tax Avoidance and Consistency of Corporate Social Responsibility (CSR): Evidence from South Korea

We investigate the association between corporate tax avoidance and corporate social responsibility (CSR) performance as well as the moderation effect of CSR performance consistency, using samples from South Korea. The baseline regression result suggests that there is a negative association between corporate tax avoidance and CSR performance among South Korean firms. However, as extant empirical studies find inconsistent evidence on the relationship and also the corporate culture theory suggests that the relationship could be non-monotonic and consistency is a important element to be considered in the model, we separated samples into two groups based on CSR performance, and re-examined the relationship with interaction term of CSR performance and CSR consistency as explanatory variable. Our empirical evidence suggests that tax avoidance is negatively associated with CSR performance for a high CSR performance group, whereas tax avoidance is positively associated with CSR performance for a low CSR performance group. Furthermore, our regression results indicate that a firm’s tax avoidance is negatively associated with the firm’s consistently high CSR performance, while a firm’s tax avoidance is positively associated with consistently low CSR performance.

Modified 1D Virtual Force Field Approach to Moving Obstacle Avoidance for Autonomous Ground Vehicles

A modified 1D-virtual force field (1D-VFF) approach is newly proposed to effectively avoid moving obstacles for autonomous ground vehicle (AGV) applications. The underlying idea of this approach is to construct an obstacle force field based on the predicted trajectory of a moving obstacle. To do this, the time-to-collision and the probable area of collision are estimated using the obstacle position and velocity information measured from a FMCW radar. The predicted obstacle force field (POFF) replaces the obstacle force field (OFF) of the conventional 1D-VFF to enhance the AGV’s obstacle avoidance performance. As in the conventional 1D-VFF scheme, the steering force field (SFF) is generated by using the prescribed position of the goal and the current position of the ground vehicle. In order to ensure a collision-free local path against the moving obstacle, the steering command is determined as the maximizing solution of the integrated force field (IFF) defined by a linear combination of POFF and SFF. Simulations of various scenarios show that the proposed algorithm provides an improved local path planning performance against moving obstacles compared to the existing 1D-VFF methodology.

A fast finite-time convergent guidance law with nonlinear disturbance observer for unmanned aerial vehicles collision avoidance

In this paper, a fast finite-time convergent guidance law with nonlinear disturbance observer is proposed for the problem of unmanned aerial vehicle collision avoidance. First, a linear time-varying collision avoidance model based on a collision cone is established. Then a fast finite-time convergent guidance law and a nonlinear disturbance observer are designed to ensure the safety of UAV collision avoidance. In addition, the stability of a nonlinear collision avoidance system is proved by the finite-time convergence stability theory, and the stability conditions are used to design the guidance coefficients. The simulation results conclusively demonstrate that this method can achieve collision avoidance in the presence of an unknown acceleration of obstacle. Moreover, the performance of collision avoidance using this method is greater than the collision avoidance method based on normal finite time convergent guidance.

Cooperative collision avoidance algorithm for multi-vehicle based on vehicle networking technology

This paper addresses the problem of low success rate of traditional algorithm collision avoidance and poor control of parking distance. The vehicle network system and the vehicle kinematics model are established. The paper describes accurate estimation of the vehicle driving state in tunnel intranet based on vehicle sensor and vehicle GPS data. Constraints are established for fast response disturbances, rapid stabilisation, reduced braking acceleration and deceleration, and safe distances. From the energy point of view, we consider the relative kinetic energy of any two vehicles under the vehicle network, measure the collision risk, establish the objective function, and analyse the relative kinetic energy of all vehicles approaching the preceding vehicle. In this way, a multi-vehicle cooperative collision avoidance method based on vehicle network technology is realised. The experimental results show that the algorithm has strong collision avoidance performance, and the parking distance control is better, which can ensure safety.

Human Intention Understanding From Multiple Demonstrations and Behavior Generalization in Dynamic Movement Primitives Framework

Human’s interference in the process of skill learning can improve the performance of the robot greatly. However, learning from demonstration to generate a new action with human behavioral characteristics in the varying situation is challenging. Generally, dynamic movement primitives (DMPs) method can generalize the trajectory imitating the demonstration, but cannot integrate the feature of multiple trajectories of different targets. In this paper, the proposed method contains two aspects of learning and generating. The statistical method Gaussian mixture model and Gaussian mixture regression (GMM-GMR) is used to extract the common characteristic and eliminate the uncertainty of the multiple demonstrations. To exert the ability of DMPs to generate a human-like motion to a new goal, and we model the shape parameter with locally weighted regression (LWR) method. To enhance the ability of DMPs in multiple trajectories learning, we propose the multivariate Gaussian process regression (MV-GPR) method to construct the regression model of shape parameters to reflect the human intentions, with respect to the target position. To verify the feasibility of the proposed method, we design a peg-in-hole experiment with proving generalization and obstacle avoidance performance. The results have shown that the strategy integrated the generalization of DMPs and feature regeneration ability of MV-GPR method, and the generated valid trajectory could achieve the peg-in-hole task with 6-DOF whole-arm avoidance.

A Rear-End Collision Risk Evaluation and Control Scheme Using a Bayesian Network Model

This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.

CARTEE: congestion avoidance with reliable transport and energy efficiency for multimedia applications in wireless sensor networks

Reliable data transport is an essential requirement for many multimedia applications in wireless sensor networks. Actually, an efficient transport protocol for these applications must take into account not only reliability and energy consumption factors but also memory occupancy and data delivery delay. Recently, many research works have been conducted in this area, however the proposed protocols treat some of these aspects and neglect others. Contrarily, in this paper we present a novel transport solution designed to provide 100% reliability without making light of other factors. Through different mechanisms, we attempt to reach this objective with congestion avoidance and good performances in terms of energy consumption, delivery delay, and memory storage. The proposed protocol, called congestion avoidance with reliable transmission and energy efficiency (CARTEE), attains these goals through several mechanisms, namely: fixed sliding window transmission, alternative implicit/explicit acknowledgement, a new congestion detection technique, and distributed transmission rate adjustment. To evaluate the proposed protocol, we have conducted simulations using ns-3. The obtained results confirm the efficiency and scalability of CARTEE and demonstrate that it outperforms the recent proposed transport protocols in terms of reliability, congestion avoidance, data cache occupancy, and latency.

Collision risk management of cognitively distracted drivers in a car-following situation

Mobile phone distraction has been recognized as an adverse factor that degrades drivers’ performance on road. Although research showed that drivers take various compensatory strategies to minimize the risk in distracted driving, little consensus has been achieved regarding the actual change in collision risk because of compensatory behaviours. This study aims to investigate the impact of mobile phone use and drivers’ compensatory behaviours on the collision risk in a car-following situation. By using a high-fidelity driving simulator, 37 participants completed the simulation experiment in three mobile phone use conditions: no phone (baseline), hands-free and hand-held. Cluster analysis was adopted to classify the final collision risk into different levels. Two logit regression models were developed to examine the relationships between drivers’ characteristics, mobile phone use, collision avoidance performances and their involvement in the collision risk. Results show that compared to no phone and hands-free, drivers using hand-held phone had a longer brake reaction time and also an increased likelihood of being involved in a high risk group. Drivers compensated to reduce the likelihood of safety-critical events through a simultaneous control of car-following speed and distance (i.e. Time-to-collision (TTC)) in distracted condition. Additionally, the results also indicated that female drivers and non-professional drivers were more likely to be involved in high risk group than male drivers and professional drivers. The study provided a systematic method to quantify the impact of mobile phone distraction and drivers’ compensation behaviors on collision risk. The effectiveness of compensatory strategy by controlling TTC also shed light on the development of intelligent transport systems to help distracted drivers avoid safety-critical situations.