Autonomous stair-hopping with Scout robots

Stoeter, S.A.; Rybski, Paul E.; Gini, Maria; Papanikolopoulos, N.

doi:10.1109/irds.2002.1041476

Cited by 56 publications

(24 citation statements)

References 13 publications

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“…Many larger jumping robots such as the planetary exploration robots developed by NASA [11], the jumping robot Grillo [12], Scout robots [13] or the pneumatic robot Leg-in-Rotor-V [14] are built very stiff and rigid to absorb the high forces. The stiffness required to absorb the high impact energy comes at a high weight cost however, which makes it ill-suited for flying platforms.…”

Section: Mechanical Design For Collision Absorption and Self-recomentioning

confidence: 99%

The AirBurr: A flying robot that can exploit collisions

Briod

Klaptocz

Zufferey

et al. 2012

2012 ICME International Conference on Complex Medical Engineering (CME)

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Abstract-Research made over the past decade shows the use of increasingly complex methods and heavy platforms to achieve autonomous flight in cluttered environments. However, efficient behaviors can be found in nature where limited sensing is used, such as in insects progressing toward a light at night. Interestingly, their success is based on their ability to recover from the numerous collisions happening along their imperfect flight path. The goal of the AirBurr project is to take inspiration from these insects and develop a new class of flying robots that can recover from collisions and even exploit them. Such robots are designed to be robust to crashes and can take-off again without human intervention. They navigate in a reactive way, bump into obstacles, and unlike conventional approaches, they don't need heavy modeling in order to fly autonomously. We believe that this new paradigm will bring flying robots out of the laboratory and allow them to tackle unstructured, cluttered environments.This paper aims at presenting the vision of the AirBurr project, as well as the latest results in the design of a platform capable of sustaining collisions and self-recovering after crashes.Index Terms-Robust bio-inspired indoor flying robot.

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Section: Mechanical Design For Collision Absorption and Self-recomentioning

confidence: 99%

The AirBurr: A flying robot that can exploit collisions

Briod

Klaptocz

Zufferey

et al. 2012

2012 ICME International Conference on Complex Medical Engineering (CME)

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“…This is evidenced by several research attempts to develop legged, [1][2][3][4][5][6][7][8][9][10][11][12][13] tracked, 14,15 and hybrid 16,17 robotic platforms for stair-climbing applications. Whereas stairs are not representative of naturally occurring terrain features, they are widely found in urban environments.…”

Section: Introductionmentioning

confidence: 99%

A Quadruped Robot With on-Boarding Sensing and Parameterized Gait for Stair Climbing

Brewer¹,

Kaipa²,

Gupta³

2012

Adaptive Mobile Robotics

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This paper presents the design of a quadrupedal robot that can automatically adapt its gait to, and climb, staircases of different configurations. This is accomplished by endowing the robot with a parameterized gait for stair climbing: First, a gait plan is synthesized that allows the robot to climb a stair of known dimensions. Second, the robot approaches a previously unseen stair and perceives its height and width by using an onboard vision system. Third, the synthesized gait plan is parameterized by the perceived estimates of height and width of the stair. Fourth, the robot executes the parameterized gait to climb the staircase; this thereby eliminates the need for a complex control system to achieve the same purpose. Whereas quadruped robots have previously demonstrated stair climbing, to the best of our knowledge, none have so far been capable of climbing stairs of variable height while simultaneously carrying all the needed perception, processing, and power modules on-board. Our work is one of the first successful attempts toward the above goal. Results with the robot climbing a variety of stair configurations demonstrate the effectiveness of our approach.

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“…In contrast to strictly airborne or land-based micro vehicles, several new hybrid configurations specifically designed to explore rough, uneven terrain have been developed [10][11][12][13][14]. These specialized vehicles offer numerous advantages over more conventional concepts given their unique ability to hop over large obstacles or gullies and also loiter on the ground for extended periods of time while conserving energy.…”

Section: Introductionmentioning

confidence: 99%

“…Also, Bachrach et al [9] developed a similar quadrotor vehicle using a scanning laser range sensor, onboard IMU, and similar visual SLAM techniques fused together with an extended Kalman filter. Unfortunately, vision-based navigation of micro vehicles requires relatively large computational power and complex sensing ability to drive such algorithms.In contrast to strictly airborne or land-based micro vehicles, several new hybrid configurations specifically designed to explore rough, uneven terrain have been developed [10][11][12][13][14]. These specialized vehicles offer numerous advantages over more conventional concepts given their unique ability to hop over large obstacles or gullies and also loiter on the ground for extended periods of time while conserving energy.…”

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confidence: 99%

Specialized Algorithm for Navigation of a Micro Hopping Air Vehicle Using Only Inertial Sensors

Scheuermann

Costello

2012

Journal of Dynamic Systems, Measurement, and Control

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The need for accurate and reliable navigation techniques for micro air vehicles plays an important part in enabling autonomous operation. Traditional navigation systems typically rely on periodic GPS updates and provide little benefit when operating indoors or in other similarly shielded environments. Moreover, direct integration of the onboard inertial measurement unit (IMU) data stream often results in substantial drift errors yielding virtually unusable positional information. This paper presents a new strategy for obtaining an accurate navigation solution for the special case of a micro hopping air vehicle, beginning from some known location and heading, using only one triaxial accelerometer and one triaxial gyroscope. Utilising the unique dynamics of the hopping vehicle, a piece-wise navigation solution is constructed by selectively integrating the inertial data stream for only those short periods of time while the vehicle is airborne. Inter-hop data post-processing and sensor bias recalibration are also used to further improve estimation accuracy. To assess the performance of the proposed algorithm, a series of tests were conducted in which the estimated vehicle position following a sequence of 10 consecutive hops was compared with measurements from an optical motion-capture system. On average, the final estimated vehicle position was within 0.70 m or just over 6% from its actual location based on a total traveled distance of approximately 11 m.Nomenclature a = vehicle linear acceleration vector a x , a y , a z = body frame components of the vehicle mass center acceleration B Ai , B Gi = i th component of the accelerometer and gyroscope bias vector C j Ai , C j Gi = cross-axis sensitivity of accelerometer and gyroscope i th axis with respect to the j th axis F = linearized system matrix g = gravitational acceleration H = linearized measurement matrix I = identity matrix K = Kalman gain matrix N Ai , N Gi = i th component of accelerometer and gyroscope noise vector P, Q, R = error, process noise, and measurement noise covariance matrices p, q, r = angular velocity components in body reference frame r a→b = position vector extending from generic point a to another point bCostello DS-11-1315 1 S A , S G = scaling matrices of accelerometer and gyroscope S Ai , S Gi = scale factor for i th axis of accelerometer and gyroscope T IB = transformation matrix from body to inertial reference frame T SB = transformation matrix from body to sensor reference frame t = time u, v, w = body frame components of vehicle translation velocity v, w = measurement and process noise vectors x, y, z = inertial position components of vehicle mass center x = system state vector y = system output vector z = sensor measurement vector α = vehicle angular acceleration vector δ A = accelerometer misposition vector φ, θ, ψ = vehicle Euler roll, pitch, and yaw angles ω = vehicle angular velocity vector Subscript B = body reference frame I = inertial reference frame S = sensor reference frame Superscript * = raw sensor value Introduct...

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Autonomous stair-hopping with Scout robots

Cited by 56 publications

References 13 publications

The AirBurr: A flying robot that can exploit collisions

The AirBurr: A flying robot that can exploit collisions

A Quadruped Robot With on-Boarding Sensing and Parameterized Gait for Stair Climbing

Specialized Algorithm for Navigation of a Micro Hopping Air Vehicle Using Only Inertial Sensors

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