Gene

Robot

Motion planning

Cartography

Computer security

Robustness (evolution)

Real-time computing

Biochemistry

Computer science

Collision

Mathematical optimization

Path length

Computer network

Reinforcement learning

Simulation

Mathematics

Geography

GRASP

Path (computing)

Artificial intelligence

Chemistry

Programming language

Terrain

Artificial neural network

Recurrent neural network

Computers and Electronics in Agriculture

In unstructured orchard environments, picking a target fruit without colliding with neighboring branches is a significant challenge for guava-harvesting robots. This paper introduces a fast and robust collision-free path-planning method based on deep reinforcement learning. A recurrent neural network is first adopted to remember and exploit the past states observed by the robot, then a deep deterministic policy gradient algorithm (DDPG) predicts a collision-free path from the states. A simulation environment is developed and its parameters are randomized during the training phase to enable recurrent DDPG to generalize to real-world scenarios. We also introduce an image processing method that uses a deep neural network to detect obstacles and uses many three-dimensional line segments to approximate the obstacles. Simulations show that recurrent DDPG only needs 29 ms to plan a collision-free path with a success rate of 90.90%. Field tests show that recurrent DDPG can increase grasp, detachment, and harvest success rates by 19.43%, 9.11%, and 10.97%, respectively, compared to cases where no collision-free path-planning algorithm is implemented. Recurrent DDPG strikes a strong balance between efficiency and robustness and may be suitable for other fruits.

Collision-free path planning for a guava-harvesting robot based on recurrent deep reinforcement learning