Deployment of multiple robots in real-world scenarios requires simultaneous information exchange from all platforms to ensure effective task performance. The robot's relative position to its peers is an important ...
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ISBN:
(纸本)9798350361087;9798350361070
Deployment of multiple robots in real-world scenarios requires simultaneous information exchange from all platforms to ensure effective task performance. The robot's relative position to its peers is an important data needed to predict collision between robots or task distribution. This paper introduces a robust and simple method for achieving cooperative localization among multiple robots, utilizing a single ultra-wideband (UWB) sensor for each platform. Each robot uses a visual-inertial odometry (VIO) system to track its own trajectory. Given the inherent drift associated with VIO systems, we leverage UWB data to estimate and correct this drift, enhancing each robot's localization accuracy. Our approach substantially improves the result compared with other cooperative localization methods and can even correct the VIO ego-motion.
Antilock braking systems (ABSs) have been developed to improve vehicle control during sudden braking, especially on slippery road surfaces. The objective of such control is to increase wheel traction force in the desi...
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Antilock braking systems (ABSs) have been developed to improve vehicle control during sudden braking, especially on slippery road surfaces. The objective of such control is to increase wheel traction force in the desired direction while maintaining adequate vehicle stability and steerability and reducing the vehicle stopping distance. In this paper, an optimized fuzzy controller is proposed for ABSs. The objective function is defined to maintain the wheel slip to a desired level so that maximum wheel traction force and maximum vehicle deceleration are obtained. All the components of a fuzzy system are optimized using genetic algorithms. The error-based global optimization approach is used for fast convergence near the optimum point. Simulation results show fast convergence and good performance of the controller for different road conditions.
Antilock braking systems (ABS) have been developed to reduce tendency of wheel lock and to improve vehicle control during sudden braking especially on slippery road surfaces. The objective of such control is to increa...
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ISBN:
(纸本)0780390466
Antilock braking systems (ABS) have been developed to reduce tendency of wheel lock and to improve vehicle control during sudden braking especially on slippery road surfaces. The objective of such control is to increase wheel tractive force in the desired direction while maintaining adequate vehicle stability and steerability and also reducing the vehicle stopping distance. In this paper, an optimized hybrid controller using a fuzzy system is proposed for antilock braking systems. The objective function is defined to maintain wheel slip to a desired level so that maximum wheel tractive force and maximum vehicle deceleration are obtained. All components of fuzzy system are optimized using a genetic algorithm and errorbasedoptimization technique. The errorbased global optimization approach is used for fast convergence near optimum point. Simulation results show fast convergence and good performance of the controller for different road conditions.
Antilock braking systems (ABS) have been developed to improve vehicle control during sudden braking especially on slippery road surfaces. The objective of such control is to increase wheel tractive force in the desire...
详细信息
ISBN:
(纸本)0780392809
Antilock braking systems (ABS) have been developed to improve vehicle control during sudden braking especially on slippery road surfaces. The objective of such control is to increase wheel tractive force in the desired direction while maintaining adequate vehicle stability and steerability and also reducing the vehicle stopping distance. In this paper, an optimized Fuzzy controller is proposed for antilock braking systems. The objective function is defined to maintain wheel slip to a desired level so that maximum wheel tractive force and maximum vehicle deceleration are obtained. All components of fuzzy system are optimized using genetic algorithms. The errorbased global optimization approach is used for fast convergence near optimum point. Simulation results show fast convergence and good performance of the controller for different road conditions.
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