scholarly journals Shared Steering Control for Lane Keeping and Obstacle Avoidance Based on Multi-Objective MPC

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4671
Author(s):  
Yang Liang ◽  
Zhishuai Yin ◽  
Linzhen Nie
Keyword(s):  
Cost Function ◽  
Obstacle Avoidance ◽  
Assessment Method ◽  
Reference Trajectory ◽  
Current Steering ◽  
Steering Control ◽  
Multi Objective ◽  
Driver’S Intention ◽  
The Cost ◽  
Lane Keeping

This paper presents a shared steering control framework for lane keeping and obstacle avoidance based on multi-objective model predictive control. One of the control objectives is to track the reference trajectory, which is updated continuously by the trajectory planning module; whereas the other is to track the driver’s current steering command, so as to consider the driver’s intention. By adding the two control objectives to the cost function of an MPC shared controller, a smooth combination of the commands of the driver and the automation can be achieved through the optimization. The authority of the driver and the automation is allocated by adjusting the weights of the objective terms in the cost function, which is determined by the proposed situation assessment method considering the longitudinal and lateral risks simultaneously. The results of the CarSim-Matlab/Simulink joint simulations show that the proposed shared controller can assist the driver to complete the tasks of lane keeping and obstacle avoidance smoothly while maintaining a good level of vehicle stability.

Analysis of Reference Shaping Control for Improved Yaw Stability in a Steer-by-Wire Vehicle

2019 ◽  
Author(s):  
Srivatsan Srinivasan ◽  
Matthias J. Schmid ◽  
Venkat N. Krovi
Keyword(s):  
Autonomous Vehicles ◽  
Open Loop ◽  
Current Steering ◽  
Steering Control ◽  
Power Steering ◽  
Steer By Wire ◽  
Yaw Stability ◽  
Adaptive Power ◽  
Lane Keeping ◽  
Wire System

Abstract Incorporation of electronic yaw stabilization in on-road vehicles can take many forms. Although the most popular ones are differential braking and torque distribution, a potentially better alternative would be the inclusion of a controller into the steering process. However, this is not often pursued in mechanically-coupled steering systems since the controller could work against the driver’s intentions creating potential challenges to safety. The growing adoption of steer-by-wire (SbW) systems now in autonomous/semi-autonomous vehicles offers an opportunity to simplify the incorporation of such steering-controller based assistance. Most current steering-assistance systems focus either on adaptive steering control (adaptive power steering and gear ratios) or on total steering control in autopilot functions (lane keeping control). Such steering-controllers (incorporated via SbW modality) can improve driving performance and maneuverability and contribute to the overall suite of active-safety vehicle systems. In this study, we introduce a new pure-feedforward (open loop) controller for the steer-by-wire system based on the concept of reference shaping control aimed at reducing the vibration/oscillation caused in vehicles during fast (evasive) maneuvers.

scholarly journals Online Reference Trajectory Adaptation: A Personalized Control Strategy for Lower Limb Exoskeletons

2021 ◽  
Author(s):  
Mohammad Shushtari ◽  
Rezvan Nasiri ◽  
Arash Arami
Keyword(s):  
Cost Function ◽  
Lower Limb ◽  
Interaction Force ◽  
Reference Trajectory ◽  
Time Symmetry ◽  
Spatiotemporal Parameters ◽  
Novel Method ◽  
The Cost ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

This paper presents a novel method for reference trajectory adaptation in lower limb rehabilitation exoskeletons during walking. Our adaptation rule is extracted from a cost function that penalizes both interaction force and trajectory modification. By adding trajectory modification term into the cost function, we restrict the boundaries of the reference trajectory adaptation according to the patient's motor capacity. The performance of the proposed adaptation method is studied analytically in terms of convergence and optimality. We also developed a realistic dynamic walking simulator and utilized it in performance analysis of the presented method. The proposed trajectory adaptation technique guarantees convergence to a stable, reliable, and rhythmic reference trajectory with no prior knowledge about the human intended motion. Our simulations demonstrate the convergence of exoskeleton trajectories to those of simulated healthy subjects while the exoskeleton trajectories adapt less to the trajectories of patients with reduced motor capacity (less reliable trajectories). Furthermore, the gait stability and spatiotemporal parameters such as step time symmetry and minimum toe off clearance enhanced by the adaptation in all subjects. The presented mathematical analysis and simulation results show the applicability and effectiveness of the proposed method and its potential to be applied for trajectory adaptation in lower limb rehabilitation exoskeletons.

An MPC Algorithm With Combined Speed and Steering Control for Obstacle Avoidance in Autonomous Ground Vehicles

2015 ◽  
Author(s):  
Jiechao Liu ◽  
Paramsothy Jayakumar ◽  
Jeffrey L. Stein ◽  
Tulga Ersal
Keyword(s):  
Obstacle Avoidance ◽  
A Priori ◽  
Simultaneous Optimization ◽  
Ground Vehicles ◽  
Steering Control ◽  
Detection Range ◽  
Autonomous Ground Vehicles ◽  
Lift Off ◽  
Multi Phase ◽  
The Cost

This article presents a model predictive control based obstacle avoidance algorithm for autonomous ground vehicles in unstructured environments. The novelty of the algorithm is the simultaneous optimization of speed and steering without a priori knowledge about the obstacles. Obstacles are detected using a planar light detection and ranging sensor and a multi-phase optimal control problem is formulated to optimize the speed and steering commands within the detection range. Acceleration capability of the vehicle as a function of speed, and stability and handling concerns such as tire lift-off are taken into account as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Thus, a safe and quick navigation is enabled without the need for a preloaded map of the environment. Simulation results show that the proposed algorithm is capable of navigating the vehicle through obstacle fields that cannot be cleared with steering control alone.

Threat Assessment and Control Decision for Automated Lane Change Maneuver

2010 ◽  
Author(s):  
Jin-Woo Lee ◽  
Xingping Chen
Keyword(s):  
Obstacle Avoidance ◽  
Threat Assessment ◽  
Test Cases ◽  
Lane Change ◽  
Steering Control ◽  
Control Decision ◽  
The Subject ◽  
Lane Keeping ◽  
And Control ◽  
Future Path

Automated vehicle steering control has been actively researched in the automotive industries and academia over a decade. While several automotive companies and suppliers have recently demonstrated autonomous parking, lane keeping control, and lane centering control systems, automated lane change and obstacle avoidance maneuvering have not been as well demonstrated with the same level of maturity. This paper describes an algorithm that assesses environment and situation around the subject vehicle and makes a proper decision when an automated lane change or obstacle avoidance maneuvering is needed. The algorithm continuously monitors the surrounding traffic and lane markings using various types of sensors, and makes judgments along the vehicle future motion. Collision threat is evaluated by comparing the future path of the vehicle and the surrounding traffics in temporal-spatial plane. Typical driving behavior patterns are modeled to ensure safety under various scenarios. This algorithm has been implemented on a test vehicle and validated on straight and curved roads for various speeds of up to 110km/h. Several test cases have been completed and the results are provided.

Kinematic Optimization of the Arm of a Working Machine

2014 ◽  
Author(s):  
Antonio Alba ◽  
Francesco Bucchi ◽  
Francesco Frendo ◽  
Marco Gabiccini
Keyword(s):  
Cost Function ◽  
Optimization Problem ◽  
Link Length ◽  
Weighting Method ◽  
Kinematic Synthesis ◽  
Multi Objective ◽  
Optimization Methodology ◽  
The Cost ◽  
Joint Limits ◽  
Kinematic Optimization

The aim of this work is to develop an optimization methodology for the design of the arm of a small-sized working machine. The workspace and a reference maneuver are firstly defined together with a pre-defined redundant kinematic topology. The kinematic synthesis is framed as a constrained multi-objective problem with respect to link length variables. The constraints consider the capability of the machine to follow the assigned trajectory and to fulfill the joint limits. The cost function incorporates the solution of the inverse kinematics and uses several indices, e.g., total link lengths, manipulability, energy consumption. The multi-objective optimization problem is solved employing the weighting method, converting the initial problem into a single-objective one. The final scalar cost function is minimized by the Nelder-Mead method. On the basis of the outcomes of numerical simulations, the effectiveness and versatility of the developed procedure for the design of novel working machine arms is verified.

Compliance and frequency optimization for energy efficiency in cyclic tasks

Robotica ◽  
2017 ◽  
Vol 35 (12) ◽  
pp. 2363-2380 ◽  
Author(s):  
Mohammad Shushtari ◽  
Rezvan Nasiri ◽  
Mohammad Javad Yazdanpanah ◽  
Majid Nili Ahmadabadi
Keyword(s):  
Cost Function ◽  
Closed Form Solution ◽  
Quadratic Function ◽  
Form Solution ◽  
Reference Trajectory ◽  
Serial Manipulators ◽  
Generalized Force ◽  
Natural Dynamics ◽  
The Cost ◽  
Reference Trajectories

SUMMARYWe present an analytical method for the concurrent calculation of optimal parallel compliant elements and frequency of reference trajectories for serial manipulators performing cyclic tasks. In this approach, we simultaneously shape and exploit the robot's natural dynamics by finding a set of compliant elements and task frequency that result in minimization of an energy-based cost function. The cost function is the integral of a weighted squared norm of the generalized forces. We prove that the generalized force needed for tracking the reference trajectory is a linear function of compliance coefficients and a quadratic function of task frequency. Therefore, the cost function is quadratic with respect to stiffness coefficients and quartic with respect to the task frequency. These properties lead to a well-posed optimization problem with a closed-form solution. Using three case studies, we elucidate the properties of our method.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

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