Measuring Smoothness as a Factor for Efficient and Socially Accepted Robot Motion

Ruíz, Silvia; Calderita, Luis V.; Hidalgo-Paniagua, Alejandro; Bandera, Juan Pedro

doi:10.3390/s20236822

Cited by 9 publications

(11 citation statements)

References 23 publications

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“…To measure the smoothness of the robot’s toolpath along the Lamé curve, a so-called smoothness factor based on reference [ 42 ] was used. The Lamé curve parameters are recomputed 100 times per movement between the origin and target, and each path segment represents a linear approximation of the corresponding Lamé curve arc segment.…”

Section: Discussionmentioning

confidence: 99%

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Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Ionescu

2021

Sensors

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The paper addresses the problem of using machine learning in practical robot applications, like dynamic path planning with obstacle avoidance, so as to achieve the performance level of machine learning model scorers in terms of speed and reliability, and the safety and accuracy level of possibly slower, exact algorithmic solutions to the same problems. To this end, the existing simplex architecture for safety assurance in critical systems is extended by an adaptation mechanism, in which one of the redundant controllers (called a high-performance controller) is represented by a trained machine learning model. This model is retrained using field data to reduce its failure rate and redeployed continuously. The proposed adaptive simplex architecture (ASA) is evaluated on the basis of a robot path planning application with dynamic obstacle avoidance in the context of two human-robot collaboration scenarios in manufacturing. The evaluation results indicate that ASA enables a response by the robot in real time when it encounters an obstacle. The solution predicted by the model is economic in terms of path length and smoother than analogous algorithmic solutions. ASA ensures safety by providing an acceptance test, which checks whether the predicted path crosses the obstacle; in which case a suboptimal, yet safe, solution is used.

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Section: Discussionmentioning

confidence: 99%

“…Shorter paths also reduce wear and energy consumption. Some authors note that path smoothness is a factor in the social perception of robots, with smoother paths being more acceptable [ 42 ].…”

Section: Applicationmentioning

confidence: 99%

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Ionescu

2021

Sensors

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“…Therefore, an omnidirectional mobile robot is able to keep the same orientation during the whole displacement θ f = θ i , reach a specific final angular orientation θ f = 90 • , rotate the mobile robot during the displacement θ f = θ i + N•360 • or maintain an orientation tangent to the planned trajectory (θ K = tan((y k+1 − y k )/(x k+1 − x k ))) in order to define a humanlike smooth motion that is expected to be more socially accepted [33]. This linearizing and smoothing strategy is based on the assumption that the motion command required to move the omnidirectional mobile robot between two positions (x k , y k , θ k ) → (x k+1 , y k+1 , θ k+1 ) with a known target translational velocity v can be analytically obtained using the procedures described in Section 3.2.…”

Section: Path Planning: Linearizing and Smoothing The Trajectorymentioning

confidence: 99%

“…Finally, the main advantage of the proposed path-following procedure is the automatic compensation of the motion errors caused by wheel slippage without requiring additional specific compensation procedures [13]. Another advantage of this path-following procedure is the ability to maintain a constant translational velocity v during the whole displacement, a feature that is expected to increase the social acceptance of a mobile robot operating in a shared space with humans [33].…”

Section: Path-following Proceduresmentioning

confidence: 99%

Evaluation of the Path-Tracking Accuracy of a Three-Wheeled Omnidirectional Mobile Robot Designed as a Personal Assistant

Palacín

Rubies

Clotet

et al. 2021

Sensors

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This paper presents the empirical evaluation of the path-tracking accuracy of a three-wheeled omnidirectional mobile robot that is able to move in any direction while simultaneously changing its orientation. The mobile robot assessed in this paper includes a precise onboard LIDAR for obstacle avoidance, self-location and map creation, path-planning and path-tracking. This mobile robot has been used to develop several assistive services, but the accuracy of its path-tracking system has not been specifically evaluated until now. To this end, this paper describes the kinematics and path-planning procedure implemented in the mobile robot and empirically evaluates the accuracy of its path-tracking system that corrects the trajectory. In this paper, the information gathered by the LIDAR is registered to obtain the ground truth trajectory of the mobile robot in order to estimate the path-tracking accuracy of each experiment conducted. Circular and eight-shaped trajectories were assessed with different translational velocities. In general, the accuracy obtained in circular trajectories is within a short range, but the accuracy obtained in eight-shaped trajectories worsens as the velocity increases. In the case of the mobile robot moving at its nominal translational velocity, 0.3 m/s, the root mean square (RMS) displacement error was 0.032 m for the circular trajectory and 0.039 m for the eight-shaped trajectory; the absolute maximum displacement errors were 0.077 m and 0.088 m, with RMS errors in the angular orientation of 6.27° and 7.76°, respectively. Moreover, the external visual perception generated by these error levels is that the trajectory of the mobile robot is smooth, with a constant velocity and without perceiving trajectory corrections.

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“…The roughness cost quantifies sudden turns along the trajectory, where perfectly straight paths receive a score of 0 degrees. Roughness is based on the smoothness scores proposed by Hidalgo-Paniagua et al (2017) and Guillén Ruiz et al (2020).…”

Section: Data Availability Statementmentioning

confidence: 99%

Using Remote Sensing and in situ Measurements for Efficient Mapping and Optimal Sampling of Coral Reefs

et al. 2021

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Coral reefs are of undeniable importance to the environment, yet little is known of them on a global scale. Assessments rely on laborious, local in-water surveys. In recent years remote sensing has been useful on larger scales for certain aspects of reef science such as benthic functional type discrimination. However, remote sensing only gives indirect information about reef condition. Only through combination of remote sensing and in situ data can we achieve coverage to understand reef condition and monitor worldwide condition. This work presents an approach to global mapping of coral reef condition that intelligently selects local, in situ measurements that refine the accuracy and resolution of global remote sensing. To this end, we apply new techniques in remote sensing analysis, probabilistic modeling for coral reef mapping, and decision theory for sample selection. Our strategy represents a fundamental change in how we study coral reefs and assess their condition on a global scale. We demonstrate feasibility and performance of our approach in a proof of concept using spaceborne remote sensing together with high-quality airborne data from the NASA Earth Venture Suborbital-2 (EVS-2) Coral Reef Airborne Laboratory (CORAL) mission as a proxy for in situ samples. Results indicate that our method is capable of extrapolating in situ features and refining information from remote sensing with increasing accuracy. Furthermore, the results confirm that decision theory is a powerful tool for sample selection.

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Measuring Smoothness as a Factor for Efficient and Socially Accepted Robot Motion

Cited by 9 publications

References 23 publications

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Evaluation of the Path-Tracking Accuracy of a Three-Wheeled Omnidirectional Mobile Robot Designed as a Personal Assistant

Using Remote Sensing and in situ Measurements for Efficient Mapping and Optimal Sampling of Coral Reefs

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