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Author

Longji Xu

Bio: Longji Xu is an academic researcher. The author has contributed to research in topics: Computer science & Psychology. The author has an hindex of 1, co-authored 6 publications receiving 6 citations.

Papers
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Journal ArticleDOI
TL;DR: This work proposes a versatile and real-time trajectory optimization method that can generate a high-quality feasible trajectory using a full vehicle model under arbitrary constraints, leveraging the differential property of car-like robots to simplify the trajectory planning problem.
Abstract: —As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, there is no efficient trajectory planning solution capable of spatial-temporal joint optimization due to nonholonomic dynamics, particularly in the presence of unstruc- tured environments and dynamic obstacles. To bridge the gap, we propose a versatile and real-time trajectory optimization method that can generate a high-quality feasible trajectory using a full vehicle model under arbitrary constraints. By leveraging the differential flatness property of car-like robots, we use flat outputs to analytically formulate all feasibility constraints to simplify the trajectory planning problem. Moreover, obstacle avoidance is achieved with full dimensional polygons to generate less conservative trajectories with safety guarantees, especially in tightly constrained spaces. We present comprehensive bench- marks with cutting-edge methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes as open-source packages with the purpose for the reference of the research community. 1

5 citations

Journal ArticleDOI
TL;DR: In this paper , a cross-sectional study was conducted to explore the interactions among high-frequency hearing loss and anxiety as well as brain structure in older adults, finding that gray matter volume decreased in 20 brain regions in the ARHL group compared with the NH group, and a positive correlation existed between highfrequency pure tone audiometry (H-PT) and anxiety scores.
Abstract: Age-related hearing loss (ARHL) is a kind of symmetrical and slow sensorineural hearing loss, which is a common condition in older adults. The characteristic of ARHL is hearing loss beginning in the high-frequency region and spreading toward low-frequency with age. Previous studies have linked it to anxiety, suggesting that brain structure may be involved in compensatory plasticity after partial hearing deprivation. However, the neural mechanisms of underlying ARHL-related anxiety remain unclear. The purpose of this cross-sectional study was to explore the interactions among high-frequency hearing loss and anxiety as well as brain structure in older adults. Sixty-seven ARHL patients and 68 normal hearing (NH) controls participated in this study, and the inclusion criterion of ARHL group was four-frequency (0.5, 1, 2, and 4 kHz) pure tone average (PTA) > 25 decibels hearing level of the better hearing ear. All participants performed three-dimensional T1-weighted magnetic resonance imaging (MRI), pure tone audiometry tests, anxiety and depression scales. Our results found gray matter volume (GMV) decreased in 20 brain regions in the ARHL group compared with the NH group, and a positive correlation existed between high-frequency pure tone audiometry (H-PT) and anxiety scores in the ARHL group. Among 20 brain regions, we also found the GMVs of the middle cingulate cortex (MCC), and the hippocampal/parahippocampal (H-P) regions were associated with H-PT and anxiety scores in all participants separately. However, the depressive symptoms indicated no relationship with hearing assessment or GMVs. Our findings revealed that the crucial role of MCC and H-P in a link of anxiety and hearing loss in older adults.

1 citations

12 Oct 2022
TL;DR: In this article , a decentralized framework for car-like robotic swarm which is capable of real-time planning in cluttered environments is proposed, where 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.
Abstract: 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 real-time planning in cluttered 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, the trajectory is discretized by fixed time steps. Penalty is imposed on the 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 potential collisions. We validate the robustness of our system in simulation and real-world experiments. Code will be released as open-source packages.
Proceedings ArticleDOI
07 Feb 2023
TL;DR: In this article , an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis is proposed, which constructs a penalty field for chassis motion constraints defined in $\mathbb{R}^{3}$ such that the optimal solution space of the trajectory is continuous, resulting in a high quality smooth chassis trajectory.
Abstract: With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in $\mathbb{R}^{3}$ such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm.
Journal ArticleDOI
TL;DR: A decentralized framework for car-like robotic swarm which is capable of real- time planning in unstructured environments and validated the robustness of the system in simulation and real-world experiments is proposed.
Abstract: —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 real- time 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.

Cited by
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Journal ArticleDOI
TL;DR: Li et al. as discussed by the authors proposed an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable autonomous parking, which has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis model to determine the better mode under various scenarios.
Abstract: Autonomous parking (AP) is an emering technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.
12 Oct 2022
TL;DR: In this article , a decentralized framework for car-like robotic swarm which is capable of real-time planning in cluttered environments is proposed, where 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.
Abstract: 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 real-time planning in cluttered 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, the trajectory is discretized by fixed time steps. Penalty is imposed on the 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 potential collisions. We validate the robustness of our system in simulation and real-world experiments. Code will be released as open-source packages.
Journal ArticleDOI
TL;DR: A decentralized framework for car-like robotic swarm which is capable of real- time planning in unstructured environments and validated the robustness of the system in simulation and real-world experiments is proposed.
Abstract: —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 real- time 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.
Proceedings ArticleDOI
29 May 2023
TL;DR: Wang et al. as mentioned in this paper proposed a trajectory optimization method for differential-drive mobile robots with controllable changing shapes in dense 3D environments, where the whole-body trajectory was modeled as a polynomial trajectory that satisfies the nonholonomic dynamics of the base and dynamics of extra joints.
Abstract: Service robots have attracted extensive attention due to specially designed functions, such as mobile manipulators or robots with extra structures. For robots that have changing shapes, autonomous navigation in the real world presents new challenges. In this paper, we propose a trajectory optimization method for differential-drive mobile robots with controllable changing shapes in dense 3D environments. We model the whole-body trajectory as a polynomial trajectory that satisfies the nonholonomic dynamics of the base and dynamics of the extra joints. These constraints are converted into soft constraints, and an activation function for dense sampling is applied to avoid nonlinear mutations. In addition, we guarantee the safety of full shape by limiting the system's distance from obstacles. To comprehensively simulate a large extent of height and width changes, we designed a novel Shape-Changing Robot with a Differential Base (SCR-DB). Our global trajectory optimization gives a smooth and collision-free trajectory for SCR-DB at a low computational cost. We present vast simulations and real-world experiments to validate our performance, including coupled whole-body and independent differential-driven vehicle motion planning.
Proceedings ArticleDOI
21 Apr 2023
TL;DR: In this paper , an autonomous driving path planning algorithm based on quadratic programming (QP) is proposed to deal with the scenario that require high flexibility in obstacle avoidance, such as urban roads.
Abstract: In order to deal with the scenario that require high flexibility in obstacle avoidance, such as urban roads, this paper proposes an autonomous driving path planning algorithm based on quadratic programming (QP). The algorithm proposes an obstacle avoidance cost based on Frenet frame, which can not only satisfy the characteristics of the positive definite quadratic form of the cost function, but also add the obstacle avoidance cost as a soft constraint, and then adapts an iterative solution strategy. The candidate paths are generated by solving the QP problem, the algorithm will output the optimal path, which satisfy the collision detection. The simulation test shows that the algorithm can deal with nudge, lane change and complex obstacle avoidance scenarios.