PMID- 38139648
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231225
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 24
DP  - 2023 Dec 13
TI  - Path Planning of a Mobile Robot for a Dynamic Indoor Environment Based on an 
      SAC-LSTM Algorithm.
LID - 10.3390/s23249802 [doi]
LID - 9802
AB  - This paper proposes an improved Soft Actor-Critic Long Short-Term Memory 
      (SAC-LSTM) algorithm for fast path planning of mobile robots in dynamic 
      environments. To achieve continuous motion and better decision making by 
      incorporating historical and current states, a long short-term memory network 
      (LSTM) with memory was integrated into the SAC algorithm. To mitigate the memory 
      depreciation issue caused by resetting the LSTM's hidden states to zero during 
      training, a burn-in training method was adopted to boost the performance. 
      Moreover, a prioritized experience replay mechanism was implemented to enhance 
      sampling efficiency and speed up convergence. Based on the SAC-LSTM framework, a 
      motion model for the Turtlebot3 mobile robot was established by designing the 
      state space, action space, reward function, and overall planning process. Three 
      simulation experiments were conducted in obstacle-free, static obstacle, and 
      dynamic obstacle environments using the ROS platform and Gazebo9 software. The 
      results were compared with the SAC algorithm. In all scenarios, the SAC-LSTM 
      algorithm demonstrated a faster convergence rate and a higher path planning 
      success rate, registering a significant 10.5 percentage point improvement in the 
      success rate of reaching the target point in the dynamic obstacle environment. 
      Additionally, the time taken for path planning was shorter, and the planned paths 
      were more concise.
FAU - Zhang, Yongchao
AU  - Zhang Y
AUID- ORCID: 0009-0003-4255-3690
AD  - School of Intelligent Manufacturing, Taizhou University, Taizhou 318000, China.
FAU - Chen, Pengzhan
AU  - Chen P
AUID- ORCID: 0000-0001-7188-362X
AD  - School of Intelligent Manufacturing, Taizhou University, Taizhou 318000, China.
LA  - eng
GR  - 62163014/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20231213
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10747912
OTO - NOTNLM
OT  - SAC-LSTM algorithm
OT  - burn-in mechanism
OT  - mobile robot
OT  - path planning
OT  - prioritized experience replay mechanism
COIS- The authors declare no conflict of interest.
EDAT- 2023/12/23 12:43
MHDA- 2023/12/23 12:44
PMCR- 2023/12/13
CRDT- 2023/12/23 01:24
PHST- 2023/10/12 00:00 [received]
PHST- 2023/12/01 00:00 [revised]
PHST- 2023/12/09 00:00 [accepted]
PHST- 2023/12/23 12:44 [medline]
PHST- 2023/12/23 12:43 [pubmed]
PHST- 2023/12/23 01:24 [entrez]
PHST- 2023/12/13 00:00 [pmc-release]
AID - s23249802 [pii]
AID - sensors-23-09802 [pii]
AID - 10.3390/s23249802 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Dec 13;23(24):9802. doi: 10.3390/s23249802.