Proprioceptive localilzatilon for a quadrupedal robot on known terrain

Abstract-This paper describes an algorithm for the probabilistic fusion of sensor data from a variety of modalities (inertial, kinematic and LIDAR) to produce a single consistent position estimate for a walking humanoid. Of specific interest is our approach for continuous LIDAR-based localization which maintains reliable drift-free alignment to a prior map using a Gaussian Particle Filter. This module can be bootstrapped by constructing the map on-the-fly and performs robustly in a variety of challenging field situations. We also discuss a two-tier estimation hierarchy which preserves registration to this map and other objects in the robot's vicinity while also contributing to direct low-level control of a Boston Dynamics Atlas robot. Extensive experimental demonstrations illustrate how the approach can enable the humanoid to walk over uneven terrain without stopping (for tens of minutes), which would otherwise not be possible. We characterize the performance of the estimator for each sensor modality and discuss the computational requirements.

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“…Localization of a quadruped (in 3 DOF) against a prior terrain map was explored by [5] using a particle filter.…”

Section: A Related Workmentioning

confidence: 99%

Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing

Fallon

Antone

Roy

et al. 2014

2014 IEEE-RAS International Conference on Humanoid Robots

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“…where velocity estimates are obtained through derivatives of (7) and (8). We then use straightforward analytic differentiation to linearize this map around the current estimate X n−1 and obtain EKF covariance update equations.…”

Section: ) Approximate Analytic Motion Model (Aam)mentioning

confidence: 99%

“…Consequently, state estimation methods have been a crucial component of research on mobile robotics with a wide variety of methods developed for platforms with simple movement patterns such as wheeled robots or kinematically observable designs such as fully actuated and statically stable legged robots [9] even while moving across rough terrain [8]. However, numerous challenges still exist for accurate state estimation with robots capable of dynamically dexterous mobility since such platforms are often underactuated and rely on second-order dynamics with state components not fully observable through kinematics alone.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Proprioceptive State Estimation for Spring-Mass Running

Gur¹,

Saranlı²

2011

Robotics: Science and Systems VII

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Abstract-Autonomous applications of legged platforms will inevitably require accurate state estimation both for feedback control as well as mapping and planning. Even though kinematic models and low-bandwidth visual localization may be sufficient for fully-actuated, statically stable legged robots, they are inadequate for dynamically dexterous, underactuated platforms where second order dynamics are dominant, noise levels are high and sensory limitations are more severe. In this paper, we introduce a model based state estimation method for dynamic running behaviors with a simple spring-mass runner. By using an approximate analytic solution to the dynamics of the model within an Extended Kalman filter framework, the estimation accuracy of our model remains accurate even at low sampling frequencies. We also propose two new event-based sensory modalities that further improve estimation performance in cases where even the internal kinematics of a robot cannot be fully observed, such as when flexible materials are used for limb designs. We present comparative simulation results to establish that our method outperforms traditional approaches which rely on constant acceleration motion models and that it 1eliminates the need for an extensive and unrealistic sensor suite.

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“…Recent research using the LittleDog platform [6], [7], [8], [1], [9] has led to further exploration of quadruped gaits for locomotion over rough terrain. However, there has been limited recent research into the use of graph-based planners for legged locomotion.…”

Section: A Previous Workmentioning

confidence: 99%

“…A local planning approach in [1] first planned an overall trajectory for the body of the robot and then planned over a finite horizon of footsteps. This approach had the benefit of planning over a small horizon and thus the planner could react faster to perturbations since it was always replanning the next step from the current position of the robot.…”

Section: Introductionmentioning

confidence: 99%

Search-based planning for a legged robot over rough terrain

Vernaza

Likhachev

Bhattacharya

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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: Vernaza, P.; Likhachev, M.; Bhattacharya, S.; Chitta, S.; Kushleyev, A.; Lee, D.D., "Search-based planning for a legged robot over rough terrain," Robotics and Automation, 2009. ICRA '09. IEEE International Conference on , vol., no., pp.2380-2387 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Search-based Planning for a Legged Robot over Rough Terrain AbstractWe present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract-We present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the highdimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains. ...

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Proprioceptive localilzatilon for a quadrupedal robot on known terrain

Cited by 40 publications

References 10 publications

Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing

Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing

Model-Based Proprioceptive State Estimation for Spring-Mass Running

Search-based planning for a legged robot over rough terrain

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