An energy efficient trajectory tracking controller for car-like vehicles using Model Predictive Control

Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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“…Model predictive techniques have also been applied to the trajectory tracking problem in various works [269][270][271][272][273][274][275].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…Designing a trajectory tracking controller is challenging because of the dynamic impacts of the inertia and actuators of a mobile robot on acquiring the appropriate linear and angular velocities. In [57], a Model Predictive Control (MPC) method was used for the bicycle drive model of car-like robots. Firstly, a trajectory tracking MPC controller was designed using a nonlinear control law.…”

Section: Motion Controlmentioning

confidence: 99%

An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots

et al. 2021

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In recent years, the use of electric Autonomous Wheeled Mobile Robots (AWMRs) has dramatically increased in transport of the production chain. Generally, AWMRs must operate for several hours on a single battery charge. Since the energy density of the battery is limited, energy efficiency becomes a key element in improving material transportation performance during the manufacturing process. However, energy consumption is influenced by the navigation stages, because the type of motion necessary for the AWMR to perform during a mission is totally defined by these stages. Therefore, this paper analyzes methods of energy efficiency that have been studied recently for AWMR navigation stages. The selected publications are classified into planning and motion control categories in order to identify research gaps. Unlike other similar studies, this work focuses on these methods with respect to their implications for the energy consumption of AWMRs. In addition, by using an industrial Self-Guided Vehicle (SGV), we illustrate the direct influence of the motion planning stage on global energy consumption by means of several simulations and experiments. The results indicate that the reaction of the SGV in response to unforeseen obstacles can affect the amount of energy consumed. Hence, energy constraints must be considered when developing the motion planning of AWMRs.

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“…In the local one, energy constraints were added to cost functions [10] or multi-sensors were used to generate optimal trajectories [11]. Using optimal control theory to achieve optimal velocity trajectory [12] and adding a model predictive control for trajectory tracking [13], [14] have been famous ways in the motion control stage. However, none of the previous works considered the effect of load position on a robot's energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position

et al. 2022

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The local path planning, as one of the navigation stages, plays a significant role in the energy consumption of Self-Guided Vehicles (SGV). Since SGV must operate for several hours on a single battery charge to transport loads, its energy consumption is a critical issue. Therefore, this article puts forward an approach for boosting the energy efficiency of the local path planning stage using load position. Unlike other similar works which solely use robots' kinematic and kinetic constraints to develop energy-efficient local path planners, this article considers the effect of load position on SGV's dynamic. In this regard, first, the kinetic model of the differential drive SGV is developed to consider the change of SGV's Center of Mass (CoM) affected by load properties. Second, machine learning methods are used to create two learning models for online estimation of the position of CoM (PoCoM) and prediction of required energy of sample trajectories. Hence, the generated SGV's kinetic model is used to train the learning models. Finally, estimated parameters are employed to add a new constraint to extend the cost function of the local path planner. The outcomes of the study show that the proposed planner generates smoother and shorter paths to pass obstacles and corridors than a general one. Thus, SGV's energy consumption decreases by considering the load effect.INDEX TERMS Energy efficiency, local path planning, dynamic, machine learning, self-guided vehicle.

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An energy efficient trajectory tracking controller for car-like vehicles using Model Predictive Control

Cited by 10 publications

References 16 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots

Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position

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