Efficient and High Path Quality Autonomous Exploration and Trajectory Planning of UAV in an Unknown Environment
Abstract
:1. Introduction
- It combines frontier-based exploration with sampling-based exploration. Via implicit voxel grouping in the octree graph representation, frontier voxels can be regarded as clusters, thereby avoiding the computationally expensive steps of frontier voxel clustering in original frontier-based methods.
- Original RRT algorithm can easily fall into a local minimal area. This paper introduced the dynamic step size and adaptive weight in UAV path planning system based on the rapid exploration tree. The purpose of planning the optimal trajectory in the task environment based on dynamic step size and adaptive weights, so as to improve search efficiency, increasing success rate and improving quality of generated paths.
- To avoid irrationality of the planned path, UAV dynamic constraints are introduced in the planning process to avoid the situation where the climbing angle and the turning angle are large. Hermite difference polynomial is used for smooth out the twisted sections of the original path.
2. Related Work
2.1. Autonomous Exploration
2.2. Path Planning
3. System Overview
4. Exploration
4.1. Map Representation
4.2. Frontier Detection
4.3. Candidate Position Sampling
5. Path Planning
5.1. Advanced RRT
5.1.1. Improvement in the Selection of the Growth Points
5.1.2. Dynamic Step Adjustment Strategy
5.2. Improved RRT Algorithm Structure
- Initialize the exploration tree T, the exploration step ds, the maximum turning angle θ, and the turning angle α.;
- Find the random exploration direction point tdir (tgoal and trandom are the task target point and a random point in the space, respectively, and P is a random number between 0 and 1).tdir = p∗tgoal + (1 − p) ∗ trand, (0 < p < 1)
- Calculate the exploration step length d:
- Select the growth point of the tree tgrow.ωi = Ii/ditgrow = argmax(ωi), ti ∈ T
- Find the new node of the tree tnew
- Determine whether tnew is a node that has not been explored. If yes, calculate the turning angle , tj = tgrow, fj + 1, and skip to (2); otherwise, tnew is added to the exploration tree, tj = NULL, and fj = 1.
- Determine whether the target point is reached. If ‖tgoal − tnew‖ < s, the goal is reached. Otherwise, return to Equation (2). Here, s is the shortest flight distance of the UAV.
- Backtrack from the target point to the root node of the exploration tree and return to the planned path.
Algorithm 1 Advanced RRT |
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5.3. Trajectory Optimization
6. Experimental Results
6.1. Simulation Setup
6.2. Apartment and Maze Environment Exploration Simulation Results
6.2.1. Apartment Environment Simulation
6.2.2. Maze Environment Simulation
6.3. Apartment and Maze Environment Path Planning Simulation Results
7. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Autonomous Exploration Algorithm | Advantage | Difficulties |
---|---|---|
Frontier-based exploration [13] | Perform well in 2-dimension environment | Redundant frontiers generating due to environmental occlusion and noise |
VFH + plus bug algorithm [14] | Perform well in sparse environments | Require the planner knows the location of the goal and assume the robot has perfect positioning |
NBV [15] | High exploration coverage | Random sampling not always detect unexplored areas fast |
Bircher’s NBV [16] | Minimize the uncertainty of robot positioning and tracking marks | High computational complexity and long exploration time. |
Directly sample candidate NBV [17] | Proposed the term of safe region, higher in exploration coverage | High precision of relative positioning is required |
Path Planning Algorithm | Advantage | Difficulties |
---|---|---|
A Star [23] | Informed search algorithm high; efficiency in heuristic planning; | Optimal search path cannot be guaranteed in multiple minimum values |
D Star [24] | Incremental search algorithm; rapid planning in re-planning | Consumes a lot of search and calculation time |
PRM [26] | Track planning using random road map method; easy to find the optimal trajectory | Cannot be applied to real-time planning |
RRT [27] | Effectively solve high-dimensional space and complex constraints | Random search is not sensitive to complex environment |
Informed RRT Star [31] | Less dependence on the dimension and domain | Cannot focus the search when associated prolate hyperspheroid is large |
RRT-Connect [32] | High Search speed and search efficiency | Expensive in high dimension; difficult to find a path in a narrow environment |
Closed-loop RRT [33] | Well performance in dynamic unstable environment | Not convenient to address the situation of a complex terrain |
Algothrim | Frontier-Based Exploration [13] | Receding Horizon NBV [18] | Bug Algorithm for Wall-Following [14] | Proposed Method |
---|---|---|---|---|
Flight time (s) | 15.29 | 16.78 | 21.52 | 14.37 |
Coverage (&) | 93.73 | 98.96 | 89.44 | 99.27 |
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Zhao, L.; Yan, L.; Hu, X.; Yuan, J.; Liu, Z. Efficient and High Path Quality Autonomous Exploration and Trajectory Planning of UAV in an Unknown Environment. ISPRS Int. J. Geo-Inf. 2021, 10, 631. https://doi.org/10.3390/ijgi10100631
Zhao L, Yan L, Hu X, Yuan J, Liu Z. Efficient and High Path Quality Autonomous Exploration and Trajectory Planning of UAV in an Unknown Environment. ISPRS International Journal of Geo-Information. 2021; 10(10):631. https://doi.org/10.3390/ijgi10100631
Chicago/Turabian StyleZhao, Leyang, Li Yan, Xiao Hu, Jinbiao Yuan, and Zhenbao Liu. 2021. "Efficient and High Path Quality Autonomous Exploration and Trajectory Planning of UAV in an Unknown Environment" ISPRS International Journal of Geo-Information 10, no. 10: 631. https://doi.org/10.3390/ijgi10100631