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Open Access

Deep Reinforcement Learning Based Mobile Robot Navigation: A Review

Department of Automation, Tsinghua University, Beijing 100084, China
Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
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Abstract

Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks. Then we systematically compare and analyze the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation. Next, we describe the development of DRL-based navigation. Last, we discuss the challenges and some possible solutions regarding DRL-based navigation.

Keywords

mobile robot navigationobstacle avoidancedeep reinforcement learning

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Tsinghua Science and Technology
Volume 26 Issue 5,
October 2021
Pages 674-691
DOI: 10.26599/TST.2021.9010012
Cite this article:
Zhu K, Zhang T. Deep Reinforcement Learning Based Mobile Robot Navigation: A Review. Tsinghua Science and Technology, 2021, 26(5): 674-691. https://doi.org/10.26599/TST.2021.9010012
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10.26599/TST.2021.9010012.T001Simple comparison of different DRL-based navigation scenarios.

Navigation scenarioStatic obstacleDynamic obstacleStructure continuous obstacleObstacle scaleObstacle velocityCooperationRandomness
Local obstacle avoidanceYYNLowLow - -
Indoor navigationYNYLow - - -
Multi-robot navigationYYNHighHighYLow
Social navigationYYNHighHighNHigh

10.26599/TST.2021.9010012.T002Simple comparison of relevant references.

Application scenarioReferenceAlgorithmPerception typeAction spaceReward settingSimulationReal systemYear
Local obstacle avoidance[32]Uncertainty-aware RLCCon, 2 -YY2017
[24]DDQNADis, 3DenseYY2017
[34]ADDPG (Asynchronous DDPG)ACon, 2DenseYY2017
[26]Fast-RDPG (Fast Recurrent DPG)BCon, 1DenseYN2017
[36]SF-RL (Successor Feature RL)DDis, 4DenseYY2017
[37]DDQNADis, 8DenseYY2018
[28]Salient region detection + ACCCon, 2–YN2018
[38]IL (Imitation Learning) + CPOACon, 2DenseYY2018
[39]DDPGACon, 2DenseYY2018
[42]SAC (Soft AC)DCon, 2DenseYY2019
[25]DDQNADis, 3DenseYY2019
[27]Fast-RDPG (Fast Recurrent DPG)BCon, 1DenseYN2019
[43]DDPGACon, 2DenseYN2020
[33]ICM A3C (Intrinstic Curiosity)A/CDis, -DenseYY2020
[40]Improved A3CACon, 2SparseYN2020
[35]DDQNCDis, 3DenseYY2020
Indoor navigation[44]Nav A3C (A3C + LSTM)CDis, 8SparseYN2017
[45]AI2-THOR (Deep siamese AC network)CDis, 4DenseYY2017
[54]LSTM + DRLCDis, 3DenseYY2018
[51]A2CAT-VNCDis, 8DenseYN2019
[52]IMPALACDis, 3SparseYY2020
[55]HISNav frameworkD––YY2020
[50]AppoNav (LSTM+PPO)CDis, 8SparseYN2020
Multi-robot navigation[58]CADRL (Coll. Avoidance with Deep RL)ECon, 2DenseYN2016
[60]Parallel PPOACon, 2DenseYN2018
[61]Hybrid-RL (Parallel PPO)ACon, 2DenseYY2018
[63]PDDPG (Parallel DDPG )ACon, 2DenseYN2019
[64]PPOACon, 2DenseYY2019
[65]PRIMAL (IL + A3C + LSTM)EDis, 5DenseYY2019
Social navigation[67]SA-CADRLECon, 2DenseYY2017
[69]GA3C-CADRLEDis, 11DenseYY2018
[72]A3C–Dis, –DenseYY2019
[71]PPO + LSTM + collision predictionECon, 2DenseYN2019
[73]Frozone + DRLECon, 2DenseYY2020

Note: Perception type: “A” denotes the laser range finder, “B” denotes ultrasonic sonar or other range finders, “C” denotes the monocular camera, “D” denotes the depth camera, and “E” denotes agent-level data provided by the system. Action space: “Con” denotes continuous action, “Dis” denotes discrete action, and the number denotes dimension.