An efficient RRT cache method in dynamic environments for path planning

Yuan, Chengren; Liu, Guifeng; Zhang, Wenqun; Pan, Xin

doi:10.1016/j.robot.2020.103595

Cited by 60 publications

(20 citation statements)

References 18 publications

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“…First, 4-D C-Space is obtained and then fast/medium/optimum path planning can be made using the sequences mentioned in Section 3.1. After obtaining the C-Space, the rest is related to path planning algorithms which can be A* [34,35], many variants of rapidly exploring random tree (RRT) [36], probabilistic roadmap (PRM) [37], particle swarm optimization (PSO) [38], and so forth. Apart from these, some papers published recently have also provided guidance [39,40,41,42,43].…”

Section: Rct Path Planning Approachmentioning

confidence: 99%

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Yerlikaya

Balkan

2021

Robotica

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Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped by providing three converging options named as fast, medium, and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using six sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options.

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Section: Rct Path Planning Approachmentioning

confidence: 99%

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Yerlikaya

Balkan

2021

Robotica

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“…By comparison, the Rapidly Exploring Random Trees (RRT) algorithm is a feasible, relatively fast, and compatible solution in a searching space which avoids colliding with obstacles [21]. It has mainly been applied to robot maneuvering [22][23][24][25][26] and modified to adapt to trajectory curvature constraint [27], collision detection [28], quicker planning by the triangular inequality method [29], and to consider the bias goal factor [30]; however, the above were all carried out in numerical simulation only. Recent advances in the RRT algorithm have been extending to path planning of autonomous vehicles by numerical simulation [31,32].…”

Section: Introductionmentioning

confidence: 99%

Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Yang

Lin

2021

Sensors

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Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

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“…As a fundamental problem, path planning has been widely studied in many fields, such as robots, robotic arms, games, and UAVs. Path planning methods are mainly classified in terms of heuristic-based [7], learning-based [8][9][10], sampling-based [11], geometrybased [12], etc. Note that solving mission planning problems usually requires evaluating many solutions to obtain an optimized mission plan.…”

Section: Introductionmentioning

confidence: 99%

“…Constraint (10) guarantees that the travel distance of the UAV does not exceed its maximum travel distance. Constraint (11) and (12) ensure that all UAVs begin and end their path at the base. Constraint ( 13) makes sure that the path of the UAVs will not collide with obstacles.…”

Section: Brief Introduction Of the Conventional Variable Neighborhood Search Algorithmmentioning

confidence: 99%

An Integrated Mission Planning Framework for Sensor Allocation and Path Planning of Heterogeneous Multi-UAV Systems

Zheng

Yuan

2021

Sensors

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Mission planning is the guidance for a UAV team to perform missions, which plays the most critical role in military and civil applications. For complex tasks, it requires heterogeneous cooperative multi-UAVs to satisfy several mission requirements. Meanwhile, airborne sensor allocation and path planning are the critical components of heterogeneous multi-UAVs system mission planning problems, which affect the mission profit to a large extent. This paper establishes the mathematical model for the integrated sensor allocation and path planning problem to maximize the total task profit and minimize travel costs, simultaneously. We present an integrated mission planning framework based on a two-level adaptive variable neighborhood search algorithm to address the coupled problem. The first-level is devoted to planning a reasonable airborne sensor allocation plan, and the second-level aims to optimize the path of the heterogeneous multi-UAVs system. To improve the mission planning framework’s efficiency, an adaptive mechanism is presented to guide the search direction intelligently during the iterative process. Simulation results show that the effectiveness of the proposed framework. Compared to the conventional methods, the better performance of planning results is achieved.

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An efficient RRT cache method in dynamic environments for path planning

Cited by 60 publications

References 18 publications

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

An Integrated Mission Planning Framework for Sensor Allocation and Path Planning of Heterogeneous Multi-UAV Systems

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