A review of motion planning algorithms for intelligent robots

Zhou, Chengmin; Huang, Bingding; Fränti, Pasi

doi:10.1007/s10845-021-01867-z

Cited by 104 publications

(54 citation statements)

References 92 publications

(159 reference statements)

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“…In the same way as before, if we find that X G is likely to result from observing a linear feature we keep this new geometry. We repeat this procedure, sampling new points to grow the geometry until we have made a fixed number of growing attempts 8 .…”

Section: Integration With Online Gpismentioning

confidence: 99%

“…Gaussian process implicit surface (GPIS) models [1] have been used in robotics as probabilistic representations for both object shape estimation [2], [3] as well as environmental mapping [4], [5], [6], [7]. Some of the factors motivating the use of GPIS in these applications are the needs for: 1) surface models which interpolate from sparse observations, 2) distance fields which are very accurate close to the surface (especially relevant for manipulation [2], [3]), 3) distance fields which are accurate far from the surface (useful for navigation tasks [8], [9], [10]), and 4) a model which captures the notion that predicted distances should be more uncertain in regions far from observations (critical for methods which use surface uncertainty to guide exploration [6] or plan grasps [3]).…”

Section: Introductionmentioning

confidence: 99%

“…with exact equality in the limit λ → ∞ (recall l is the length scale of the GP and cf. (8), where λ = √ 2l −1 ). More directly, rather than modelling f edf , the GP is a model of f λ (x) = exp(−λf edf (x)).…”

mentioning

confidence: 99%

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Online Distance Field Priors for Gaussian Process Implicit Surfaces

Ivan

Stoyanov

Stork

2022

IEEE Robot. Autom. Lett.

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Gaussian process (GP) implicit surface models provide environment and object representations which elegantly address noise and uncertainty while remaining sufficiently flexible to capture complex geometry. However, GP models quickly become intractable as the size of the observation set grows-a trait which is difficult to reconcile with the rate at which modern range sensors produce data. Furthermore, naïve applications of GPs to implicit surface models allocate model resources uniformly, thus using precious resources to capture simple geometry. In contrast to prior work addressing these challenges though model sparsification, spatial partitioning, or ad-hoc filtering, we propose introducing model bias online through the GP's mean function. We achieve more accurate distance fields using smaller models by creating a distance field prior from features which are easy to extract and have analytic distance fields. In particular, we demonstrate this approach using linear features. We show the proposed distance field halves model size in a 2D mapping task using data from a SICK S300 sensor. When applied to a single 3D scene from the TUM RGB-D SLAM dataset, we achieve a fivefold reduction in model size. Our proposed prior results in more accurate GP implicit surfaces, while allowing existing models to function in larger environments or with larger spatial partitions due to reduced model size.

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Section: Integration With Online Gpismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Distance Field Priors for Gaussian Process Implicit Surfaces

Ivan

Stoyanov

Stork

2022

IEEE Robot. Autom. Lett.

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“…Path planning algorithms have been continuously proposed for enhancing the autonomy of the mobile robot in complicated environments [ 1 ]. Nowadays, research on motion planning is in a period of vigorous development [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Path Planning Approach with Multi-SARSA Based on Topological Map

Wen

Jiang

Cui

et al. 2022

Sensors

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In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the region dynamic growth algorithm based on the grid map. In the next level, the Multi-SARSA method divided into two layers is applied to find a near-optimal global planning path, in which the artificial potential field method, first of all, is used to initialize the first Q table for faster learning speed, and then the second Q table is initialized with the connected domain obtained by topological map, which provides the prior information. A combination of the two algorithms makes the algorithm easier to converge. Simulation experiments for path planning have been executed. The results indicate that the method proposed in this paper can find the optimal path with a shorter path length, which demonstrates the effectiveness of the presented method.

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“…Currently, the frequently utilized path planning algorithms can be classified into two categories: traditional algorithms and intelligent algorithms [8]. Moreover, the traditional algorithm consists of four main groups: graph search (e.g., A* algorithm and Dijkstra's algorithm), sampling based (e.g., rapidly exploring random tree), interpolating curve (e.g., line and circle), and reaction based (e.g., artificial potential field) [9]. Because traditional algorithms suffer from poor optimization and high time complexity, intelligent algorithms are increasingly becoming the mainstream algorithms when dealing with path planning problems under complex environmental information.…”

Section: Introductionmentioning

confidence: 99%

IHSSAO: An Improved Hybrid Salp Swarm Algorithm and Aquila Optimizer for UAV Path Planning in Complex Terrain

et al. 2022

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In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed IHSSAO is to enhance the performance of AO by introducing the leader mechanism of SSA, tent chaotic map, and pinhole imaging opposition-based learning strategy. Firstly, the tent chaotic map is utilized to substitute the randomly generated initial population in the original algorithm to increase the diversity of the initial individuals. Secondly, we integrate the leader mechanism of SSA into the position update formulation of the basic AO, which enables the search individuals to fully utilize the optimal solution information and enhances the global search capability of AO. Thirdly, we introduce the pinhole imaging opposition-based learning in the proposed IHSSAO to enhance the capability to escape from the local optimization. To verify the effectiveness of the proposed IHSSAO algorithm, we tested it against SSA, AO, and five other advanced meta-heuristic algorithms on 23 classical benchmark functions and 17 IEEE CEC2017 test functions. The experimental results indicate that the proposed IHSSAO is superior to the other seven algorithms in most cases. Eventually, we applied the IHSSAO, SSA, and AO to solve the UAV path planning problem. The experimental results verify that the IHSSAO is superior to the basic SSA and AO for solving the UAV path planning problem in complex terrain.

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A review of motion planning algorithms for intelligent robots

Cited by 104 publications

References 92 publications

Online Distance Field Priors for Gaussian Process Implicit Surfaces

Online Distance Field Priors for Gaussian Process Implicit Surfaces

A Hierarchical Path Planning Approach with Multi-SARSA Based on Topological Map

IHSSAO: An Improved Hybrid Salp Swarm Algorithm and Aquila Optimizer for UAV Path Planning in Complex Terrain

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