C-FOREST: Parallel Shortest Path Planning With Superlinear Speedup

Otte, Michael W.; Correll, Nikolaus

doi:10.1109/tro.2013.2240176

Cited by 79 publications

(45 citation statements)

References 19 publications

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“…Also, the use of parallelized planning algorithms such as C-Forest [40] seems convenient in order to take advantage of modern multi-core processors. To conclude, path pruning techniques such as the proposed in [41] can be used in a post-processing step to the paths obtained with RRT* method in order to get rid of unnecessary waypoints.…”

Section: Discussionmentioning

confidence: 99%

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Alejo

Cobano

Heredia

et al. 2015

Cooperative Robots and Sensor Networks 2015

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This chapter discusses the problem of trajectory planning for WSN (Wireless Sensor Network) data retrieving deployed in remote areas with a cooperative system of UAVs (Unmanned Aerial Vehicles). Three different path planners are presented in order to autonomously guide the UAVs during the mission. The missions are given by a set of waypoints which define WSN collection zones and each UAV should pass through them to collect the data while avoiding passing over forbidden areas and collisions between UAVs. The proposed UAV trajectory planners are based on Genetics Algorithm (GA), RRT (Rapidly-exploring Random Trees) and RRT* (Optimal Rapidly-exploring Random Trees). Simulations and experiments have been carried out in the airfield of Utrera (Seville, Spain). These results are compared in order to measure the performance of the proposed planners.

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Section: Discussionmentioning

confidence: 99%

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Alejo

Cobano

Heredia

et al. 2015

Cooperative Robots and Sensor Networks 2015

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“…Otte and Correll [39] use a parallel hybrid method in their Coupled Forest of Random Engrafting Search Trees (C-FOREST) algorithm. Demonstrated with RRT*, the algorithm shares information between multiple sampling-based planners using heuristics and rejection sampling.…”

Section: Hybridizationmentioning

confidence: 99%

Regionally accelerated batch informed trees (RABIT*): A framework to integrate local information into optimal path planning

Choudhury

Gammell

Barfoot

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Sampling-based optimal planners, such as RRT*, almost-surely converge asymptotically to the optimal solution, but have provably slow convergence rates in high dimensions. This is because their commitment to finding the global optimum compels them to prioritize exploration of the entire problem domain even as its size grows exponentially. Optimization techniques, such as CHOMP, have fast convergence on these problems but only to local optima. This is because they are exploitative, prioritizing the immediate improvement of a path even though this may not find the global optimum of nonconvex cost functions.In this paper, we present a hybrid technique that integrates the benefits of both methods into a single search. A key insight is that applying local optimization to a subset of edges likely to improve the solution avoids the prohibitive cost of optimizing every edge in a global search. This is made possible by Batch Informed Trees (BIT*), an informed global technique that orders its search by potential solution quality. In our algorithm, Regionally Accelerated BIT* (RABIT*), we extend BIT* by using optimization to exploit local domain information and find alternative connections for edges in collision and accelerate the search. This improves search performance in problems with difficult-to-sample homotopy classes (e.g., narrow passages) while maintaining almost-sure asymptotic convergence to the global optimum.Our experiments on simulated random worlds and real data from an autonomous helicopter show that on certain difficult problems, RABIT* converges 1.8 times faster than BIT*. Qualitatively, in problems with difficult-to-sample homotopy classes, we show that RABIT* is able to efficiently transform paths to avoid obstacles.

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“…One source of speedup has been parallelism, including the use of multiple cores or processors (e.g., [5], [7], [10], [15], [28], [35]) and GPUs (e.g., [3], [29]) to achieve speedups for single-query sampling-based motion planners (e.g., RRT and RRT*). Wedge and Branicky showed that periodically restarting sampling-based tree construction can improve the mean and the variability of the runtime of RRT [47].…”

Section: Related Workmentioning

confidence: 99%

High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning

2015

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As sampling-based motion planners become faster, they can be re-executed more frequently by a robot during task execution to react to uncertainty in robot motion, obstacle motion, sensing noise, and uncertainty in the robot’s kinematic model. We investigate and analyze high-frequency replanning (HFR), where, during each period, fast sampling-based motion planners are executed in parallel as the robot simultaneously executes the first action of the best motion plan from the previous period. We consider discrete-time systems with stochastic nonlinear (but linearizable) dynamics and observation models with noise drawn from zero mean Gaussian distributions. The objective is to maximize the probability of success (i.e., avoid collision with obstacles and reach the goal) or to minimize path length subject to a lower bound on the probability of success. We show that, as parallel computation power increases, HFR offers asymptotic optimality for these objectives during each period for goal-oriented problems. We then demonstrate the effectiveness of HFR for holonomic and nonholonomic robots including car-like vehicles and steerable medical needles.

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C-FOREST: Parallel Shortest Path Planning With Superlinear Speedup

Cited by 79 publications

References 19 publications

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Regionally accelerated batch informed trees (RABIT*): A framework to integrate local information into optimal path planning

High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning

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