Gaussian noise and the intersection problem in Human-Robot Systems - analytical and fuzzy approach

Palm, Rainer; Lilienthal, Achim J.

doi:10.1109/fuzz-ieee.2019.8858796

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…and angle γ are assumed to be measurable. If ϕ H is not directly measurable then it can be computed by The coordinates x c and y c of the intersection are computed straight forward by [7] x…”

Section: Geometrical Relationsmentioning

confidence: 99%

“…Figure 8 shows the control surface of x c and y c for the non-fuzzy case (4). Control surfaces of the fuzzy approximations (7) for 30 • and 7.5 • sectors are shown in Figures 9-10. Resolution 30 • (Figure 9) shows a very high deviation compared to the non-fuzzy approach (Figure 8) which decreases further down to resolution 7.5 • (Figure 10).…”

Section: Statistical Linearizationmentioning

confidence: 99%

“…A human-friendly interaction between robots and humans can be obtained by human-like sensor systems [5]. A prominent role in robot navigation is the trajectorycrossing problem of robots and humans [6] and corresponding fuzzy solutions [7]. Motivations for a fuzzy solution of the intersection problem are manifold.…”

Section: Introductionmentioning

confidence: 99%

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Crossing Point Estimation in Human/Robot Navigation - Statistical Linearization versus Sigma-Point-Transformation

Palm,

Lilienthal

2024

Preprint

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Interactions between mobile robots and human operators in common areas require a high safety especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection the crossings of planned trajectories, their uncertainty based on model fluctuations, system noise and sensor noise,play an outstanding role. This paper discusses the calculation of expected areas of interactions du ring human-robot navigation with respect to fuzzy and noisy information. Expected crossing points of possible trajectories are nonlinearily associated with positions and orientations of robot and human. The nonlinear transformation of a noisy system input, such as directions of motion of human and robot, to a system output, the expected area of intersection of their trajectories, is done by two methods: statistical linearization and the sigma-point-transformation. For both approaches fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from given output distribution parameters.

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Section: Geometrical Relationsmentioning

confidence: 99%

Section: Statistical Linearizationmentioning

confidence: 99%

See 1 more Smart Citation

Crossing Point Estimation in Human/Robot Navigation - Statistical Linearization versus Sigma-Point-Transformation

Palm,

Lilienthal

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A humanfriendly interaction between robots and humans can be obtained by human-like sensor systems [5]. A prominent role in robot navigation is the trajectory-crossing problem of robots and humans [6,7] and corresponding fuzzy solutions [8]. Motivations for a fuzzy solution of the intersection problem are manifold.…”

Section: Introductionmentioning

confidence: 99%

Crossing-Point Estimation in Human–Robot Navigation—Statistical Linearization versus Sigma-Point Transformation

Palm,

Lilienthal

2024

Sensors

Self Cite

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Interactions between mobile robots and human operators in common areas require a high level of safety, especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection, the crossings of planned trajectories and their uncertainty based on model fluctuations, system noise and sensor noise play an outstanding role. This paper discusses the calculation of the expected areas of interactions during human–robot navigation with respect to fuzzy and noisy information. The expected crossing points of the possible trajectories are nonlinearly associated with the positions and orientations of the robots and humans. The nonlinear transformation of a noisy system input, such as the directions of the motion of humans and robots, to a system output, the expected area of intersection of their trajectories, is performed by two methods: statistical linearization and the sigma-point transformation. For both approaches, fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from the given output distribution parameters.

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A two‐stage Bayesian framework for rapid dynamics identification in industrial robots

Qiao,

Yang,

Liu

et al. 2024

Intl J Robust & Nonlinear

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Accurate dynamic models that update in real‐time are vital for industrial robots to adapt flexibly and robustly to evolving environments and tasks, enabling precise model‐based control, motion planning and disturbance estimation. However, the identification of a dynamic robot model involves multiple challenges. First, existing friction models cannot accurately characterize torque variance during prolonged robot operations. Second, traditional offline identification methods such as the least squares (LS) method, suffer from over‐fitting when the inertia parameters are unknown a priori. Furthermore, existing online identification approaches, such as sliding LS, fail to meet the accuracy and real‐time requirements of industrial robots. To address these challenges, this article proposes a two‐stage Bayesian framework for rapid offline identification and online updating of industrial robot dynamics. In the offline stage, we developed a recursive sparse Bayesian learning (RSBL) method to select the dominant parameters and discover a thermal‐related nonlinear dynamic friction model from data, which was then used to find a sparser and simpler inertia model. The obtained friction and inertia models were used as prior knowledge for online updating. The RSBL method significantly reduces the computational complexity and runtime, while it also accelerates online model updating. Experimental results for a 6‐DOF medium‐load robot validate dual offline improvements in prediction accuracy and model sparsity, along with higher online prediction accuracy and shorter computational time.

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Gaussian noise and the intersection problem in Human-Robot Systems - analytical and fuzzy approach

Cited by 3 publications

References 15 publications

Crossing Point Estimation in Human/Robot Navigation - Statistical Linearization versus Sigma-Point-Transformation

Crossing Point Estimation in Human/Robot Navigation - Statistical Linearization versus Sigma-Point-Transformation

Crossing-Point Estimation in Human–Robot Navigation—Statistical Linearization versus Sigma-Point Transformation

A two‐stage Bayesian framework for rapid dynamics identification in industrial robots

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