Model Predictive Trajectory Optimization and Tracking in Highly Constrained Environments

Fu, Zhiqiang; Liu, Xiong; Qian, Zixuan; Leng, Bo; Zeng, Dequan; Huang, Yanjun

doi:10.1007/s12239-022-0081-3

Cited by 18 publications

(3 citation statements)

References 15 publications

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“…For the initial path planning of vehicles, we adopt a straightforward implementation of the A* algorithm at the bottom layer. We then propose an enhanced heuristic function-based adaptive dynamic A* algorithm (ADA*), which addresses the issue of the standard A* algorithm's lengthy computation time [33]. For constraining tree nodes, the upper method introduces a bi-objective expansion mechanism.…”

Section: Icbs-based Multi-intelligent Vehicle Planning Methodsmentioning

confidence: 99%

UGV Parking Planning Based on Swarm Optimization and Improved CBS in High-Density Scenarios for Innovative Urban Mobility

Zeng

Chen

et al. 2023

Drones

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The existence of information silos between vehicles and parking lots means that Unmanned Ground Vehicles (UGVs) repeatedly drive to seek available parking slots, resulting in wasted resources, time consumption and traffic congestion, especially in high-density parking scenarios. To address this problem, a novel UGV parking planning method is proposed in this paper, which consists of cooperative path planning, conflict resolution strategy, and optimal parking slot allocation, intending to avoid ineffective parking seeking by vehicles and releasing urban traffic pressure. Firstly, the parking lot induction model was established and the IACA–IA was developed for optimal parking allocation. The IACA–IA was generated using the improved ant colony algorithm (IACA) and immunity algorithm. Compared with the first-come-first-served algorithm (FCFS), the normal ant colony algorithm (NACA), and the immunity algorithm (IA), the IACA–IA was able to allocate optimal slots at a lower cost and in less time in complex scenarios with multi-entrance parking lots. Secondly, an improved conflict-based search algorithm (ICBS) was designed to efficiently resolve the conflict of simultaneous path planning for UGVs. The dual-layer objective expansion strategy is the core of the ICBS, which takes the total path cost of UGVs in the extended constraint tree as the first layer objective, and the optimal driving characteristics of a single UGV as the second layer objective. Finally, three kinds of load-balancing and unbalanced parking scenarios were constructed to test the proposed method, and the performance of the algorithm was demonstrated from three aspects, including computation, quality and timeliness. The results show that the proposed method requires less computation, has higher path quality, and is less time-consuming in high-density scenarios, which provide a reasonable and efficient solution for innovative urban mobility.

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Section: Icbs-based Multi-intelligent Vehicle Planning Methodsmentioning

confidence: 99%

UGV Parking Planning Based on Swarm Optimization and Improved CBS in High-Density Scenarios for Innovative Urban Mobility

Zeng

Chen

et al. 2023

Drones

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“…Methods regarding this approach were introduced in [11], where a search based multiple node expansion and path optimization methods are elaborated. In [12], a sampling and optimization based path and velocity profile generation method was introduced. Geometric and dynamic constraints have been incorporated for the optimization problem, and the method has been validated through path tracking and obstacle avoidance maneuvers of an actual test vehicle.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

et al. 2023

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This paper presents a trajectory planning and control algorithm of autonomous vehicles for static traffic agent avoidance in multi vehicle urban environments. In urban autonomous driving, the subject vehicle encounters diverse traffic scenes including lane changing, intersection driving, and illegally parked static vehicle avoidance. Among these, dealing with illegally parked static target vehicle is a major challenge to urban autonomous driving due to large velocity difference between ego and target vehicles and interactions with surrounding vehicles. In order to tackle this problem, we introduce a decision making and motion planning framework for static vehicle avoidance considering both the preceding static vehicles and surrounding vehicles. Among the surrounding vehicles, the set of objects with potential collision risk is selected based on the lane boundaries and road geometry. Then, the driving status of the selected target vehicles are classified as normal driving vehicles or parked vehicles based on their longitudinal speed, lateral position and lateral space occupancy. For the preceding parked vehicles, the motion planner generates lateral and longitudinal evasive motion, by taking side lane traffic flow and risk into account. The desired motion is executed by applying optimized control inputs computed by lateral and longitudinal model predictive controllers. The performance validation of the proposed algorithm has been conducted with actual autonomous test vehicles. The test results confirmed that the proposed algorithm can successfully perform evasive maneuvers on urban roads to ensure safety and mitigate collision risk with the surrounding traffic agents.

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“…The evaluation functions of all the aforementioned algorithms can plan a better path [ 24 ], but they all use fixed weights [ 25 ]. Additionally, the distance between the robot and target points and obstacles changes dynamically throughout the motion [ 26 ], and the safety distance and driving speed of the robot from obstacles should change in real time according to environmental conditions in areas without obstacles or close to target points, which lacks rationality [ 27 ]. As a result, the DWA algorithm and IIA are combined in this paper, and the weights are dynamically altered using IIA in response to environmental changes, giving the hybrid algorithm greater environmental flexibility.…”

Section: Introductionmentioning

confidence: 99%

Microrobot Path Planning Based on the Multi-Module DWA Method in Crossing Dense Obstacle Scenario

Zeng

Chen

et al. 2023

Micromachines

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A hard issue in the field of microrobots is path planning in complicated situations with dense obstacle distribution. Although the Dynamic Window Approach (DWA) is a good obstacle avoidance planning algorithm, it struggles to adapt to complex situations and has a low success rate when planning in densely populated obstacle locations. This paper suggests a multi-module enhanced DWA (MEDWA) obstacle avoidance planning algorithm to address the aforementioned issues. An obstacle-dense area judgment approach is initially presented by combining Mahalanobis distance, Frobenius norm, and covariance matrix on the basis of a multi-obstacle coverage model. Second, MEDWA is a hybrid of enhanced DWA (EDWA) algorithms in non-dense areas with a class of two-dimensional analytic vector field methods developed in dense areas. The vector field methods are used instead of the DWA algorithms with poor planning performance in dense areas, which greatly improves the passing ability of microrobots over dense obstacles. The core of EDWA is to extend the new navigation function by modifying the original evaluation function and dynamically adjusting the weights of the trajectory evaluation function in different modules using the improved immune algorithm (IIA), thus improving the adaptability of the algorithm to different scenarios and achieving trajectory optimization. Finally, two scenarios with different obstacle-dense area locations were constructed to test the proposed method 1000 times, and the performance of the algorithm was verified in terms of step number, trajectory length, heading angle deviation, and path deviation. The findings indicate that the method has a smaller planning deviation and that the length of the trajectory and the number of steps can both be reduced by about 15%. This improves the ability of the microrobot to pass through obstacle-dense areas while successfully preventing the phenomenon of microrobots going around or even colliding with obstacles outside of dense areas.

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Model Predictive Trajectory Optimization and Tracking in Highly Constrained Environments

Cited by 18 publications

References 15 publications

UGV Parking Planning Based on Swarm Optimization and Improved CBS in High-Density Scenarios for Innovative Urban Mobility

UGV Parking Planning Based on Swarm Optimization and Improved CBS in High-Density Scenarios for Innovative Urban Mobility

Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

Microrobot Path Planning Based on the Multi-Module DWA Method in Crossing Dense Obstacle Scenario

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