Dynamic programming guided exploration for sampling-based motion planning algorithms

Arslan, Oktay; Tsiotras, Panagiotis

doi:10.1109/icra.2015.7139869

Cited by 27 publications

(12 citation statements)

References 13 publications

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“…RRT # (Arslan and Tsiotras, 2013, 2015) uses heuristics to find and update a tree in the graph built incrementally by Rapidly exploring Random Graph (RRG; Karaman and Frazzoli, 2011). It efficiently maintains the optimal connection to each vertex by using LPA * to propagate changes through the entire graph.…”

Section: Prior Work Ordering Sampling-based Plannersmentioning

confidence: 99%

“…Search outside this informed set is provably unnecessary for the solution found by each planner and illustrates the inefficiency of the initial search of RRT * , RRT # , and FMT * . Note that RRT # finds an initial solution from the same samples as RRT * as the heuristics presented in Arslan and Tsiotras (2015) do not alter the search until a solution is found. Also note that by ordering its search on potential solution quality, BIT * does not consider any samples that cannot provide the best solution in its current approximation (i.e., batch of samples).…”

Section: Introductionmentioning

confidence: 99%

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Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Gammell

Barfoot

Srinivasa

2020

The International Journal of Robotics Research

118

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Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as used by informed graph-based searches and anytime sampling-based planners, respectively. Informed graph-based searches, such as A*, traditionally use heuristics to search a priori graphs in order of potential solution quality. This makes their search efficient, but leaves their performance dependent on the chosen approximation. If the resolution of the chosen approximation is too low, then they may not find a (suitable) solution, but if it is too high, then they may take a prohibitively long time to do so. Anytime sampling-based planners, such as RRT*, traditionally use random sampling to approximate the problem domain incrementally. This allows them to increase resolution until a suitable solution is found, but makes their search dependent on the order of approximation. Arbitrary sequences of random samples approximate the problem domain in every direction simultaneously, but may be prohibitively inefficient at containing a solution. This article unifies and extends these two approaches to develop Batch Informed Trees (BIT*), an informed, anytime sampling-based planner. BIT* solves continuous path planning problems efficiently by using sampling and heuristics to alternately approximate and search the problem domain. Its search is ordered by potential solution quality, as in A*, and its approximation improves indefinitely with additional computational time, as in RRT*. It is shown analytically to be almost-surely asymptotically optimal and experimentally to outperform existing sampling-based planners, especially on high-dimensional planning problems.

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Section: Prior Work Ordering Sampling-based Plannersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Gammell

Barfoot

Srinivasa

2020

The International Journal of Robotics Research

118

View full text Add to dashboard Cite

show abstract

“…2 In recent years, a great amount of sampling-based pathplanning algorithms have been proposed. [5][6][7][8][9][10][11] These works have in common that they outperform the RRT* algorithm by modifying and optimizing some of the subroutines that compose the original RRT* algorithm. However, the cited algorithms are specifically designed to solve the optimal shortest path-planning problem under certain restrictions.…”

Section: Related Workmentioning

confidence: 99%

Planning with ants

Viseras

Losada

Merino

2016

International Journal of Advanced Robotic Systems

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Rapidly exploring random trees (RRTs) have been proven to be efficient for planning in environments populated with obstacles. These methods perform a uniform sampling of the state space, which is needed to guarantee the algorithm's completeness but does not necessarily lead to the most efficient solution. In previous works it has been shown that the use of heuristics to modify the sampling strategy could incur an improvement in the algorithm performance. However, these heuristics only apply to solve the shortest path-planning problem. Here we propose a framework that allows us to incorporate arbitrary heuristics to modify the sampling strategy according to the user requirements. This framework is based on 'learning from experience'. Specifically, we introduce a utility function that takes the contribution of the samples to the tree construction into account; sampling at locations of increased utility then becomes more frequent. The idea is realized by introducing an ant colony optimization concept in the RRT/RRT* algorithm and defining a novel utility function that permits trading off exploitation versus exploration of the state space. We also extend the algorithm to allow an anytime implementation. The scheme is validated with three scenarios: one populated with multiple rectangular obstacles, one consisting of a single narrow passage and a maze-like environment. We evaluate its performance in terms of the cost and time to find the first path, and in terms of the evolution of the path quality with the number of iterations. It is shown that the proposed algorithm greatly outperforms state-of-the-art RRT and RRT* algorithms.

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“…The scheduling method is improved to generate energy optimal trajectories for six-link manipulators using dynamic time scaling (Wigström et al , 2013). Dynamic programming can be applied to an existing trajectory and generate a new energy optimal trajectory that follows the same path but in a different execution time frame (Arslan and Tsiotras, 2015). To achieve low-carbon processing, energy consumption topological graph is constructed in accordance with NC code and process parameters (Wang, 2017).…”

Section: Introductionmentioning

confidence: 99%

A survey of energy-efficient motion planning for wheeled mobile robots

Zhang

Yang

2020

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Purpose As wheeled mobile robots find increasing use in outdoor applications, it becomes more important to reduce energy consumption to perform more missions efficiently with limit energy supply. The purpose of this paper is to survey the current state-of-the-art on energy-efficient motion planning (EEMP) for wheeled mobile robots. Design/methodology/approach The use of wheeled mobile robots has been increased to replace humans in performing risky missions in outdoor applications, and the requirement of motion planning with efficient energy consumption is necessary. This study analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present. The dynamic constraints play a key role in EEMP problem, which derive the power model related to energy consumption. The surveyed approaches differ in the used steering mechanisms for wheeled mobile robots, in assumptions on the structure of the environment and in computational requirements. The comparison among different EEMP methods is proposed in optimal, computation time and completeness. Findings According to lots of literature in EEMP problem, the research results can be roughly divided into online real-time optimization and offline optimization. The energy consumption is considered during online real-time optimization, which is computationally expensive and time-consuming. The energy consumption model is used to evaluate the candidate motions offline and to obtain the optimal energy consumption motion. Sometimes, this optimization method may cause local minimal problem and even fail to track. Therefore, integrating the energy consumption model into the online motion planning will be the research trend of EEMP problem, and more comprehensive approach to EEMP problem is presented. Research limitations/implications EEMP is closely related to robot’s dynamic constraints. This paper mainly surveyed in EEMP problem for differential steered, Ackermann-steered, skid-steered and omni-directional steered robots. Other steering mechanisms of wheeled mobile robots are not discussed in this study. Practical implications The survey of performance of various EEMP serves as a reference for robots with different steering mechanisms using in special scenarios. Originality/value This paper analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present.

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Dynamic programming guided exploration for sampling-based motion planning algorithms

Cited by 27 publications

References 13 publications

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Planning with ants

A survey of energy-efficient motion planning for wheeled mobile robots

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