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PINSAT: Parallelized Interleaving of Graph Search and Trajectory Optimization for Kinodynamic Motion Planning

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Ramkumar Natarajan, Shohin Mukherjee, Howie Choset, Maxim Likhachev

Robotics · Submitted 17 Jan 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Inspired by the recent work on edge-based parallel graph search, we present PINSAT, which introduces systematic parallelization in INSAT to achieve lower planning times and higher success rates, while maintaining significantly lower costs over relevant baselines.

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arXiv ID2401.08948 (version 2)
Pith ID64NDITLQ, Pith's permanent record of this paper and its review
CitationHowie Choset, Maxim Likhachev, Ramkumar Natarajan, Shohin Mukherjee. "PINSAT: Parallelized Interleaving of Graph Search and Trajectory Optimization for Kinodynamic Motion Planning." arXiv:2401.08948 (2024). https://pith.science/paper/64NDITLQ
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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.12 · human participants 0.02 · controlled comparison 0.83 · randomized assignment 0.01 · states quantitative result 0.24 · compares with prior results 0.94 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; .

Trajectory optimization is a widely used technique in robot motion planning for letting the dynamics and constraints on the system shape and synthesize complex behaviors. Several previous works have shown its benefits in high-dimensional continuous state spaces and under differential constraints. However, long time horizons and planning around obstacles in non-convex spaces pose challenges in guaranteeing convergence or finding optimal solutions. As a result, discrete graph search planners and sampling-based planers are preferred when facing obstacle-cluttered environments. A recently developed algorithm called INSAT effectively combines graph search in the low-dimensional subspace and trajectory optimization in the full-dimensional space for global kinodynamic planning over long horizons. Although INSAT successfully reasoned about and solved complex planning problems, the numerous expensive calls to an optimizer resulted in large planning times, thereby limiting its practical use. Inspired by the recent work on edge-based parallel graph search, we present PINSAT, which introduces systematic parallelization in INSAT to achieve lower planning times and higher success rates, while maintaining significantly lower costs over relevant baselines. We demonstrate PINSAT by evaluating it on 6 DoF kinodynamic manipulation planning with obstacles.

background contribution method result interpretation main claim

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#sentencerolethe authors' own workstates the main conclusionstrength
s0 Trajectory optimization is a widely used technique in robot motion planning for letting the dynamics and constraints on the system shape and synthesize complex behaviors. background 1.00 0.04 0.02 asserted 1.00
s1 Several previous works have shown its benefits in high-dimensional continuous state spaces and under differential constraints. background 1.00 0.03 0.02 asserted 1.00
s2 However, long time horizons and planning around obstacles in non-convex spaces pose challenges in guaranteeing convergence or finding optimal solutions. background 0.74 0.07 0.02 asserted 0.97
s3 As a result, discrete graph search planners and sampling-based planers are preferred when facing obstacle-cluttered environments. background 0.97 0.06 0.02 asserted 0.99
s4 A recently developed algorithm called INSAT effectively combines graph search in the low-dimensional subspace and trajectory optimization in the full-dimensional space for global kinodynamic planning over long horizons. background 1.00 0.05 0.05 asserted 1.00
s5 Although INSAT successfully reasoned about and solved complex planning problems, the numerous expensive calls to an optimizer resulted in large planning times, thereby limiting its practical use. gap 0.65 0.21 0.05 asserted 1.00
s6 Inspired by the recent work on edge-based parallel graph search, we present PINSAT, which introduces systematic parallelization in INSAT to achieve lower planning times and higher success rates, while maintaining significantly lower costs over relevant baselines. contribution 0.99 0.89 0.74 asserted 1.00
s7 We demonstrate PINSAT by evaluating it on 6 DoF kinodynamic manipulation planning with obstacles. method 0.53 0.95 0.07 asserted 1.00

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

Trajectory optimization is a widely used technique in robot motion planning for letting the dynamics and constraints on the system shape and synthesize complex behaviors. Several previous works have shown its benefits in high-dimensional continuous state spaces and under differential constraints. However, long time horizons and planning around obstacles in non-convex spaces pose challenges in guaranteeing convergence or finding optimal solutions. As a result, discrete graph search planners and sampling-based planers are preferred when facing obstacle-cluttered environments. A recently developed algorithm called INSAT effectively combines graph search in the low-dimensional subspace and trajectory optimization in the full-dimensional space for global kinodynamic planning over long horizons. Although INSAT successfully reasoned about and solved complex planning problems, the numerous expensive calls to an optimizer resulted in large planning times, thereby limiting its practical use. Inspired by the recent work on edge-based parallel graph search, we present PINSAT, which introduces systematic parallelization in INSAT to achieve lower planning times and higher success rates, while maintaining significantly lower costs over relevant baselines. We demonstrate PINSAT by evaluating it on 6 DoF kinodynamic manipulation planning with obstacles.

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