Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environment

Chen, Ying; He, Zhen; Li, Shunli

doi:10.1007/s10514-019-09879-8

Cited by 14 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Asymptotically optimal techniques to replan efficiently when new information becomes available include using a bidirectional search to facilitate updates (82) and continuously refining and repairing a search during execution (144,148,149). Work has also investigated achievable notions of optimality specific to incrementally revealed environments that result in policies guaranteed to be collision free (150).…”

Section: Unknown And/or Dynamic Environmentsmentioning

confidence: 99%

Asymptotically Optimal Sampling-Based Motion Planning Methods

Gammell,

Strub

2020

Preprint

View full text Add to dashboard Cite

Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge towards the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.

show abstract

Section: Unknown And/or Dynamic Environmentsmentioning

confidence: 99%

Asymptotically Optimal Sampling-Based Motion Planning Methods

Gammell,

Strub

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Some works are based on RRT-like planners [7] and prune and modify the search tree when changes of the configuration space happen [8], [9], [10], [11]. Time dimension can be added to the search tree to plan a new path foreseeing possible future collisions with mobile obstacles [12], [13], [14]. Another typical approach consists of modifying the robot's trajectory applying virtual repulsive forces to the robot to move it away from the obstacles [15].…”

Section: Introductionmentioning

confidence: 99%

“…All these techniques prune trees when changes of the configuration space happen. However, when the environment is complex, a larger effort is required to prune the graph rather than replan a new one [18]. In some approaches, time dimension is added to the tree to plan a new path foreseeing possible future collisions with mobile obstacle [19].…”

mentioning

confidence: 99%

“…In some approaches, time dimension is added to the tree to plan a new path foreseeing possible future collisions with mobile obstacle [19]. Obstacles as time-space volumes can be used to check vertices and edges inside a defined time horizon, postponing the check of those outside it [18], [20]. Another type of strategy involves the use of potential fields in the configuration space.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Anytime informed path re-planning and optimization for human-robot collaboration

Tonola

Faroni

Pedrocchi

et al. 2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

View full text Add to dashboard Cite

Robots working in proximity of humans often need to change their motion to avoid collisions and interference with the operators. This paper uses a path re-planning approach to change the robot path online when the human operator is in the robot way. The method exploits a set of pre-computed paths to compute a new feasible path in case of obstruction to enhance the trajectory's readability. Moreover, the algorithm iteratively optimizes the current solution in an anytime fashion to deal with strict computing time requirements. Experimental results show the method's effectiveness in a collaborative cell, compared with industry best practices.

show abstract

“…It maintains a well-defined and connected structure over the entire explored region; however, re-optimizing the connections in the entire graph when the environment changes, makes it inefficient in highly dynamic settings. Chen et al [14] presented the Horizon-based Lazy RRT (HL-RRT ) algorithm. In this method, when parts of the search tree are made invalid due to moving obstacles, they are pruned and new samples are drawn to find a new path via a trained Gaussian mixture model.…”

Section: Introductionmentioning

confidence: 99%

SMARRT: Self-Repairing Motion-Reactive Anytime RRT for Dynamic Environments

Shen¹,

Wilson²,

Harvey³

et al. 2021

Preprint

View full text Add to dashboard Cite

This paper addresses the fast replanning problem in dynamic environments with moving obstacles. Since for randomly moving obstacles the future states are unpredictable, the proposed method, called SMARRT, reacts to obstacle motions and revises the path in real-time based on the current interfering obstacle state (i.e., position and velocity). SMARRT is fast and efficient and performs collision checking only on the partial path segment close to the robot within a feasibility checking horizon. If the path is infeasible, then tree parts associated with the path inside the horizon are pruned while maintaining the maximal tree structure of already-explored regions. Then, a multi-resolution utility map is created to capture the environmental information used to compute the replanning utility for each cell on the multi-scale tiling. A hierarchical searching method is applied on the map to find the sampling cell efficiently. Finally, uniform samples are drawn within the sampling cell for fast replanning. The SMARRT method is validated via simulation runs, and the results are evaluated in comparison to four existing methods. The SMARRT method yields significant improvements in travel time, replanning time, and success rate compared against the existing methods.

show abstract

Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environment

Cited by 14 publications

References 39 publications

Asymptotically Optimal Sampling-Based Motion Planning Methods

Asymptotically Optimal Sampling-Based Motion Planning Methods

Anytime informed path re-planning and optimization for human-robot collaboration

SMARRT: Self-Repairing Motion-Reactive Anytime RRT for Dynamic Environments

Contact Info

Product

Resources

About