A Decoupled Trajectory Planning Framework Based on the Integration of Lattice Searching and Convex Optimization

Yang, Meng; Wu, Yangming; Gu, Qing; Liu, Li

doi:10.1109/access.2019.2940271

Cited by 39 publications

(14 citation statements)

References 44 publications

(150 reference statements)

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“…The convex polygonal constraints are constructed with the process shown in Figure 9 and allow the OCP solver to handle the inherently nonconvex obstacle avoidance problem easily. Combined, this gives us a fast solution to (20), which is a locally optimal and dynamically feasible trajectory between the start and goal poses.…”

Section: Methods Summarymentioning

confidence: 99%

“…. , N , where N is the number of shooting intervals used in the transcription of (20). With h = t f /N being the shooting interval duration, we have t k = h • k. The convex regions that define the obstacle avoidance constraints are generated along the solution of the hybrid A trajectory, i.e., the initial guess.…”

Section: A Convex Collision Avoidance Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Two-Stage Optimized Trajectory Planning for ASVs Under Polygonal Obstacle Constraints: Theory and Experiments

et al. 2020

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We propose a method for energy-optimized trajectory planning for autonomous surface vehicles (ASVs), which can handle arbitrary polygonal maps as obstacle constraints. The method comprises two stages: The first is a hybrid A search that finds a dynamically feasible trajectory in a polygonal map on a discretized configuration space using optimal motion primitives. The second stage uses the resulting hybrid A trajectory as an initial guess to an optimal control problem (OCP) solver. In addition to providing the OCP with a warm start, we use the initial guess to create convex regions encoded as halfspace descriptions, which converts the inherent nonconvex obstacle constraints into a convex and smooth representation. The OCP uses this representation in order to optimize the initial guess within a collision-free corridor. The OCP solves the trajectory planning problem in continuous state space. Our approach solves two challenges related to optimization-based trajectory planning: The need for a dynamically feasible initial guess that can guide the solver away from undesirable local optima and the ability to represent arbitrary obstacle shapes as smooth constraints. The method can take into account external disturbances such as wind or ocean currents. We compare our method to two similar trajectory planning methods in simulation and have found significant computation time improvements. Additionally, we have validated the method in full-scale experiments in the Trondheim harbor area.

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Section: Methods Summarymentioning

confidence: 99%

Section: A Convex Collision Avoidance Constraintsmentioning

confidence: 99%

Two-Stage Optimized Trajectory Planning for ASVs Under Polygonal Obstacle Constraints: Theory and Experiments

et al. 2020

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“…The motion planning of an autonomous vehicle can be divided into two approaches [5]: Direct planning methods and decoupled planning methods based on whether the state space of the configuration space is decoupled.…”

Section: A Related Workmentioning

confidence: 99%

“…The cost of edge e n k →n k+1 is shown in the following equation, where n k is a node in layer L k and n k+1 is a node in layer L k+1 . The first cost term represents the length of each edge using the Euclidean distance between two nodes, and it prevents the ego vehicle from changing lanes frequently by assigning a penalty to a long edge, as shown in (5).…”

Section: A Optimal Spatial Path Generationmentioning

confidence: 99%

A Hierarchical Motion Planning Framework for Autonomous Driving in Structured Highway Environments

Kim

et al. 2022

IEEE Access

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This paper presents an efficient hierarchical motion planning framework with a long planning horizon for autonomous driving in structured environments. A 3D motion planning with time information is a non-convex problem because there exists more than one local minimum point and various constraints such as roads and obstacles. Accordingly, to deal with high computational complexity and the problem of falling into a local minimum in an enormous solution space, a decoupled method is utilized, that consists of two steps: Long-term planning and short-term planning. First, the long-term planner provides reasonable far-sighted behavior through two processes. In the first process, a rough path that includes a driving strategy is generated in the 2D spatial space. Then, the jump point search algorithm is utilized with time information on the path to reduce the computational burden of A*, giving an acceptable quality of solution at the same time. In this step, a safe, comfortable, and dynamically feasible trajectory is generated. Next, the short-term planner optimizes a short-sighted trajectory using particle swarm optimization. In this method, a steering angle set is encoded as a particle, resulting in a safe, comfortable, and kinodynamically feasible trajectory. The proposed algorithm is implemented and evaluated in a range of vehicle-in-the-loop simulation scenarios, which include various virtual static and dynamic obstacles generated by Intelligent Driver Model. In the evaluation results, the proposed method reduced the computation time by up to 0.696 s with increasing the step cost by up to about 3%. The proposed algorithm is executed every 100 ms for a planning horizon of 10 seconds, and the average computation time is 31 ms with the worst-case computation time of 94 ms.

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“…As for the optimization, there are two main approaches. One is to optimize the stations at discretized time instants directly [15], [16]. Another is to parameterize the speed profile with polynomials and convert the speed optimization problem into finding optimal polynomial coefficients which satisfy certain constraints [14].…”

Section: Introductionmentioning

confidence: 99%

Speed Planning Using Bezier Polynomials with Trapezoidal Corridors

Li,

Xie,

et al. 2021

Preprint

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To generate safe and real-time trajectories for an autonomous vehicle in dynamic environments, path and speed decoupled planning methods are often considered. This paper studies speed planning, which mainly deals with dynamic obstacle avoidance given the planning path. The main challenges lie in the decisions in non-convex space and the trade-off between safety, comfort and efficiency performances. This work uses dynamic programming to search heuristic waypoints on the S-T graph and to construct convex feasible spaces. Further, a piecewise Bézier polynomials optimization approach with trapezoidal corridors is presented, which theoretically guarantees the safety and optimality of the trajectory. The simulations verify the effectiveness of the proposed approach.

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A Decoupled Trajectory Planning Framework Based on the Integration of Lattice Searching and Convex Optimization

Cited by 39 publications

References 44 publications

Two-Stage Optimized Trajectory Planning for ASVs Under Polygonal Obstacle Constraints: Theory and Experiments

Two-Stage Optimized Trajectory Planning for ASVs Under Polygonal Obstacle Constraints: Theory and Experiments

A Hierarchical Motion Planning Framework for Autonomous Driving in Structured Highway Environments

Speed Planning Using Bezier Polynomials with Trapezoidal Corridors

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