Adaptive robot navigation with collision avoidance subject to 2nd-order uncertain dynamics

Verginis, Christos K.; Dimarogonas, Dimos V.

doi:10.1016/j.automatica.2020.109303

Cited by 34 publications

(23 citation statements)

References 37 publications

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“…Many early methods used fuzzy logic to implement robot control for ground navigation over unstructured terrains (Babunski et al (2020); Adib and Masoumi (2017); Fries (2018)). As these methods didn't consider robot dynamics, methods were proposed to generate navigational controls that also model a robot's dynamics (Meng and Kak (1993); Kelly (1994); Dash et al (2015); Verginis and Dimarogonas (2021); Tsunoda et al (2020)). Under control theory, system identification has been commonly considered to model unknown dynamic systems by observing the behavior of the system to various robot inputs (Johansson (1993); Rabiner et al (1978)).…”

Section: Control Methods For Ground Robot Navigationmentioning

confidence: 99%

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

Siva¹,

Wigness²,

Rogers³

et al. 2021

Preprint

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Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method's performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.

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Section: Control Methods For Ground Robot Navigationmentioning

confidence: 99%

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

Siva¹,

Wigness²,

Rogers³

et al. 2021

Preprint

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show abstract

“…However, it does not consider dynamic elements. To enable a robot to move and recognize a target environment, many techniques and algorithms have been used [17][18][19][20][21][22][23]. A specific framework has been proposed in [12].…”

Section: Roboticsmentioning

confidence: 99%

SRAVIP: Smart Robot Assistant for Visually Impaired Persons

Albogamy¹,

Alotaibi²,

Alhawdan³

et al. 2021

IJACSA

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Vision is one of the most important human senses, as visually impaired people encounter various difficulties due to their inability to move safely in different environments. This research aimed to facilitate integrating such persons into society by proposing a robotic solution (Robot Assistance) to assist them in navigating within indoor environments, such as schools, universities, hospitals, airports, etc. according to a prescheduled task. The proposed system is called the smart robot assistant for visually impaired persons (SRAVIP). It includes two subsystems: 1) an initialization system aimed to initialize the robot, create an environment map, and register a visual impaired person as a target object; 2) a real-time operation system implemented to navigate the mobile robot and communicate with the target object using a speech-processing engine and an optical character recognition (OCR) module. An important contribution of the proposed SRAVIP is the user-independent, i.e. it does not depend on the user, and one robot can serve unlimited users. SRAVIP utilized Turtlebot3 robot to realize SRAVIP and then tested it in the College of Computer and Information Sciences, King Saud University, AlMuzahmiyah Campus. The experimental results confirmed that the proposed system could function successfully.

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“…and note that Ω is an open set. Further, the nonlinearities in (14) are smooth, the control law (3) -( 8), ( 13) is well defined and smooth in Ω and (q 0 1 , ξ s 0 ) ∈ Ω, owing to the admitted initial configurations and the update law of ρ 0 . Hence, the existence of a maximal solution to ( 14) is established [15].…”

Section: Proof Of Theoremmentioning

confidence: 99%

“…The latter is especially true when the robot has to operate in close proximity to the obstacle. In [14], an obstacle-cluttered environment with an agent of 2ndorder uncertain dynamics is considered and safe navigation is guaranteed. However, even in the aforementioned work, the complete Euler-Lagrange class is not addressed.…”

Section: Introductionmentioning

confidence: 99%

Improving Safety in Human-Robot Collaboration via Dynamic Active Constraints Enforcement

Kanakis

Rovithakis

2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

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The problem of motion planning in obstacle cluttered environments is an important task in robotics. In the literature several methodologies exist to address the problem. In this work we consider using the feedback-based approach, where the solution comes from designing a controller capable of guaranteeing trajectory tracking with obstacle avoidance. Commonly, all respective studies consider simplified robot dynamics, which is usually insufficient in practical applications.In this work we focus on the collision avoidance problem with respect to a moving spherical object. We assume knowledge of a nominal controller that achieves tracking of a desired trajectory in the absence of obstacles, and we design an auxiliary control scheme to guarantee that the robot's end-effector will always operate in a safe distance from the moving obstacle's surface. The controller we develop does not take into account the actual robot dynamics, thus constituting a truly model-free approach. Experimental studies conducted on a KUKA LWR4+ robotic manipulator clarify and verify the proposed control scheme.

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Adaptive robot navigation with collision avoidance subject to 2nd-order uncertain dynamics

Cited by 34 publications

References 37 publications

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

SRAVIP: Smart Robot Assistant for Visually Impaired Persons

Improving Safety in Human-Robot Collaboration via Dynamic Active Constraints Enforcement

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