Control and Machine Intelligence for System Autonomy

Antsaklis, Panos J.; Rahnama, Arash

doi:10.1007/s10846-018-0832-6

Cited by 24 publications

(8 citation statements)

References 42 publications

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“…In recent years, machine learning is emerging as an alternative paradigm for data-driven robot control design (Antsaklis & Rahnama, 2018;Arulkumaran, Deisenroth, Brundage, & Bharath, 2017). Programming-by-demonstration approaches are proposed in Mollard, Munzer, Baisero, Toussaint, and Lopes (2015) and Rozo, Calinon, Caldwell, Jiménez, and Torras (2013), in which the human is physically teaching a specific task to the manipulator, that simultaneously learn dynamic behaviors and task execution.…”

Section: Contextmentioning

confidence: 99%

Robot control parameters auto-tuning in trajectory tracking applications

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Forgione

Piga

2020

Control Engineering Practice

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Autonomy is increasingly demanded to industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, adapting to the specific task to be executed without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task, which involves modeling/identification of the robot dynamics. This usually results in an onerous procedure, both in terms of experimental and data-processing time. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking, optimizing control parameters based on the specific trajectory to be executed. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., the equivalent link-masses of the feedback linearizator and the feedforward controller) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position error are also included. The performance of proposed approach is demonstrated on a torquecontrolled 7-degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in less than 130 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved. In addition, the generalization capabilities of the proposed approach are shown exploiting the proper reference trajectory for the tuning of the control parameters.

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Section: Contextmentioning

confidence: 99%

Robot control parameters auto-tuning in trajectory tracking applications

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2020

Control Engineering Practice

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“…However, in the field of engineering, “autonomous” is used as an adjective to describe systems having the ability of self-governance [17]. More precisely, according to [18], “a system is autonomous, with respect to a set of goals and under a set of uncertainties, if it achieves these goals under those uncertainties without external intervention.” Autonomous systems behave like human beings and perform complex tasks under varying degrees of uncertainty. To be able to address the uncertainty from environmental disturbances, autonomous systems must be able to sense and interact with their environment.…”

Section: Autonomy In Engineeringmentioning

confidence: 99%

“…“ Every autonomous system is a control system ” quoted in [18], and it implies the autonomous system would always have a set of goals to be achieved and a control mechanism to achieve them. It is believed that the autonomous system may be evolved from existing control system.…”

Section: Autonomy In Engineeringmentioning

confidence: 99%

Integrations between Autonomous Systems and Modern Computing Techniques: A Mini Review

Chen

Abbod

Shieh

2019

Sensors

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The emulation of human behavior for autonomous problem solving has been an interdisciplinary field of research. Generally, classical control systems are used for static environments, where external disturbances and changes in internal parameters can be fully modulated before or neglected during operation. However, classical control systems are inadequate at addressing environmental uncertainty. By contrast, autonomous systems, which were first studied in the field of control systems, can be applied in an unknown environment. This paper summarizes the state of the art autonomous systems by first discussing the definition, modeling, and system structure of autonomous systems and then providing a perspective on how autonomous systems can be integrated with advanced resources (e.g., the Internet of Things, big data, Over-the-Air, and federated learning). Finally, what comes after reaching full autonomy is briefly discussed.

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“…Improving the cognitive ability of robots has received a considerable amount of attention over the last decade. As described in the introduction, the two key factors of cognitive robotics, are knowledge representation and cognitive reasoning [3]. Although the problem of improving autonomy is non-trivial, it is relevant to a variety of robotic applications, for example, in humanoid robotics [4,5], human-robot interaction [6][7][8][9], Search and Rescue (SAR) [10] and multi-robot systems [11].…”

Section: Related Workmentioning

confidence: 99%

A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

2019

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High-level, real-time mission control of semi-autonomous robots, deployed in remote and dynamic environments, remains a challenge. Control models, learnt from a knowledgebase, quickly become obsolete when the environment or the knowledgebase changes. This research study introduces a cognitive reasoning process, to select the optimal action, using the most relevant knowledge from the knowledgebase, subject to observed evidence. The approach in this study introduces an adaptive entropy-based set-based particle swarm algorithm (AE-SPSO) and a novel, adaptive entropybased fitness quantification (AEFQ) algorithm for evidence-based optimization of the knowledge. The performance of the AE-SPSO and AEFQ algorithms are experimentally evaluated with two unmanned aerial vehicle (UAV) benchmark missions: (1) relocating the UAV to a charging station and (2) collecting and delivering a package. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. The results show that the AE-SPSO/AEFQ approach successfully finds the optimal state-transition for each mission task and that autonomous flight control is successfully achieved.

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Control and Machine Intelligence for System Autonomy

Cited by 24 publications

References 42 publications

Robot control parameters auto-tuning in trajectory tracking applications

Robot control parameters auto-tuning in trajectory tracking applications

Integrations between Autonomous Systems and Modern Computing Techniques: A Mini Review

A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

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