Matrix completion as a post-processing technique for probabilistic roadmaps

Esposito, Joel M.; Wright, John

doi:10.1177/0278364919830554

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…Equation is derived in Janson et al (). Much effort has been focused on developing variants of PRMs that address the computational bottleneck of edge construction (Dobson & Bekris, ; Esposito & Wright, ; Salzman, Shaharabani, Agarwal, & Halperin, ). Additional improvements include heuristic techniques that lazily check obstacle collisions along promising candidate paths as described for PRM in Bohlin and Kavraki () and for PRM* in Hauser ().…”

Section: Related Workmentioning

confidence: 99%

Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover

Reid

Fitch

Göktoğan

et al. 2019

Journal of Field Robotics

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Reconfigurable mobile planetary rovers are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. Planning paths for these adaptive robots is challenging due to their many degrees of freedom, and the need to consider potentially continuous platform reconfiguration along the length of the path. We propose a novel hierarchical structure for asymptotically optimal (AO) sampling‐based planners and specifically apply it to the state‐of‐the‐art Fast Marching Tree (FMT*) AO planner. Our algorithm assumes a decomposition of the full configuration space into multiple subspaces, and begins by rapidly finding a set of paths through one such subspace. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full configuration space. This technique provides a novel way to incorporate prior knowledge of subspaces to efficiently bias search within existing AO sampling‐based planners. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results in simulation are provided that benchmark the algorithm against state‐of‐the‐art sampling‐based planners without the hierarchical variation. Additional experimental results performed with a physical wheel‐on‐leg platform demonstrate application to planetary rover mobility and showcase how constraints such as actuator failures and sensor pointing may be easily incorporated into the planning problem. In minimizing an energy objective that combines an approximation of the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform's mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes, without the need for predefined configurations.

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Section: Related Workmentioning

confidence: 99%

Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover

Reid

Fitch

Göktoğan

et al. 2019

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…The CD was analyzed in three parts: regular (RCD) [4][5][6], approximate (ACD) [7,8], and exact decomposition (ECD) [9,10]. RRT (rapidly exploring random tree) [11] and PRM (probabilistic roadmap method) [12,13] algorithms were studied under the heading of SBM. Dijkstra algorithm [14,15] and A* algorithm [16] are in the GSA group.…”

Section: Introductionmentioning

confidence: 99%

Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot

Top,

Gökbulut

2024

Turkish Journal of Science and Technology

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This paper uses a cascade-connected fuzzy-PI controller to control the position and speed of a differential drive and four-wheel drive of an autonomous mobile robot for optimal path planning. The angular speed information obtained from the encoder of each motor and the instantaneous position and angle information of the robot were calculated. The angle and position error between the reference points and these values is applied to the fuzzy logic controller as an input signal. The robot angular and linear speed data obtained from the fuzzy logic output were converted into reference speed values with kinematic equations to be applied to the motors. The speed controls of the motors were carried out with a PI controller based on these reference values. The study was performed both as a simulation in the MATLAB program and experimentally in the laboratory environment for one and more reference coordinates. In the experimental study, reference values were sent to the robot via Bluetooth with the Android application designed. At the same time, the instant data of the robot was also collected on the Android device through the same application. These data collected in Excel format were transferred to the computer via e-mail and the graphics were drawn in the MATLAB program. When the results were examined, it was seen that both speed and position control were successfully implemented with the fuzzy-PI controller for optimum path planning of the robot.

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“…Kurniawait et al [37] designed an improved PRM algorithm, which was based on obstacle boundary sampling and evaluated the optimal feasible region to optimize the dispersion of random sampling of the PRM algorithm. Esposito et al [38] proposed a processing algorithm for optimizing probabilistic roadmaps. Dealing with the format of convex cells in free space with a number of nodes that requires a lot of computation, this algorithm could simplify the computation required for this step by sparse decomposition.…”

Section: Introductionmentioning

confidence: 99%

Smart Vehicle Path Planning Based on Modified PRM Algorithm

et al. 2022

Sensors

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Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length.

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Matrix completion as a post-processing technique for probabilistic roadmaps

Cited by 7 publications

References 41 publications

Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover

Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover

Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot

Smart Vehicle Path Planning Based on Modified PRM Algorithm

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