Although autonomous vehicles (AV) have been rapidly developed, their control technology is not sufficiently mature for daily use, yet not human-centred enough. Some studies regarding trajectory planning are overly con...
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Although autonomous vehicles (AV) have been rapidly developed, their control technology is not sufficiently mature for daily use, yet not human-centred enough. Some studies regarding trajectory planning are overly conservative and the vehicle avoids obstacles in an unhuman-like trajectory which causes discomfort to passengers;meanwhile, other studies are overly simplistic, the transport scenario, and vehicle trajectory are disregarded. The potentialfield (PF) algorithm is one of the most frequently used methods for the trajectory planning of AVs;however, most studies regarding the PF algorithm do not consider driving comfort and smoothness. This paper introduces optimised human-centred dynamic trajectory planning for AVs. The PF algorithm is implemented in a vehicle simulation model, which is integrated with model predictive control (MPC). The reference path is planned by PF algorithm and improved by MPC. The human-centred AV control is proposed in a simulation environment. The proposed planning method achieves a trade-off between safety, driving comfort, and driving smoothness and is validated with several driving simulation scenarios.
This paper addresses a significant challenge within the realm of fixed wing Unmanned Aerial Vehicles (UAVs), namely obstacle avoidance of the Leader-Follower system. The proposed approach involves formulating the Lead...
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ISBN:
(纸本)9798350362350;9798350362343
This paper addresses a significant challenge within the realm of fixed wing Unmanned Aerial Vehicles (UAVs), namely obstacle avoidance of the Leader-Follower system. The proposed approach involves formulating the Leader-Follower problem, consisting of one leader and three followers, with control strategies utilizing the logarithm exponential Quaternion method. Subsequently, the designed control law employing quaternions is applied to both the leader and the follower UAVs. Moreover, a notable contribution of this research lies in the incorporation of adaptive obstacle avoidance. The idea based on potentialfield as obstacle avoidance algorithm, with adaptive techniques for determining obstacle avoidance parameters. These parameters are dynamically adjusted based on the distance between the UAV and the obstacle through a non-linear adaptation function. The comprehensive implementation of these methods is rigorously evaluated by employing a predefined reference path for the leader and conducting numerous scenario-based tests. Random directions of followers were used in case of stuck in local minimum to avoid it;this was applied in case when potential filed force is less than small value. Experiments to test some scenarios of obstacle avoidance were conducted. The final results conclusively demonstrate the stability of the control law even in the presence of obstacles. The effectiveness of the adaptive obstacle avoidance approach is clearly evident from the trajectories of the UAV followers.
How rescue robots reach their destinations quickly and efficiently has become a hot research topic in recent years. Aiming at the complex unstructured environment faced by rescue robots, this paper proposes an artific...
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How rescue robots reach their destinations quickly and efficiently has become a hot research topic in recent years. Aiming at the complex unstructured environment faced by rescue robots, this paper proposes an artificial potential field algorithm based on reinforcement learning. Firstly, use the traditional artificial potentialfield method to perform basic path planning for the robot. Secondly, in order to solve the local minimum problem in planning and improve the robot's adaptive ability, the reinforcement learning algorithm is run by fixing preset parameters on the simulation platform. After intensive training, the robot continuously improves the decision-making ability of crossing typical concave obstacles. Finally, through simulation experiments, it is concluded that the rescue robot can combine the artificial potentialfield method and reinforcement learning to improve the ability to adapt to the environment, and can reach the destination with the optimal route.
Autonomous Ground Vehicles (AGVs) are nominated for a wide range of applications in smart cities. This paper studies and simulates the potential field algorithm used for path planning of such AGVs to extend there appl...
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Autonomous Ground Vehicles (AGVs) are nominated for a wide range of applications in smart cities. This paper studies and simulates the potential field algorithm used for path planning of such AGVs to extend there application for automation and control of smart grids. Different sensors are studied and implemented in the algorithm to help the AGV navigate from an initial position to a goal by avoiding any obstacles in its path. A solution is analyzed and simulated for the problem of AGVs getting trapped in local minima. A new application for the AGVs as assistance in smart grid is also discussed. A real implementation of the project has also been done at the KTH Smart Mobility Lab, Sweden using the Nexus robots and the algorithm was implemented successfully.
In order to solve the problem that is difficult to establish the model of the huge data complex industrial production process. Propose fusion algorithm that based pn the topology of the potential field algorithm and f...
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In order to solve the problem that is difficult to establish the model of the huge data complex industrial production process. Propose fusion algorithm that based pn the topology of the potential field algorithm and fuzzy c mean algorithm, the result indicate: through fuse twice cluster algorithm, obtain the precise clustered number and the, determine the fuzzy neural network's structure, and according to that membership degree, construct the neural network model based on multi-criterion information fusion and the fuzzy technology, through the actual simulation on coal mining production process, the result confirms that the model is valid. This achievement has certain reference value and the guiding sense to the coal mining.
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