Positioning of the Robotic Arm Using Different Reinforcement Learning Algorithms

Lindner, Tymoteusz; Milecki, Andrzej; Wyrwał, Daniel

doi:10.1007/s12555-020-0069-6

Cited by 13 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where D denotes the replay buffer to store experience tuples (s t , a t , r t s t+1 ).r t = r(s t , a t ) denotes the reward that was obtained when the agent executed action a t from the action space A while in state s t , belonging to the state space S. γ is a discount factor in the range of 0 to 1, but usually close to 1 [30].…”

Section: Background Of Ddpgmentioning

confidence: 99%

Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning

Li,

Feng,

Zhang

et al. 2024

Biomimetics

View full text Add to dashboard Cite

Intermittent stop–move motion planning is essential for optimizing the efficiency of harvesting robots in greenhouse settings. Addressing issues like frequent stops, missed targets, and uneven task allocation, this study introduced a novel intermittent motion planning model using deep reinforcement learning for a dual-arm harvesting robot vehicle. Initially, the model gathered real-time coordinate data of target fruits on both sides of the robot, and projected these coordinates onto a two-dimensional map. Subsequently, the DDPG (Deep Deterministic Policy Gradient) algorithm was employed to generate parking node sequences for the robotic vehicle. A dynamic simulation environment, designed to mimic industrial greenhouse conditions, was developed to enhance the DDPG to generalize to real-world scenarios. Simulation results have indicated that the convergence performance of the DDPG model was improved by 19.82% and 33.66% compared to the SAC and TD3 models, respectively. In tomato greenhouse experiments, the model reduced vehicle parking frequency by 46.5% and 36.1% and decreased arm idleness by 42.9% and 33.9%, compared to grid-based and area division algorithms, without missing any targets. The average time required to generate planned paths was 6.9 ms. These findings demonstrate that the parking planning method proposed in this paper can effectively improve the overall harvesting efficiency and allocate tasks for a dual-arm harvesting robot in a more rational manner.

show abstract

Section: Background Of Ddpgmentioning

confidence: 99%

Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning

Li,

Feng,

Zhang

et al. 2024

Biomimetics

View full text Add to dashboard Cite

show abstract

“…Therefore, it can be considered less complex to implement than SLAM. These advantages have attracted the interest of many researchers [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Driving of Mobile Robots in Dynamic Environments Based on Deep Deterministic Policy Gradient: Reward Shaping and Hindsight Experience Replay

Park,

Kwon

2024

Biomimetics

View full text Add to dashboard Cite

In this paper, we propose a reinforcement learning-based end-to-end learning method for the autonomous driving of a mobile robot in a dynamic environment with obstacles. Applying two additional techniques for reinforcement learning simultaneously helps the mobile robot in finding an optimal policy to reach the destination without collisions. First, the multifunctional reward-shaping technique guides the agent toward the goal by utilizing information about the destination and obstacles. Next, employing the hindsight experience replay technique to address the experience imbalance caused by the sparse reward problem assists the agent in finding the optimal policy. We validated the proposed technique in both simulation and real-world environments. To assess the effectiveness of the proposed method, we compared experiments for five different cases.

show abstract

“…One of the trending topics in manipulator research is the coordinated motion of multiple manipulators [1] [2] . The emergence of cooperative multi-manipulator systems overcomes the working limit of a single manipulator system by providing larger working space, greater working ability, a more flexible system structure and greater load capacity compared with a single manipulator.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with a single manipulator, a cooperative multi-manipulator system has a more complex organisational structure, which is not a simple combination of multiple manipulators. It needs to address the problems of uncertain environment interaction, feasible control methods and coordinated planning [5] . Multi-manipulator coordination involves the cross-integration of multiple disciplines and is extremely complicated to implement.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive coordinated motion constraint control for cooperative multi-manipulator systems

Zhang

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

Positioning of the Robotic Arm Using Different Reinforcement Learning Algorithms

Cited by 13 publications

References 20 publications

Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning

Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning

Autonomous Driving of Mobile Robots in Dynamic Environments Based on Deep Deterministic Policy Gradient: Reward Shaping and Hindsight Experience Replay

Adaptive coordinated motion constraint control for cooperative multi-manipulator systems

Contact Info

Product

Resources

About