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This paper proposes a novel neural network-based control policy to enable a mobile robot to navigate safety through environments filled with both static obstacles, such as tables and chairs, and dense crowds of pedestrians. The network architecture uses early fusion to combine a short history of lidar data with kinematic data about nearby pedestrians. This kinematic data is key to enable safe robot navigation in these uncontrolled, human-filled environments. The network is trained in a supervised setting, using expert demonstrations to learn safe navigation behaviors. A series of experiments in detailed simulated environments demonstrate the efficacy of this policy, which is able to achieve a higher success rate than either standard model-based planners or state-of-the-art neural network control policies that use only raw sensor data.
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DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles
This article proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and dense crowds of pedestrians. The policy uses a unique combination of input data to generate the desired steering angle and forward velocity: a short history of lidar data, kinematic data about nearby pedestrians, and a subgoal point. The policy is trained in a reinforcement learning setting using a reward function that contains a novel term based on velocity obstacles to guide the robot to actively avoid pedestrians and move toward the goal. Through a series of 3-D simulated experiments with up to 55 pedestrians, this control policy is able to achieve a better balance between collision avoidance and speed (i.e., higher success rate and faster average speed) than state-of-the-art model-based and learning-based policies, and it also generalizes better to different crowd sizes and unseen environments. An extensive series of hardware experiments demonstrate the ability of this policy to directly work in different real-world environments with different crowd sizes with zero retraining. Furthermore, a series of simulated and hardware experiments show that the control policy also works in highly constrained static environments on a different robot platform without any additional training. Lastly, several important lessons that can be applied to other robot learning systems are summarized.
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- Award ID(s):
- 1830419
- PAR ID:
- 10510588
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Robotics
- Volume:
- 39
- Issue:
- 4
- ISSN:
- 1552-3098
- Page Range / eLocation ID:
- 2700 to 2719
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TRO.2023.3257549
