Trajectory Optimization and Replanning Framework for a Micro Air Vehicle in Cluttered Environments

Lee, Hyeonbeom; Seo, Hoseong; Kim, Hyeong-Geun

doi:10.1109/access.2020.3011401

Cited by 13 publications

(11 citation statements)

References 31 publications

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“…Step 2-7b (Algorithm 2 line [29][30][31][32][33][34][35] The collision between the robot and obstacles is checked. If the robot collides with obstacles, this path candidate is not used for the optimal path.…”

Section: )mentioning

confidence: 99%

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Kobayashi

Motoi

2022

IEEE Access

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Local path planning considering static and dynamic obstacles for a mobile robot is one of challenging research topics. Conventional local path planning methods generate path candidates by assuming constant velocities for a certain period time. Therefore, path candidates consist of straight line and arc paths. These path candidates are not suitable for dynamic environments and narrow spaces. This paper proposes a novel local path planning method based on dynamic window approach with virtual manipulators (DWV). DWV consists of dynamic window approach (DWA) and virtual manipulator (VM). DWA is the local path planning method that performs obstacle avoidance for static obstacles under robot constraints. DWA also generates straight line and arc path candidates by assuming constant velocities. VM generates velocities of reflective motion by using virtual manipulators and environmental information. DWV generates path candidates by variable velocities modified by VM and predicted positions of static and dynamic obstacles. Therefore, in an environment with dynamic obstacles, the obstacle-avoidable paths which include non-straight line and non-arc paths are generated. The effectiveness of the proposed method was confirmed from simulation and experimental results.

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Section: )mentioning

confidence: 99%

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Kobayashi

Motoi

2022

IEEE Access

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“…Xu et al [100] used the Sarsa(λ) reinforcement learning method to achieve autonomous trajectory planning for target tracking and obstacle avoidance in an on-orbit operation environment with unknown target characteristics. Lee et al [101] established a spatial obstacle avoidance type, by optimally adjusting the path points in the collision-free region to satisfy the dynamic constraints based on the target trajectory of elastic optimization (EO). They used the dynamic movement primitives (DMPs) to learn the optimized trajectories and reprogram them to avoid unknown obstacles.…”

Section: Application Of Reinforcement Learning Algorithmsmentioning

confidence: 99%

A Review of Spatial Robotic Arm Trajectory Planning

et al. 2022

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With space technology development, the spatial robotic arm plays an increasingly important role in space activities. Spatial robotic arms can effectively replace humans to complete in-orbit service tasks. The trajectory planning is the basis of robotic arm motion. Its merit has an essential impact on the quality of the completed operation. The research on spatial robotic arm trajectory planning has not yet formed a broad framework categorization, so it is necessary to analyze and deeply summarize the existing research systematically. This paper introduces the current situation of space obstacle avoidance trajectory planning and motion trajectory planning. It discusses the basic principle and practical application of the spatial robotic arm trajectory planning method. The future development trend has also been prospected.

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“…In addition, DMPs were incorporated in the control scheme to modify the flight trajectories and avoid obstacles on the fly. The approach was later extended to incorporate path optimization, where DMPs play a significant tole for real time obstacle avoidance (Lee et al 2020).…”

Section: Autonomous Driving and Field Roboticsmentioning

confidence: 99%

Dynamic Movement Primitives in Robotics: A Tutorial Survey

Saveriano¹,

Abu‐Dakka²,

Kramberger³

et al. 2021

Preprint

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Biological systems, including human beings, have the innate ability to perform complex tasks in versatile and agile manner. Researchers in sensorimotor control have tried to understand and formally define this innate property. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally lead to the formulation of the motor primitives theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems, and are well suited to generate motor commands for artificial systems like robots. In the last decades, Dynamic Movement Primitives (DMPs) have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human-robot co-working, resulting a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMPs formulations in rigorous mathematical terms, and discuss advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state of the art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions.

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Trajectory Optimization and Replanning Framework for a Micro Air Vehicle in Cluttered Environments

Cited by 13 publications

References 31 publications

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

A Review of Spatial Robotic Arm Trajectory Planning

Dynamic Movement Primitives in Robotics: A Tutorial Survey

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