A Multi-Objective Reinforcement Learning Based Controller for Autonomous Navigation in Challenging Environments

Dooraki, Amir Ramezani; Lee, Deok Jin

doi:10.3390/machines10070500

Cited by 15 publications

(2 citation statements)

References 23 publications

(25 reference statements)

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“…More similar to the present study, Camci et al [36] utilize a quadrotor with a depth camera for obstacle avoidance but with discrete actions. Dooraki et al [37] also propose a similar application with continuous actions in the position domain. The present research differs by proposing safety boundaries and enabling heading angle steps together with position steps.…”

Section: Related Workmentioning

confidence: 99%

Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots

2022

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In this study, a novel end-to-end path planning algorithm based on deep reinforcement learning is proposed for aerial robots deployed in dense environments. The learning agent finds an obstacle-free way around the provided rough, global path by only depending on the observations from a forward-facing depth camera. A novel deep reinforcement learning framework is proposed to train the end-to-end policy with the capability of safely avoiding obstacles. The Webots open-source robot simulator is utilized for training the policy, introducing highly randomized environmental configurations for better generalization. The training is performed without dynamics calculations through randomized position updates to minimize the amount of data processed. The trained policy is first comprehensively evaluated in simulations involving physical dynamics and software-in-the-loop flight control. The proposed method is proven to have a 38% and 50% higher success rate compared to both deep reinforcement learning-based and artificial potential field-based baselines, respectively. The generalization capability of the method is verified in simulation-to-real transfer without further training. Real-time experiments are conducted with several trials in two different scenarios, showing a 50% higher success rate of the proposed method compared to the deep reinforcement learning-based baseline.

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Section: Related Workmentioning

confidence: 99%

Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots

2022

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“…By high-level control, we refer to a trajectory of waypoints or attitudes generated by the controller. High-level control works include [5] where an end-to-end approach using deep RL is used to learn to control three axes of quad-copter when RGB-D image is provided as input.…”

Section: Literature Reviewmentioning

confidence: 99%

Reinforcement learning based flight controller capable of controlling a quadcopter with four, three and two working motors

Dooraki

Lee

2020

2020 20th International Conference on Control, Automation and Systems (ICCAS)

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Curiosity is one of the main motives in many of the natural creatures with measurable levels of intelligence for exploration and, as a result, more efficient learning. It makes it possible for humans and many animals to explore efficiently by searching for being in states that make them surprised with the goal of learning more about what they do not know. As a result, while being curious, they learn better. In the machine learning literature, curiosity is mostly combined with reinforcement learning-based algorithms as an intrinsic reward. This work proposes an algorithm based on the drive of curiosity for autonomous learning to control by generating proper motor speeds from odometry data. The quadcopter controlled by our proposed algorithm can pass through obstacles while controlling the Yaw direction of the quad-copter toward the desired location. To achieve that, we also propose a new curiosity approach based on prediction error. We ran tests using on-policy, off-policy, on-policy plus curiosity, and the proposed algorithm and visualized the effect of curiosity in evolving exploration patterns. Results show the capability of the proposed algorithm to learn optimal policy and maximize reward where other algorithms fail to do so.

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A Review on Smart Navigation Techniques for Automated Vehicle

Haldorai,

Murugan

et al. 2024

Artificial Intelligence for Sustainable Development

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A Multi-Objective Reinforcement Learning Based Controller for Autonomous Navigation in Challenging Environments

Cited by 15 publications

References 23 publications

Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots

Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots

Reinforcement learning based flight controller capable of controlling a quadcopter with four, three and two working motors

A Review on Smart Navigation Techniques for Automated Vehicle

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