Efficient Robot Motion Planning via Sampling and Optimization

Leu, Jessica; Zhang, Ge; Sun, Liting; Tomizuka, Masayoshi

doi:10.23919/acc50511.2021.9483146

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…In Ref. [ 126 ], sequential convex feasible set is introduced to RRT*, and bad local optima are avoided to get stuck in. In Ref.…”

Section: Overview Of Rrt-based Algorithm Improvementsmentioning

confidence: 99%

Recent advances in Rapidly-exploring random tree: A review

2024

Heliyon

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“…In Ref. [ 126 ], sequential convex feasible set is introduced to RRT*, and bad local optima are avoided to get stuck in. In Ref.…”

Section: Overview Of Rrt-based Algorithm Improvementsmentioning

confidence: 99%

Recent advances in Rapidly-exploring random tree: A review

2024

Heliyon

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“…Prevailing motion planning approaches fall into three categories: search-based [30]- [32], sampling-based [33]- [35] and optimization-based [36]- [39]. Sampling-based planners could raise concerns in risk-sensitive tasks due to their nondeterministic nature, while optimization-based planners are only locally optimal and often need to work with global planners [39]- [43]. Various search-based motion planners are widely adopted by autonomous vehicles for their computation efficiency with well-chosen motion primitives and heuristics [6], [44]- [50].…”

Section: B Planningmentioning

confidence: 99%

Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning

Leu¹,

Wang²,

Tomizuka³

et al. 2022

Preprint

Self Cite

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This paper presents an integrated motion planning system for autonomous vehicle (AV) parking in the presence of other moving vehicles. The proposed system includes 1) a hybrid environment predictor that predicts the motions of the surrounding vehicles and 2) a strategic motion planner that reacts to the predictions. The hybrid environment predictor performs short-term predictions via an extended Kalman filter and an adaptive observer. It also combines short-term predictions with a driver behavior cost-map to make long-term predictions. The strategic motion planner comprises 1) a model predictive control-based safety controller for trajectory tracking; 2) a search-based retreating planner for finding an evasion path in an emergency; 3) an optimization-based repairing planner for planning a new path when the original path is invalidated. Simulation validation demonstrates the effectiveness of the proposed method in terms of initial planning, motion prediction, safe tracking, retreating in an emergency, and trajectory repairing.

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“…Several optimization-based algorithms can be applied to motion planning [34,35], including Covariant Hamiltonian Optimization for Motion Planning (CHOMP) [36,37], Stochastic Trajectory Optimization for Motion Planning (STOMP) [38], and Trajectory Optimization (TrajOpt) [39,40]. Trajectory optimization methods start with an initial trajectory and then minimize an objective function to optimize the trajectory.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Using Combined Informed Rapidly- Exploring Random Tree Star and Particle Swarm Optimization Algorithms

Pohan,

Trilaksono,

Santosa

et al. 2024

IEEE Access

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This study proposes a path planning algorithm that combines an informed rapidly-exploring random tree (RRT*) and particle swarm optimization (PSO) algorithms. The informed RRT* algorithm can build optimal pathways quickly. However, in some cases, the informed RRT* strategy cannot help increase the convergence rate. Meanwhile, the PSO algorithm can be combined with other algorithms to improve algorithm performance. The proposed algorithm will combine the advantages of informed RRT* and PSO algorithms. The proposed algorithm is called the RRT-PSO algorithm. The RRT-PSO algorithm's performance was compared to the informed RRT*, RRT*, RRT-ACS, and informed RRT*-connect algorithms using eight benchmark scenarios. The test results on benchmark scenarios with known optimal values demonstrate that the RRT-PSO algorithm achieves those optimal values. The statistical analysis shows that the RRT-PSO algorithm has a fast convergence rate compared to other comparison algorithms. The test results also demonstrate the RRT-PSO algorithm's stability and robustness compared to other comparison algorithms. Our test results also show the RRT-PSO algorithm's advantages over other PSObased and traditional path planning algorithms. The results show that the RRT-PSO algorithm is suitable for applications requiring a fast and optimal path planning algorithm, such as self-driving cars and unmanned aerial vehicles (UAV).INDEX TERMS Path planning, convergence rate, optimal path, informed rapidly-exploring random tree, particle swarm optimization.

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Efficient Robot Motion Planning via Sampling and Optimization

Cited by 10 publications

References 16 publications

Recent advances in Rapidly-exploring random tree: A review

Recent advances in Rapidly-exploring random tree: A review

Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning

Path Planning Using Combined Informed Rapidly- Exploring Random Tree Star and Particle Swarm Optimization Algorithms

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