Localizing external contact using proprioceptive sensors: The Contact Particle Filter

Manuelli, Lucas; Tedrake, Russ

doi:10.1109/iros.2016.7759743

Cited by 91 publications

(66 citation statements)

References 20 publications

(22 reference statements)

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“…A systematic and formal solution to this problem did not exist so far and will be derived in the following assuming that only one collision occurs at a time. A more expensive approach for multiple contacts is given in [62].…”

Section: B Isolationmentioning

confidence: 99%

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Robot Collisions: A Survey on Detection, Isolation, and Identification

2017

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Abstract-Robot assistants and professional co-workers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for realtime collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human-robot interaction or manipulation tasks. The problem is addressed for rigid robots first, and then extended to the presence of joint/transmission flexibility. The basic, physically-motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products.

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Section: B Isolationmentioning

confidence: 99%

“…Similarly, the estimation of multiple contact forces cannot be resolved with proprioceptive sensing only. In this context, its fusion with tactile sensors [63] and/or with exteroceptive sensing [64] are interesting recent research directions, together with the independent use of more expensive machine learning techniques [62].…”

Section: B Isolationmentioning

confidence: 99%

Robot Collisions: A Survey on Detection, Isolation, and Identification

2017

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“…Out of the approaches that rely solely on the robot's proprioception, the work of Alessandro De Luca stands out as the source for most of the major contributions to the field [5], [7], [8], [9], [10], [11], [12], [13]. Climent Bigas [14] and Manuelli [15], [16] proposed probabilistic methods to address this problem. These approaches are considered in the next section.…”

Section: B Related Workmentioning

confidence: 99%

Collision Detection and Isolation on a Robot using Joint Torque Sensing

Bimbo

Pacchierotti²,

Tsagarakis³

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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As robotic systems become more flexible and intelligent, they must be able to move into environments with a high degree of uncertainty or clutter, such as our homes, workplaces, and the outdoors. In these unstructured scenarios, it is possible that the body of the robot collides with its surroundings. As such, it would be desirable to characterise these contacts in terms of their location and interaction forces. This paper addresses the problem of detecting and isolating collisions between a robotic manipulator and its environment, using only on-board joint torque and position sensing. The algorithm is based on a particle filter and, under some assumptions, is able to identify the contact location anywhere on the robot body. It requires the robot to perform small exploratory movements, progressively integrating the new sensing information through a Bayesian framework. The approach was tested and benchmarked in simulation, with respect to its accuracy and robustness. Validation using a robot with joint torque sensing in a real environment demonstrated the applicability of the method to real-world scenarios.

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“…C. Beyond force/moment estimation 1) Contact axis: Following [17], we may assume that the external force has no moment at its point of application. This may happen, for example, when the force comes from a point contact.…”

Section: B Real Robotmentioning

confidence: 99%

“…Most approaches deal with this problem, for humanoid robots and manipulator arms, by using the residual method. This is achieved either using torque sensors, a model of the stiffness of the joint or an inversion of the motor model regarding electrical current or tracking error [16], [6], [17], [11], [10], [22]. However, the torque data may not be always available, since torque sensors are still heavy and expensive to be equipped at all the joints of a humanoid robot, and motor model inversion can be insufficient especially for the case of non-backdrivable or high-friction actuators.…”

Section: Introductionmentioning

confidence: 99%

Model-Based External Force/Moment Estimation for Humanoid Robots with no Torque Measurement

Benallegue¹,

Gergondet²,

Audrerr

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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The dynamics of a humanoid robot cannot be correctly described independently from the external forces acting on it. These forces have to be reconstructed to enable the robot to control them or to compensate for them. Force sensors are usually used to measure these forces, but because of their cost, they are often put only on the ankle/feet and possibly the wrists. This paper addresses the issue of the estimation of external forces and moments that apply at any part of a robot without direct force measurements and without torque measurements. The sensors used are the regular force sensors and the IMUs of the robot. The method relies on a model-based estimator able to make the fusion between these sensors and the whole body dynamics. The estimator reconstructs a single state vector containing the floating-base kinematics, a filtered measurement of contact force and an additional estimation external force that we evaluate in this paper. Validation is performed on HRP-2 in a multi-contact motion.

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Localizing external contact using proprioceptive sensors: The Contact Particle Filter

Cited by 91 publications

References 20 publications

Robot Collisions: A Survey on Detection, Isolation, and Identification

Robot Collisions: A Survey on Detection, Isolation, and Identification

Collision Detection and Isolation on a Robot using Joint Torque Sensing

Model-Based External Force/Moment Estimation for Humanoid Robots with no Torque Measurement

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