IP Library › Granted Patent US 12,730,451
Granted Patent B2
US 12,730,451 · App. 18/128,337 · Granted Sep 8, 2026

Systems and methods for determining position errors of front hazard sensors on robots

Inventor: Micah Richert (San Diego, CA)
Assignee: Brain Corporation
G05D1/0274
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Quick Facts
Patent No.
US 12,730,451
App. No.
18/128,337
Granted
Sep 8, 2026
Kind
B2
Abstract

Systems and methods for detecting an error in the mounting of a front hazard sensor are disclosed herein. According to at least one exemplary embodiment, an error in a pose of a front hazard sensor may comprise the front hazard sensor being orientated or positioned incorrectly with respect to a default pose. The present disclosure provides systems and methods for determining if this error in the pose is present.

Claims (44)

1 . A method for determining an error in a pose of a front hazard sensor, comprising:

determining a margin of error based on an expected measurement from the front hazard sensor at a default pose, the front hazard sensor coupled to a robot;

collecting a plurality of distance measurements from the front hazard sensor as the robot navigates a route through an environment over a duration in time;

calculating measurement errors of the front hazard sensor based on a magnitude of a discrepancy between the margin of error and each of the plurality of collected distance measurements;

determining an error parameter based on a normalized summation of average values of the calculated measurement errors over the duration in time;

determining an error in mounting of the front hazard sensor based on the error parameter meeting or exceeding a threshold; and

outputting a signal indicative of the determined error,

wherein the signal is communicated to one or more actuator units to stop the robot.

2 . The method of claim 1 , further comprising:

determining, based on a computer readable map of the environment, locations along the route traveled by the robot where value of the error parameter increases due to objects in the environment; and

omitting measurement errors for distance measurements which detect the objects based on the computer readable map.

3 . The method of claim 1 , further comprising:

adjusting the threshold or value of the error parameter based on predetermined objects within the environment, the predetermined objects configured to cause an increase in value of the error parameter.

4 . The method of claim 1 , wherein the signal is outputted to a device, the device is configured to indicate detection of the error in the pose of the front hazard sensor.

5 . A non-transitory computer readable storage medium comprising a plurality of computer readable instructions stored thereon, that when executed by a processor, configure the processor to:

determine a margin of error based on an expected measurement from a front hazard sensor at a default pose on a robot;

collect a plurality of distance measurements from the front hazard sensor at an operating pose as the robot navigates a route through an environment over a duration in time;

calculate measurement errors of the front hazard sensor based on a magnitude of a discrepancy between the margin of error and each of the plurality of collected distance measurements;

determine an error parameter based on a normalized summation of average values of the calculated measurement errors and the duration in time;

determine an error in mounting of the front hazard sensor based on the error parameter meeting or exceeding a threshold; and

output a signal indicative of the determined error,

wherein the signal is communicated to one or more actuator units to stop the robot.

6 . The non-transitory computer readable storage medium of claim 5 , wherein the processor is further configured to execute the computer readable instructions to,

determine, based on a computer readable map of the environment, locations along the route traveled by the robot where value of the error parameter increases due to objects in the environment; and

omit errors for distance measurements which detect the objects based on the computer readable map.

7 . The non-transitory computer readable storage medium of claim 5 , wherein the processor is further configured to execute the computer readable instructions to

adjust the threshold or value of the error parameter based on predetermined objects within the environment, the predetermined objects configured to cause an increase in value of the error parameter value.

8 . The non-transitory computer readable storage medium of claim 5 , wherein the signal is outputted to a device, the device configured to indicate detection of the error in the operating pose of the front hazard sensor.

9 . A robotic system, comprising:

a non-transitory computer readable storage medium comprising computer readable instructions stored thereon; and

at least one processor configured to execute the computer readable instructions to,

determine a margin of error for a measurement of a front hazard sensor mounted to the robotic system at a default pose;

collect a plurality of distance measurements from a front hazard sensor as the robotic system navigates a route through an environment over a duration in time;

determine measurement errors of the front hazard sensor based on a magnitude of a discrepancy between the margin of error and each of the plurality of the distance measurements collected;

determine a value of an error parameter based on a normalized summation of average values of the measurement errors and the duration in time; and

detect an error in a pose of the front hazard sensor based on the value of the error parameter meeting or exceeding a threshold; and

output a signal based on the detected error in the pose,

wherein the signal is communicated to one or more actuator units of the robotic system to stop the robotic system.

10 . The robotic system of claim 9 , wherein the processor is further configured to execute the computer readable instructions to

determine, based on a computer readable map of an environment, locations along the route traveled by the robotic system where value of the error parameter is configured to increase due to objects in the environment; and

omit errors for distance measurements which detect the objects based on the computer readable map.

11 . The robotic system of claim 9 , wherein the processor is further configured to execute the computer readable instructions to

adjust the threshold or value of the error parameter based on predetermined objects within the environment, the predetermined objects configured to cause an increase in value of the error parameter value.

12 . The robotic system of claim 9 , wherein the signal is outputted to a device, the device configured to indicate detection of the error in the pose of the front hazard sensor.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 30, 2023
From: RICHERT, MICAH
To: BRAIN CORPORATION
Reel/Frame 065389/0555 →
Continuity (3)
Continuation PCTUS2021053875 · Oct 7, 2021
Provisional Application 63088583 · Oct 7, 2020
Related Publication 20230236607A1 · Jul 27, 2023
References Cited (130)
US 5111401A · Everett, Jr. et al. · 1992 [cited by applicant]
US 5696675A · Nakamura et al. · 1997 [cited by applicant]
US 5758298A · Guldner · 1998 [cited by applicant]
US 5793900A · Nourbakhsh et al. · 1998 [cited by applicant]
US 5793934A · Bauer · 1998 [cited by applicant]
US 6667592B2 · Jacobs et al. · 2003 [cited by applicant]
US 7170252B2 · Maeki · 2007 [cited by applicant]
US 7502065B2 · Nakahara · 2009 [cited by applicant]
US 7965890B2 · Marcus et al. · 2011 [cited by applicant]
US 8185239B2 · Teng et al. · 2012 [cited by applicant]
US 8300890B1 · Gaikwad et al. · 2012 [cited by applicant]
US 8542875B2 · Eswara · 2013 [cited by applicant]
US 8903589B2 · Sofman et al. · 2014 [cited by applicant]
US 8918241B2 · Chen et al. · 2014 [cited by applicant]
US 8948913B2 · Choi et al. · 2015 [cited by applicant]
US 8954191B2 · Yi et al. · 2015 [cited by applicant]
US 9076214B2 · Tsutsumi · 2015 [cited by applicant]
US 9122948B1 · Zhu et al. · 2015 [cited by applicant]
US 9174672B2 · Zeng et al. · 2015 [cited by applicant]
US 9192869B2 · Moriya · 2015 [cited by applicant]
US 9208384B2 · Conwell et al. · 2015 [cited by applicant]
US 9216745B2 · Beardsley et al. · 2015 [cited by applicant]
US 9233472B2 · Angle et al. · 2016 [cited by applicant]
US 9286810B2 · Eade et al. · 2016 [cited by applicant]
US 9304001B2 · Park et al. · 2016 [cited by applicant]
US 9380922B2 · Duffley et al. · 2016 [cited by applicant]
US 9400501B2 · Schnittman · 2016 [cited by applicant]
US 9404756B2 · Fong et al. · 2016 [cited by applicant]
US 9519289B2 · Munich et al. · 2016 [cited by applicant]
US 9534899B2 · Gutmann et al. · 2017 [cited by applicant]
US 9538892B2 · Fong et al. · 2017 [cited by applicant]
US 9864377B2 · Welty et al. · 2018 [cited by applicant]
US 9910444B2 · Eade et al. · 2018 [cited by applicant]
US 9952053B2 · Fong et al. · 2018 [cited by applicant]
US 10093021B2 · Aghamohammadi et al. · 2018 [cited by applicant]
US 10209063B2 · Anderson-Sprecher · 2019 [cited by applicant]
US 10383497B2 · Han et al. · 2019 [cited by applicant]
US 10667664B2 · Sheikh et al. · 2020 [cited by applicant]
US 11481918B1 · Ebrahimi Afrouzi · 2022 [cited by examiner]
US 11927965B2 · Ebrahimi Afrouzi et al. · 2024 [cited by applicant]
US 20040117079A1 · Hulden · 2004 [cited by applicant]
US 20060188168A1 · Sheraizin et al. · 2006 [cited by applicant]
US 20080059015A1 · Whittaker et al. · 2008 [cited by applicant]
US 20080294338A1 · Doh et al. · 2008 [cited by applicant]
US 20120121161A1 · Eade et al. · 2012 [cited by applicant]
US 20140207282A1 · Angle · 2014 [cited by examiner]
US 20150205299A1 · Schnittman · 2015 [cited by examiner]
US 20150261223A1 · Fong et al. · 2015 [cited by applicant]
US 20170197315A1 · Haegermarck · 2017 [cited by examiner]
US 20190196493A1 · Xiong · 2019 [cited by examiner]
US 20200225673A1 · Ebrahimi Afrouzi · 2020 [cited by examiner]
US 20220066456A1 · Ebrahimi Afrouzi · 2022 [cited by examiner]
CA 2886106A1 · 2014 [cited by examiner]
CN 104299244B · 2017 [cited by examiner]
EP 1240562B1 · 2004 [cited by applicant]
EP 2343615B1 · 2018 [cited by applicant]
JP 3265212B2 · 2002 [cited by applicant]
JP 2009216600A · 2009 [cited by applicant]
JP 2009288930A · 2009 [cited by applicant]
JP 5769455B2 · 2015 [cited by applicant]
KR 1020100027683A · 2010 [cited by applicant]
KR 101048098B1 · 2011 [cited by applicant]
WO 2011146259A2 · 2011 [cited by applicant]
WO 2020010043A1 · 2020 [cited by applicant]
International Search Report and Written Opinion for PCT/US21/53875 dated Jan. 11, 2022. [cited by applicant]
Basic Research Series: Optical Information Technology State-of-the-Art Report; Publication date 1993. [cited by applicant]
Bernard Feverjon et al., A local based approach for path planning of manipulators with a high number of degrees of freedom. Proceedings. 1987 IEEE International Conference on Robotics and Automation 4 (1987): 1152-1159. [cited by applicant]
Brett R. Fajen et al., A Dynamical Model of Visually-Guided Steering, Obstacle Avoidance, and Route Selection. International Journal of Computer Vision 54 (2003): 13-34. [cited by applicant]
Christoph Rosmann et al., Efficient Trajectory Optimization Using a Spares Model. 2013 Eur. Conf. on Mobile Robots; Sep. 2013. [cited by applicant]
Dave Ferguson et al., Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles, Int'l J. of Robotics Research, vol. 28(8); Jun. 26, 2009. [cited by applicant]
David G. Lowe, Object Recognition from Local Scale-Invariant Features; 1999. [cited by applicant]
Dieter Fox et al., The Dynamic Window Approach to Collision Avoidance, IEEE Robotics and Automation Magazine, vol. 4(1); Apr. 1997. [cited by applicant]
Francisco Amoros et al., Global Appearance Applied to Visual Map Building and Path Estimation Using Multiscale Analysis, Hindawi Publishing Corporation, Mathematical Problems in Engineering, vol. 2014 Article ID 365417;… [cited by applicant]
Gany Berkovic and Ehud Shafir, Optical Methods for Distance and Displacement Measurements; Sep. 11, 2012. [cited by applicant]
Handbook of Optical Systems, vol. 4: Survey of Optical Instruments; 2008. [cited by applicant]
Hani Safadi, Local Path Planning Using Virtual Potential Field, McGill University School of Computer Science; Apr. 18, 2007. [cited by applicant]
J.M. Rueger, Electronic Distance Measurement, 3rd Ed.; 1990. [cited by applicant]
J.M. Rueger, Electronic Distance Measurement, 4th Ed.; 1996. [cited by applicant]
Kazushi Nakazawa et al., Movement Control of Accompanying Robot Based on Artificial Potential Field Adapted to Dynamic Environments, Elec. Engin. in Japan, vol. 192; Feb. 2014. [cited by applicant]
Khalid Yousif et al., An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics, Intell. Ind. Syst. vol. 1, 289; Nov. 13, 2015. [cited by applicant]
Laurent Itti, Christof Koch, and Ernst Niebur, A Model of Saliency-Based Visual Attention for Rapid Scene Analysis; Nov. 1998. [cited by applicant]
Lina J. Karam, Nabil G. Sadaka, Rony Ferzli, and Zoran A. Ivanovski, An Efficient Selective Perceptual-Based Super-Resolution Estimator; Dec. 2011. [cited by applicant]
Lu and Milios, Globally Consistent Range Scan Alignment for Environment Mapping, Autonomous robots, 4 Autonomous robots 333-49 (1997). [cited by applicant]
Lu Yin et al., A New Potential Field Method for Mobile Robot Path Planning in the Dynamic Environments, Asian J. of Conti ol vol. 11(2); Mar. 2009. [cited by applicant]
Matthias Nieuwenhuisen et al., Predictive Potential Field-Based Collision Avoidance for Multicopters, Remote Sensing and Spatial Information Sciences, vol. XL-1; Sep. 4, 2013. [cited by applicant]
Mejdl Safran and Steven Haar, Arduino and Android Powered Object Tracking Robot; Nov. 27, 2012. [cited by applicant]
Oscar Montiel et al., Optimal Path Planning Generation for Mobile Robots Using Parallel Evolutionary Artificial Potential Field, J. Intell. Robot Sys.; Sep. 20, 2014. [cited by applicant]
Oussama Khatib, Real-Time Obstacle Avoidance for Manipulators and Mobile Robots Int'l J. of Robotics Research, vol. 5(1); 1986. [cited by applicant]
P. Payeur, Improving Robot Path Planning Efficiency with Probabilistic Virtual Environment Models. IEEE Int'l Conf. on Virtual Environments; Jul. 2004. [cited by applicant]
Peter Biber and Wolfgang StraBer, nScan-Matching: Simultaneous Matching of Multiple Scans and Application to SLAM, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. [cited by applicant]
Pierre Melchior et al., Robust Path Planning for Mobile Robot Based on Fractional Attractive Force. 2009 American Control Conf.; Jun. 2009. [cited by applicant]
Qian Jia & Xingsong Wang, An Improved Potential Field Method for Path Planning, 2010 Chinese Control and Decision Conference; 2010. [cited by applicant]
Qian Xu, Chaitali Chakrabarti, and Lina J. Karam, A Distributed Canny Edge Detector and its Implementation on FGPA; 2011. [cited by applicant]
Sean Quinlan & Oussama Khatib, Elastic Bands: Connecting Path Planning and Control, IEEE Int'l Conference on Robotics and Automation; 1998. [cited by applicant]
Srenivas Varadarajan and Lina J. Karam, An Improved Perception-Based No-Reference Objective Image Sharpness Metric Using Iterative Edge Refinement; 2008. [cited by applicant]
Srenivas Varadarajan, Chaitali Chakrabarti, Lina J. Karam, and Judit Maitinez Bauza, A Distributed Psycho—Visually Motivated Canny Edge Detector; 2010. [cited by applicant]
Srenivas Varadarajan, Lina J. Karam, and Dinei Florencio, Background Recovery from Video Sequences Using Motion Parameters; 2009. [cited by applicant]
Stachniss et al., Springer Handbook of Robotics, Chapter 46: Simultaneous Localization and Mapping, Springer (2d ed.); Jan. 1, 2016. [cited by applicant]
Prassler et al., Springer Handbook of Robotics, Chapter 65: Domestic Robots, Springer (2d ed.); Jan. 1, 2016. [cited by applicant]
Suzuki and Abe, Topological Structural Analysis of Digitized Binary Images by Border Following; 1983. [cited by applicant]
Wang Tianmiao et al., Mobile Robot Control Based on Dynamic Potential, Chinese J. of Sys. Engin. & Elec. vol. 3; Mar. 31, 1992. [cited by applicant]
Ya-Chun Chang & Yoshio Yamamoto, Path Planning of Wheeled Mobile Robot with Simultaneous Free Space Locating Capability, Int'l Serv. Robotics; Nov. 25, 2008. [cited by applicant]
Yong K. Hwang & Narenda Ahuja, A Potential Field Approach to Path Planning, IEEE Transactions on Robotics and Automation vol. 8(1); Feb. 1992. [cited by applicant]
Wolfgang Hess et al., Real-time loop closure in 2D Lidar Slam, 2016 IEEE International Conference on Robotics and Automation; May 16-21, 2016. [cited by applicant]
Zhenyu Wu et al., Collision Avoidance for Mobile Robots Based on Artificial Potential Field and Obstacle Envelope Modelling, Assembly Automation, vol. 36(3); Aug. 1, 2016. [cited by applicant]
Kaiqi Huang, Liangsheng Wang, Tieniu Tan, and Steve Maybank, A Real-Time Object Detecting and Tracking System for Outdoor Night Surveillance; 2008. [cited by applicant]
Subramanian Saravankumar et al., Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space, Intel Serv Robots 2013; Oct. 6, 2013. [cited by applicant]
Javier Minguez et al., Abstracting Vehicle Shape and Kinematic Constraints from Obstacle Avoidance Methods, Autonomous Robots, vol. 20(1); Feb. 3, 2006. [cited by applicant]
Alan Conrad Bovik, Marianna Clark, Wilson S. Geisler, Multichannel Texture Analysis Using Localized Spatial Filters; Jan. 1990. [cited by applicant]
John Canny, A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI- 8, No. 6; Nov. 1986. [cited by applicant]
Edwin B. Olson, Robust and Efficient Robotic Mapping, Massachusetts Institute of Technology; 2008. [cited by applicant]
Giorgio Grisetti, et al., Nonlinear Constraint Network Optimization for Efficient Map Learning, 10 IEEE Transactions on Intelligent Transportation Systems 428; Sep. 2009. [cited by applicant]
Edwin Olson, et al., Fast Iterative Alignment of Pose Graphs with Poor Initial Estimates, Proceedings of the 2006 IEE Conference on Robotics and Automation; May 2006. [cited by applicant]
John Folkesson and Henrik I. Christensen, Closing the Loop with Graphical SLAM 23 IEEE Transactions on Robotics 731; Aug. 2007. [cited by applicant]
Carlos Estrada, et al., Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments, 21 IEEE Transactions on Robotics 588; Aug. 2005. [cited by applicant]
Tim Bailey and Hugh Durrant-Whyte, Simultaneous Localization and Mapping (SLAM): Part II, IEEE Robotics & Optimization Magazine; Sep. 2006. [cited by applicant]
Seongsoo Lee and Sukhan Lee, Embedded visual SLAM: Applications for low-cost consumer robots, 20 IEEE Robotics & Automation Magazine 83; 2013. [cited by applicant]
J.A. Castellanos, J. Montiel, J. Neira, J.D Tardos, The SPmap: A probabilistic framework for simultaneous localization and map building, 15(5) IEEE Trans. Robotics and Automation 948-952; 1999. [cited by applicant]
J. Scholz, S. Chitta, B. Marthi, M. Likhachev, Cart Pushing with a Mobile Manipulation System: Towards Navigation with Moveable Objects, IEEE International Conference on Robotics and Automation 6115-6120; 2011. [cited by applicant]
Neato XV-11. [cited by applicant]
Internet Archives, WayBackMachine; http://www.neatorobotics.com/buy.html; Catured date: Jan. 23, 2010. [cited by applicant]
How Waterloo became a world-class robotics hub—The Globe and Mail; https://www.theglobeandmail.com/report-on-business/rob-magazine/how-waterloo-became-a-world-class-robotics-hub/article29784283/; Accessed Mar. 15, 2025. [cited by applicant]
News Release, iRobot Enters the Smart Home with Roomba® 980 Vacuum Cleaning Robot, Date: Sep. 16, 2015. [cited by applicant]
News Release, iRobot Expands Connected Product Line with Roomba® 960; Date: Aug. 4, 2016. [cited by applicant]
E. Eade, P. Fong and M. E. Munich, “Monocular graph SLAM with complexity reduction,” 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 2010, pp. 3017-3024. [cited by applicant]
Internet Archives, WayBackMachine; http://www.neatorobotics.com/faq.html; captured Jan. 24, 2010. [cited by applicant]
Neato XV-11 Robotic All-Floor Vacuum System; 2009. [cited by applicant]
Neato XV-11™ Vacuum User's Guide; Jan. 30, 2010. [cited by applicant]
Chang, Ya-Chun and Yoshio Yamamoto. “On-line path planning strategy integrated with collision and dead-lock avoidance schemes for wheeled mobile robot in indoor environments.” Ind. Robot 35 (2008): 421-434. [cited by applicant]
Internet Archives, WayBackMachine; http://www.robotreviews.com/reviews/review-of-the-neato-xv-11-the-roomba-killer; Catured date: Sep. 23, 2010. [cited by applicant]