A Framework for Unknown Environment Manipulator Motion Planning via Model Based Realtime Rehearsal

Um, Dugan; Ryu, Dongseok; Kang, Sungchul

doi:10.1007/978-3-642-33932-5_58

Cited by 4 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each link is assumed to be equipped with IPA sensors so that the entire body is covered by a collision shield. Two algorithms are tested for comparison: sensor-based RRT (see [11] for more details) and BNM algorithm introduced in this paper. The same sensor model and workspace configurations are applied.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Sequential mapping of a local area followed by a local path planning is a natural step in sensor-based motion planning algorithm. In [11], a novel framework for an unknown environment path planning of manipulator type robots is proposed. The framework proposed is a sensor-based planner composed of a sequence of multiple MBPs (model based planners) in the notion of cognitive planning using real-time rehearsal.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SPAM for a Manipulator by Best Next Move in Unknown Environments

Ryu

2013

ISRN Robotics

Self Cite

View full text Add to dashboard Cite

We propose a SPAM (simultaneous planning and mapping) technique for a manipulator-type robot working in an uncertain environment via a novel Best Next Move algorithm. Demands for a smart decision to move a manipulator such as humanoid arms in uncertain or crowded environments call for a simultaneous planning and mapping technique. In the present work, we focus more on rapid map generation rather than global path search to reduce ignorance level of a given environment. The motivation is that the global path quality will be improved as the ignorance level of the unknown environment decreases. The 3D sensor introduced in the previous work has been improved for better mapping capability and the real-time rehearsal idea is used for -space cloud point generation. Captured cloud points by 3D sensors, then, create an instantaneous -space map whereby the Best Next Move algorithm directs the local motion of the manipulator. The direction of the Best Next Move utilizes the gradient of the density distribution of the -nearest-neighborhood sets in -space. It has a tendency of traveling along the direction by which the point clouds spread in space, thus rendering faster mapping of -space obstacles possible. The proposed algorithm is compared with a sensor-based algorithm such as sensor-based RRT for performance comparison. Performance measures, such as mapping efficiency, search time, and total number of -space point clouds, are reported as well. Possible applications include semiautonomous telerobotics planning and humanoid arm path planning.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SPAM for a Manipulator by Best Next Move in Unknown Environments

Ryu

2013

ISRN Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…After a local minimum was detected and its enclosure was defined, the robot traveled to a safe destination out of the deadlock enclosure and then closed the enclosure by a virtual wall placed at the entrance of this enclosure, which can be identified by a special laser finding sensor on the robot on the next visit. In addition, some studies [33,34] use the Bug algorithm [35] and its variations to keep the robot away from falling into traps.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Robots Navigation in Internet of Things Indoor Systems

et al. 2021

View full text Add to dashboard Cite

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.

show abstract

“…Facing the unknown environment in application, Um and Ryu 13 proposed RRT-Cabomba algorithm based on the sampling method RRT 14 which controls the IPA sensitive skin equipped robot to perceive and plan alternately. Further, they also adopted the bug algorithm in RRT-Cabomba to implement local minima avoidance.…”

Section: Introductionmentioning

confidence: 99%

A snake-inspired path planning algorithm based on reinforcement learning and self-motion for hyper-redundant manipulators

Lin

Wang

Xiao

et al. 2022

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Redundant manipulators are flexible enough to adapt to complex environments, but their controller is also required to be specific for their extra degrees of freedom. Inspired by the morphology of snakes, we propose a path planning algorithm named Swinging Search and Crawling Control, which allows the snake-like redundant manipulators to explore in complex pipeline environments without collision. The proposed algorithm consists of the Swinging Search and the Crawling Control. In Swinging Search, a collision-free manipulator configuration that of the end-effector in the target point is found by applying reinforcement learning to self-motion, instead of designing joint motion. The self-motion narrows the search space to the null space, and the reinforcement learning makes the algorithm use the information of the environment, instead of blindly searching. Then in Crawling Control, the manipulator is controlled to crawl to the target point like a snake along the collision-free configuration. It only needs to search for a collision-free configuration for the manipulator, instead of searching collision-free configurations throughout the process of path planning. Simulation experiments show that the algorithm can complete path planning tasks of hyper-redundant manipulators in complex environments. The 16 degrees of freedom and 24 degrees of freedom manipulators can achieve 83.3% and 96.7% success rates in the pipe, respectively. In the concentric pipe, the 24 degrees of freedom manipulator has a success rate of 96.1%.

show abstract

A Framework for Unknown Environment Manipulator Motion Planning via Model Based Realtime Rehearsal

Cited by 4 publications

References 7 publications

SPAM for a Manipulator by Best Next Move in Unknown Environments

SPAM for a Manipulator by Best Next Move in Unknown Environments

Enhancing Robots Navigation in Internet of Things Indoor Systems

A snake-inspired path planning algorithm based on reinforcement learning and self-motion for hyper-redundant manipulators

Contact Info

Product

Resources

About