Path Planning with Autonomous Obstacle Avoidance Using Reinforcement Learning for Six-axis Arms

Jia, Yinsen; Li, Yichen; Xin, Bo; Chen, Chunlin

doi:10.1109/icnsc48988.2020.9238112

Cited by 1 publication

(1 citation statement)

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“…To accelerate the trajectory planning for serial manipulators in the presence of (dynamic) obstacles, there exist approaches that leverage different machine learning techniques. In reference [ 21 ], the authors propose a reinforcement learning-based strategy for 6-DOF manipulators, which starts with planning an obstacle avoidance path for the terminal element of the manipulator (e.g., tool center point (TCP)). Subsequently, different robot poses are tested along this path so as to avoid collisions between any of the robot joints and the obstacles.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Ionescu

2021

Sensors

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The paper addresses the problem of using machine learning in practical robot applications, like dynamic path planning with obstacle avoidance, so as to achieve the performance level of machine learning model scorers in terms of speed and reliability, and the safety and accuracy level of possibly slower, exact algorithmic solutions to the same problems. To this end, the existing simplex architecture for safety assurance in critical systems is extended by an adaptation mechanism, in which one of the redundant controllers (called a high-performance controller) is represented by a trained machine learning model. This model is retrained using field data to reduce its failure rate and redeployed continuously. The proposed adaptive simplex architecture (ASA) is evaluated on the basis of a robot path planning application with dynamic obstacle avoidance in the context of two human-robot collaboration scenarios in manufacturing. The evaluation results indicate that ASA enables a response by the robot in real time when it encounters an obstacle. The solution predicted by the model is economic in terms of path length and smoother than analogous algorithmic solutions. ASA ensures safety by providing an acceptance test, which checks whether the predicted path crosses the obstacle; in which case a suboptimal, yet safe, solution is used.

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Section: Related Workmentioning

confidence: 99%

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Ionescu

2021

Sensors

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Path Planning with Autonomous Obstacle Avoidance Using Reinforcement Learning for Six-axis Arms

Cited by 1 publication

References 14 publications

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

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