Sampling-based algorithms for optimal motion planning using closed-loop prediction

Arslan, Oktay; Berntorp, Karl; Tsiotras, Panagiotis

doi:10.1109/icra.2017.7989581

Cited by 56 publications

(20 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future works will extend the current algorithm to solve a three-dimensional motion planning problem. The proposed MP-RRT # strategy will be adapted for real-time motion planning problems like the one described in [1,20]. In addition to that, experimental tests will be conducted on a physical robotic platform to evaluate the performance under realistic conditions.…”

Section: Discussionmentioning

confidence: 99%

“…The sampled reference is then used to compute a trajectory using the closed-loop model of the robot. A similar approach is also used in [1] with the RRT # algorithm. Similarly, in [16] a kinodynamic RRT * is realized using Dubins curves as a primitive curve defining the reference path between the new sample and the existing tree, whereas an LQR controller is used in [33] to compute the cost of tracking the reference path.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

Primatesta

Osman

Rizzo

2021

J Intell Robot Syst

View full text Add to dashboard Cite

This paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT#. MP-RRT# joins the potentialities of RRT# with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT# explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT# algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT# approach.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

Primatesta

Osman

Rizzo

2021

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…The algorithm was further extended to combine the closed-loop control by [15]. Recently, the vehicle model prediction was also integrated into the algorithm [1]. Sampling-based path planning methods can find a path in high dimensional space efficiently.…”

Section: A Search-based and Sampling-based Path Planningmentioning

confidence: 99%

Real-time Multi-target Path Prediction and Planning for Autonomous Driving aided by FCN

Zhou,

Yang,

Zhang

et al. 2019

Preprint

View full text Add to dashboard Cite

Real-time multi-target path planning is a key issue in the field of autonomous driving. Although multiple paths can be generated in real-time with polynomial curves, the generated paths are not flexible enough to deal with complex road scenes such as S-shaped road and unstructured scenes such as parking lots. Search and sampling-based methods, such as A* and RRT and their derived methods, are flexible in generating paths for these complex road environments. However, the existing algorithms require significant time to plan to multiple targets, which greatly limits their application in autonomous driving. In this paper, a real-time path planning method for multi-targets is proposed. We train a fully convolutional neural network (FCN) to predict a path region for the target at first. By taking the predicted path region as soft constraints, the A* algorithm is then applied to search the exact path to the target. Experiments show that FCN can make multiple predictions in a very short time (50 times in 40ms), and the predicted path region effectively restrict the searching space for the following A* search. Therefore, the A* can search much faster so that the multi-target path planning can be achieved in real-time (3 targets in less than 100ms).

show abstract

“…It is asymptotically complete, i.e., as the number of samples goes to infinity, the probability of finding a solution if one exists approaches 1. RRT* is an extension of RRT that finds the shortest feasible path and has been widely used recently, e.g., by [21], [22], where the latter used RRT* in an off-road scenario. Another samplingbased approach to terrain driving was presented in [23].…”

Section: Introductionmentioning

confidence: 99%

Path Planning for UGVs Based on Traversability Hybrid A*

Thoresen

Nielsen

Mathiassen

et al. 2021

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

In this paper, a new method of path planning for unmanned ground vehicles (UGVs) on terrain is developed. For UGVs moving on terrain, path traversability and collision avoidance are important factors. If traversability is not considered, the planned path may lead a UGV into areas that will cause rough vehicle motion or lead to the UGV getting stuck if the traversability is low. The proposed path planning method is based on the Hybrid A* algorithm and uses estimated terrain traversability to find the path that optimizes both traversability and distance for the UGV.The path planning method is demonstrated using simulated traversability maps and is compared to the original Hybrid A* algorithm. The method is also verified through real-time experiments in real terrain, further demonstrating the benefits of terrain traversability optimization using the proposed path planning method. In the experiments, the proposed method was successfully applied for autonomous driving over distances of up to 270 m in rough terrain. Compared with the existing Hybrid A* method, the proposed method produces more traversable paths.

show abstract

Sampling-based algorithms for optimal motion planning using closed-loop prediction

Cited by 56 publications

References 18 publications

MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

Real-time Multi-target Path Prediction and Planning for Autonomous Driving aided by FCN

Path Planning for UGVs Based on Traversability Hybrid A*

Contact Info

Product

Resources

About