ArticleScientific reports2026
Integrating self-attention and LSTM into TD3 for robust mobile robot navigation in dynamic environments.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mobile robot path planning in dynamic environments is challenging because existing deep reinforcement learning methods lack temporal memory, suffer from inefficient sample utilization under uniform replay, and face credit assignment difficulties with sparse rewards. This paper proposes the Self-Attention LSTM TD3 (SAL-TD3) algorithm, which integrates LSTM networks and multi-head self-attention into the TD3 framework to capture temporal dependencies for proactive obstacle avoidance. A rank-based prioritized experience replay with n-step returns improves sample efficiency, and a composite reward function provides dense feedback for efficient policy learning. Experiments show that SAL-TD3 achieves a 91% success rate (vs. 77% for TD3), reduces path length by 16.6%, and lowers collision rate from 23% to 9%. Generalization tests and real-world robot deployment confirm robust sim-to-real transfer performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.