Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement

Cheng, Jie; Chen, Yingbing; Zhang, Qingwen; Gan, Lu; Liu, Chengju; Liu, Ming

doi:10.1109/icra46639.2022.9812405

Cited by 21 publications

(5 citation statements)

References 18 publications

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“…Fossen et al [4] proposed a method based on the extended Kalman filter, which uses the AIS system to obtain information such as the position and velocity of a ship in real-time and predicts the ship's trajectory by building a ship motion model and designing a Kalman filter. Cheng [5] proposed a real-time trajectory planning method combining a Gaussian process and incremental optimization, which applies to the path planning of an autonomous vehicle. A Gaussian process regression model is used to predict the vehicle trajectory, and then adjustment and optimization by an incremental optimization algorithm ensures that the constraints and optimization objectives are satisfied.…”

Section: Dynamical Modeling Based Approachmentioning

confidence: 99%

GAT-ABiGRU Based Prediction Model for AUV Trajectory

Zhao,

Zhang,

et al. 2024

Applied Sciences

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Autonomous underwater vehicles (AUVs) are critical components of current maritime operations. However, because of the complicated marine environment, AUVs are at significant risk of being lost, and such losses significantly impact the continuity and safety of aquatic activities. This article suggests a methodology for forecasting the trajectory of lost autonomous underwater vehicles (AUVs) based on GAT-ABiGRU. Firstly, the time-series data of the AUV are transformed into a graph structure to represent the dependencies between data points. Secondly, a graph attention network is utilized to capture the spatial features of the trajectory data, while an attention-based bidirectional gated recurrent unit network learns the temporal features of the trajectory data; finally, the predicted drift trajectory is obtained. The findings show that the GAT-ABiGRU model outperforms previous trajectory prediction models, is highly accurate and robust in drift trajectory prediction, and presents a new method for forecasting the trajectory of wrecked AUVs.

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Section: Dynamical Modeling Based Approachmentioning

confidence: 99%

GAT-ABiGRU Based Prediction Model for AUV Trajectory

Zhao,

Zhang,

et al. 2024

Applied Sciences

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“…Spatial-temporal decomposition is a widely used strategy in autonomous driving to improve planning efficiency, where speed planning is the temporal part. Works [16], [17] use search-based speed planning to search for a feasible speed profile. Works [18], [19] propose optimization-based speed planning methods to generate a smooth S-T (Space-Time) curve.…”

Section: Speed Planningmentioning

confidence: 99%

“…Speed planning generates a time span satisfying dynamic obstacle avoidance and kinematic feasibility. We adopt search-based speed planning method, similar to [16], and conduct a 1-dimension A* search on the S-T graph. The control input is set as the tangential acceleration A = {a min ≤ a 1 , .…”

Section: B Search-based Speed Planningmentioning

confidence: 99%

Decentralized Planning for Car-Like Robotic Swarm in Unstructured Environments

Ma¹,

Han²,

Zhang³

et al. 2022

Preprint

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Robot swarm is a hot spot in robotic research community. In this paper, we propose a decentralized framework for car-like robotic swarm which is capable of realtime planning in unstructured environments. In this system, path finding is guided by environmental topology information to avoid frequent topological change, and search-based speed planning is leveraged to escape from infeasible initial value's local minima. Then spatial-temporal optimization is employed to generate a safe, smooth and dynamically feasible trajectory. During optimization, penalty is imposed on signed distance between agents to realize collision avoidance, and differential flatness cooperated with limitation on front steer angle satisfies the non-holonomic constraints. With trajectories broadcast to the wireless network, agents are able to check and prevent from potential collisions. We validate the robustness of our system in simulation and real-world experiments. Code will be released as open-source packages.

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“…The trajectory-planning methods focus on shorter paths, lower energy consumption, and other constrained kinetic conditions [20]. With the development stage comes to the practical application; the technologies of real-time trajectory updating [21,22] and noise tolerance [23,24] are developed to face the dynamic environment. Further optimization of trajectory planning is developed by applying artificial intelligence techniques like neural networks, evaluation algorithms, swarm intelligence, and fuzzy logic [25,26].…”

Section: Introductionmentioning

confidence: 99%

User Experience Evaluation of Upper Limb Rehabilitation Robots: Implications for Design Optimization: A Pilot Study

Yeh,

Chou

2023

Sensors

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With the development of science and technology, people are trying to use robots to assist in stroke rehabilitation training. This study aims to analyze the result of the formative test to provide the orientation of upper limb rehabilitation robot design optimization. We invited 21 physical therapists (PTs) and eight occupational therapists (OTs) who had no experience operating any upper limb rehabilitation robots before, and 4 PTs and 1 OT who had experience operating upper limb rehabilitation robots. Data statistics use the Likert scale. The general group scored 3.5 for safety-related topics, while the experience group scored 4.5. In applicability-related questions, the main function score was 2.3 in the general group and 2.4 in the experience group; and the training trajectory score was 3.5 in the general group and 5.0 in the experience group. The overall ease of use score was 3.1 in the general group and 3.6 in the experience group. There was no statistical difference between the two groups. The methods to retouch the trajectory can be designed through the feedback collected in the formative test and gathering further detail in the next test. Further details about the smooth trajectory must be confirmed in the next test. The optimization of the recording process is also important to prevent users from making additional effort to know it well.

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Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement

Cited by 21 publications

References 18 publications

GAT-ABiGRU Based Prediction Model for AUV Trajectory

GAT-ABiGRU Based Prediction Model for AUV Trajectory

Decentralized Planning for Car-Like Robotic Swarm in Unstructured Environments

User Experience Evaluation of Upper Limb Rehabilitation Robots: Implications for Design Optimization: A Pilot Study

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