Model Predictive Path Planning for an Autonomous Ground Vehicle in Rough Terrain

Shin, Jong‐Ho; Kwak, Donghun; Kwak, Kiho

doi:10.1007/s12555-020-0267-2

Cited by 17 publications

(9 citation statements)

References 23 publications

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“…Shin et al have pointed out that the environmental uncertainty encountered by AGV degrades the overall performance of vehicle autonomous navigation, and have proposed a limited optimization, dynamic path planning model based on model predictive control (MPC), such model regards the dynamic model of AGV as equality constraint, and the limited state range and control input as inequality constraints. e stability of the proposed model is improved by passive constraints and particle swarm optimization algorithm, and the superiority of the proposed model has also been verified via the path planned [5].…”

Section: Traditional Path Planningmentioning

confidence: 84%

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

Nai¹,

Yang²,

Lin³

et al. 2022

Journal of Mathematics

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The transportation system of those countries has a huge traffic flow is bearing great pressure on transportation planning and management. Vehicle path planning is one of the effective ways to alleviate such pressure. Deep reinforcement learning (DRL), as a state-of-the-art solution method in vehicle path planning, can better balance the ability and complexity of the algorithm to reflect the real situation. However, DRL has its own disadvantages of higher search cost and earlier convergence to the local optimum, as vehicle path planning issues are usually in a complex environment, and their action set can be diverse. In this paper, a mixed policy gradient actor-critic (AC) model with random escape term and filter operation is proposed, in which the policy weight is both data driven and model driven. The empirical data-driven method is used to improve the poor asymptotic performance, and the model-driven method is used to ensure the convergence speed of the whole model. At the same time, in order to avoid the model converging local optimum, a random escape term has been added in the policy weight update process to overcome the problem that it is difficult to optimize the non-convex loss function, and the random escape term can help to explore the policy in more directions. In addition, filter optimization has been innovatively introduced in this paper, and the step size of each iteration of the model is selected through the filter optimization algorithm to achieve the better iterative effect. Numerical experiment results have shown that the model proposed in this paper can not only improve the accuracy of the solution without losing the accuracy but also speed up the convergence speed and improve the utilization of data.

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Section: Traditional Path Planningmentioning

confidence: 84%

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

Nai¹,

Yang²,

Lin³

et al. 2022

Journal of Mathematics

View full text Add to dashboard Cite

show abstract

“…In early research, most studies focused on single-object optimization problems, such as traversability or avoiding obstacles. Therefore, scholars are concerned about vehicle dynamics to ensure motion stability [7,12,13]. For instance, a probabilistic road map (PRM)-based path planning method considering traversability is proposed for extreme-terrain rappelling rovers [7].…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based 2.5D Multi-Objective Path Planning for Ground Vehicles: Considering Distance and Energy Consumption

Wu,

Huang,

Huang

2023

Electronics

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Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) transform the high-resolution 2.5D map into a small-size map, (2) use a trained deep Q network (DQN) to find the desired path on the small-size map, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks.

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“…The loss L is the difference in the state s t between the output of π net (s t ; θ ) and the action in the dataset a exp,t . This is expressed as L(π net (s t ; θ ), a exp,t ) , and its detailed expression is given in (4). A large number T of datasets D = {(s t , a exp,t )} N t=1 is used to optimize θ .…”

Section: Behavior Cloningmentioning

confidence: 99%

“…The path planning method using optimization theory, such as model predictive control (MPC) [4] and convex optimization [5], uses a kinematic and dynamic model of the vehicle to predict its future trajectory. This method provides an optimal solution that satisfies the objective function and constraints.…”

Section: Introductionmentioning

confidence: 99%

Vision-based Autonomous Driving for Unstructured Environments Using Imitation Learning

Ahn¹,

Kim²,

Park³

2022

Preprint

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Unstructured environments are difficult for autonomous driving. This is because various unknown obstacles are lied in drivable space without lanes, and its width and curvature change widely. In such complex environments, searching for a path in real-time is difficult. Also, inaccurate localization data reduce the path tracking accuracy, increasing the risk of collision. Instead of searching and tracking the path, an alternative approach has been proposed that reactively avoids obstacles in real-time. Some methods are available for tracking global path while avoiding obstacles using the candidate paths and the artificial potential field. However, these methods require heuristics to find specific parameters for handling various complex environments. In addition, it is difficult to track the global path accurately in practice because of inaccurate localization data. If the drivable space is not accurately recognized (i.e., noisy state), the vehicle may not smoothly drive or may collide with obstacles. In this study, a method in which the vehicle drives toward drivable space only using a vision-based occupancy grid map is proposed. The proposed method uses imitation learning, where a deep neural network is trained with expert driving data. The network can learn driving patterns suited for various complex and noisy situations because these situations are contained in the training data. Experiments with a vehicle in actual parking lots demonstrated the limitations of general model-based methods and the effectiveness of the proposed imitation learning method.

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Model Predictive Path Planning for an Autonomous Ground Vehicle in Rough Terrain

Cited by 17 publications

References 23 publications

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

Deep Reinforcement Learning-Based 2.5D Multi-Objective Path Planning for Ground Vehicles: Considering Distance and Energy Consumption

Vision-based Autonomous Driving for Unstructured Environments Using Imitation Learning

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