Planning using a Network of Reusable Paths: A Physical Embodiment of a Rapidly Exploring Random Tree

Stenning, Braden; McManus, Colin; Barfoot, Timothy D.

doi:10.1002/rob.21474

Cited by 16 publications

(16 citation statements)

References 66 publications

(81 reference statements)

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“…Since GPS was not available, the improvement due to the LB‐NMPC algorithm was quantified by the localization of the VT&R algorithm. The VT&R algorithm is based on visual odometry and provides localization with errors less than 4 cm/m when compared against GPS ground‐truth (Stenning, McManus, & Barfoot, ).…”

Section: Field Testingmentioning

confidence: 99%

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Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

et al. 2015

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This paper presents a Learning‐based Nonlinear Model Predictive Control (LB‐NMPC) algorithm to achieve high‐performance path tracking in challenging off‐road terrain through learning. The LB‐NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modeled as a Gaussian process (GP) as a function of system state, input, and other relevant variables. The GP is updated based on experience collected during previous trials. Localization for the controller is provided by an onboard, vision‐based mapping and navigation system enabling operation in large‐scale, GPS‐denied environments. The paper presents experimental results including over 3 km of travel by three significantly different robot platforms with masses ranging from 50 to 600 kg and at speeds ranging from 0.35 to 1.2 m/s (associated video at http://tiny.cc/RoverLearnsDisturbances). Planned speeds are generated by a novel experience‐based speed scheduler that balances overall travel time, path‐tracking errors, and localization reliability. The results show that the controller can start from a generic a priori vehicle model and subsequently learn to reduce vehicle‐ and trajectory‐specific path‐tracking errors based on experience.

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Section: Field Testingmentioning

confidence: 99%

“…Localization for the controller is provided by an onboard, Visual Teach & Repeat (VT&R) mapping and navigation algorithm enabling operation in large‐scale, GPS‐denied environments (Furgale and Barfoot, ; Stenning, McManus, & Barfoot, ). In the first operational phase, namely the teach phase, the robot is piloted along the desired path.…”

Section: Introductionmentioning

confidence: 99%

Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

et al. 2015

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“…Since GPS was not available, the improvement due to the LB-NMPC algorithm was quantified by the localization of the VT&R algorithm. The VT&R algorithm is based on Visual Odometry and provides localization with errors less than 4 cm/m when compared against GPS ground-truth (Stenning et al, 2013).…”

Section: -M-long Pathmentioning

confidence: 99%

“…Localization for the controller is provided by an on-board, Visual Teach & Repeat (VT&R) mapping and navigation algorithm enabling operation in large-scale, GPS-denied environments (Furgale and Barfoot, 2010;Stenning et al, 2013). In the first operational phase, the teach phase, the robot is piloted along the desired path.…”

Section: Introductionmentioning

confidence: 99%

Learning-based nonlinear model predictive control to improve vision-based mobile robot path-tracking in challenging outdoor environments

Ostafew

Schoellig

Barfoot

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm to achieve high-performance path tracking in challenging off-road terrain through learning. The LB-NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modelled as a Gaussian Process (GP) as a function of system state, input, and other relevant variables. The GP is updated based on experience collected during previous trials. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, GPS-denied environments. The paper presents experimental results including over 3 km of travel by three significantly different robot platforms with masses ranging from 50 kg to 600 kg and at speeds ranging from 0.35 m/s to 1.2 m/s. 1 Planned speeds are generated by a novel experience-based speed scheduler that balances overall travel time, path-tracking errors, and localization reliability. The results show that the controller can start from a generic a priori vehicle model and subsequently learn to reduce vehicle-and trajectory-specific path-tracking errors based on experience. 1 Associated video at http://tiny.cc/RoverLearnsDisturbances DMRV ROC6 Clearpath Husky Mass 600 kg 150 kg 50 kg Size (LW) 2 m x 1.5 m 1.5 m x 0.5 m 0.9 m x 0.6 m Steering Ackermann Steering Skid Steering Skid Steering Figure 1: Robots used to demonstrate the effectiveness of the learning controller. Despite significant differences in robot mass, wheel base, kinematics, and actuator designs, the algorithm uses the same nominal model for all three robots and learns disturbances over trials in order to accurately track desired paths.

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“…There are heated discussions on how to solve the second problem, 12 the third problem, handling uncertain factors. 6,13,14 However, each problem is partly solved, and the relationship between system dynamics and environmental information is not well constructed. Our approach is inspired by artificial potential filed, which introduces bidirectional potential field (BPF) for exploration.…”

Section: Introductionmentioning

confidence: 99%

Gart

Yang

Xiao

et al. 2016

International Journal of Advanced Robotic Systems

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The problem of three-dimensional path planning in obstacle-crowded environments is a challenge (an NP-hard problem), which becomes even more complex when considering environmental uncertainty and system control. Int this paper, we mainly focused on more challenging problem, that is, path planning in obstacle-crowded environments, and we try to find the relation between contact information and obstacle modeling. We proposed a newactive exploring sampling-based algorithm based on rapidly exploring random tree (RRT), namely, guiding attraction-based random tree (GART). GART introduces bidirectional potential field to redistribute each newly sampled state, such that the in-collision samples can be redistributed for extension. Furthermore, dynamic constraints are deployed to establish forward extending region by GART. Thus, GART can ensure kinodynamic reachability as well as smoothness. Theoretical analysis demonstrate that GART is probabilistic complete, and it obtains faster convergence rate because of its redistribution ability. In addition to theoretical analysis, this article provides comparative simulations as well as experiments under typical situations. Results demonstrate that GART has a much better time-efficiency performance than RRT*, retraction-based RRT, and other referred algorithms when applying redistribution and dynamic constraints on random exploration. KeywordsSampling-based planning algorithm, bidirectional potential field, control and sensing uncertainty, percentage of useful samples Date

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Planning using a Network of Reusable Paths: A Physical Embodiment of a Rapidly Exploring Random Tree

Cited by 16 publications

References 66 publications

Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

Learning-based nonlinear model predictive control to improve vision-based mobile robot path-tracking in challenging outdoor environments

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