The performance of active steering technology directly affects the safety of the vehicle. Therefore, an adaptive back thrusting active steering control strategy based on haptic sharing is proposed. Aiming at the distu...
详细信息
ISBN:
(纸本)9798350379860;9798350379877
The performance of active steering technology directly affects the safety of the vehicle. Therefore, an adaptive back thrusting active steering control strategy based on haptic sharing is proposed. Aiming at the disturbance caused by driver's steering torque intervention and unknown steering resistance moment. Firstly, the perturbations are divided into known time-varying perturbations and unknown time-varying bounded perturbations, and then the adaptive law is designed for the unknown time-varying bounded perturbations. Finally, an adaptive backthrust control strategy based on haptic sharing is designed, and the parameter adaptive law is designed for the parameters. Through the joint simulation experiment based on CarSim/MATLAB, the effect of the active steering motion control of the vehicle is verified. The results show that the proposed strategy can ensure the precise lateral motion control of intelligent vehicles on roads with different adhesion coefficients and at different speeds, while improving the robustness of the system to unknown disturbances and parameter changes.
Physical falls are a significant threat for the elderly population. This research study reviews the existing AI-based fall detection systems, which can also predict falls with an increased accuracy. Even though many f...
详细信息
Unmanned surface vehicles (USVs) equipped with autonomous controllers have emerged as a viable alternative to human drivers in complex and unstructured task environments. However, challenges remain in terms of social ...
详细信息
ISBN:
(纸本)9798350364200;9798350364194
Unmanned surface vehicles (USVs) equipped with autonomous controllers have emerged as a viable alternative to human drivers in complex and unstructured task environments. However, challenges remain in terms of social trust and flexibility when handling complex multitasking scenarios, which hinder the widespread adoption of unmanned operations. To address this, a Human-Driving Co-driving framework to achieve the human-in-the loop control is proposed in our work. A nonlinear Model Predictive control (MPC) framework with nominal dynamic model will be solved for optimal tracking accuracy. And a confidence-based dynamic authority allocation method considering temporal stochastic driver model enable the flexible and efficient shared control. The effectiveness of our proposed approach is validated through comprehensive simulation experiments.
Humans have latency in their visual perception system between observation and action. Any action we take is based on an earlier observation since, by the time we act, the state has already changed, and we got a new ob...
详细信息
ISBN:
(纸本)9781665491907
Humans have latency in their visual perception system between observation and action. Any action we take is based on an earlier observation since, by the time we act, the state has already changed, and we got a new observation. In autonomous driving, this latency is also present, determined by the amount of time the control algorithm needs to process information before acting. This algorithmic perception latency can be reduced by massive computing power via GPUs and FPGAs, which is improbable in automobile platforms. Thus, it is a reasonable assumption that the algorithmic perception latency is inevitable. Many researchers have developed different neural network driving models without consideration of the algorithmic perception latency. This paper studies the latency effect on vision-based neural network autonomous driving in the lane-keeping task and proposes a vision-based novel neural network controller, the Adaptive Neural Ensemble controller (ANEC) that is inspired by the near/far gaze distribution of human drivers during lane-keeping. ANEC was tested in Gazebo 3D simulation environment with Robot Operating System (ROS) which showed the effectiveness of ANEC in dealing with algorithmic latency. The source code is available at https://***/jrkwon/oscar/tree/devel_anec.
Binary information trees are key visualization and evaluation tools, particularly during the passing of algorithms and synthesizing designs. This paper is an interactive tool for producing binary information trees ove...
详细信息
Efficient motion planning and control for multiple mobile robots in industrial automation and indoor logistics face challenges such as trajectory generation and collision avoidance in complex environments. We propose ...
详细信息
ISBN:
(纸本)9798350377712;9798350377705
Efficient motion planning and control for multiple mobile robots in industrial automation and indoor logistics face challenges such as trajectory generation and collision avoidance in complex environments. We propose a hybrid, sequential method combining Bird's-Eye-View vision-based continuous Deep Reinforcement Learning (DRL) with Model Predictive control (MPC). DRL generates candidate trajectories in complex environments, while MPC refines these trajectories to ensure adherence to kinematic and dynamic constraints of the robot, as well as constraints modeling humans' current and predicted future positions. In this study, the DRL utilizes a Deep Deterministic Policy Gradient model for trajectory generation, demonstrating its capability to navigate non-convex obstacles, a task that might pose challenges for MPC. We demonstrate that the proposed hybrid DRL-MPC model performs favorably in handling new scenarios, computational efficiency, time to destination, and adaptability to complex multi-robot situations when compared to pure DRL or pure MPC approaches.
Machine learning has the potential for transforming the workplace across various industries. It can streamline repetitive tasks, improve decisionmaking, enhance safety, and increase operational efficiency. Rather than...
详细信息
The rapid expansion of the Internet of Things (IoT) poses challenges to certain applications, such as Digital Twin (DT). While data from user devices can be filtered by human intelligence, this is not feasible for IoT...
详细信息
ISBN:
(纸本)9798350354720;9798350354713
The rapid expansion of the Internet of Things (IoT) poses challenges to certain applications, such as Digital Twin (DT). While data from user devices can be filtered by human intelligence, this is not feasible for IoT devices. Owing to the voluminous data generated by IoT devices that require transmission and computing, traditional cloud computing architectures may no longer guarantee the Quality of Experience (QoE), even causing network congestion. To address this issue, we propose a novel Cloud-Network-Edge-Terminal (CNET) model, which includes an intelligent edge layer for filtering IoT data. The computing paradigm shift indicates that the network will provide services at the edge rather than in the cloud, which is so-called service localization. To demonstrate the benefits of service localization, we use integrated user requirement descriptions to measure QoE, specifically the concepts of Service Requirement Zone (SRZ) and User Satisfaction Ratio (USR). Additionally, we conduct extensive numerical simulations to evaluate the model's performance under varying Degrees of Localization (DoL). Our results show that service localization can significantly improve USR even in changing network conditions.
In this paper, we present a spherical wheel-legged mobile robot, aiming to meet the demands of adaptability to complex terrains and high maneuverability. It consists of a spherical main body and five-bar linkage paral...
详细信息
ISBN:
(纸本)9798350377712;9798350377705
In this paper, we present a spherical wheel-legged mobile robot, aiming to meet the demands of adaptability to complex terrains and high maneuverability. It consists of a spherical main body and five-bar linkage parallel wheel-legged mechanisms. It can switch between legged and spherical modes by extending and retracting its legs according to the demands of the actual environment, thereby enhancing the overall mobility of the robot. By designing the Linear Quadratic Regulator (LQR) controller, we achieve the impact-resistant leg balancing motion and autonomous pitch adjustment for the robot on inclined surfaces in the legged configuration. For the rolling control in the spherical configuration, a hierarchical sliding mode control method and Proportional-Integral-Derivative controller (PID) are employed to control the rolling and turning of the robot. We verify the robustness of the robot in wheel-legged configuration against disturbances and the stability of its motion in spherical configuration.
作者：
Poovizhi, J Mary RamyaDevi, R.
Schools of Computing Sciences Department of Computer Science Chennai India
Schools of Computing Sciences Department of Computer Applications Chennai India
The abstraction of IT infrastructure enables the integration and pooling of IT resources to be shared across several applications to compensate for declining resources. Growing business needs virtualization offers a c...
详细信息
暂无评论