Model-Based Proprioceptive State Estimation for Spring-Mass Running

2012-01-01
Gur, Ozlem
Saranlı, Uluç
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 in-adequate 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 eliminates the need for an extensive and unrealistic sensor suite.
ROBOTICS: SCIENCE AND SYSTEMS VII

Suggestions

Model-based proprioceptive state estimation for spring-mass running
Gür, Özlem; Saranlı, Uluç (2012-01-01)
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 sta...
An Adaptive Unscented Kalman Filter For Tightly Coupled INS/GPS Integration
Akca, Tamer; Demirekler, Mübeccel (2012-04-26)
In order to overcome the various disadvantages of standalone INS and GPS, these systems are integrated using nonlinear estimation techniques. The standard and most widely used estimation algorithm for the INS/GPS integration is Extended Kalman Filter (EKF) which makes a first order approximation for the nonlinearity involved. Unscented Kalman Filter (UKF) approaches this problem by carefully selecting deterministic sigma points from Gaussian distributions and propagating these points through the nonlinear f...
A novel mobile robot navigation method based on combined feature based scan matching and fastslam algorithm
Özgür, Ayhan; Saranlı, Afşar; Konukseven, Erhan İlhan; Department of Electrical and Electronics Engineering (2010)
The main focus of the study is the implementation of a practical indoor localization and mapping algorithm for large scale, structured indoor environments. Building an incremental consistent map while also using it for localization is partially unsolved problem and of prime importance for mobile robot navigation. Within this framework, a combined method consisting of feature based scan matching and FastSLAM algorithm using LADAR and odometer sensor is presented. In this method, an improved data association ...
A Novel Map Merging Methodology for Multi-Robot Systems
Topal, Sebahattin; Erkmen, İsmet; Erkmen, Aydan Müşerref (2010-10-22)
In this paper, we consider the problem of occupancy grid map merging which is an important issue especially for multi-robot exploration task in search and rescue environments. We present scale invariant feature transform based methodology for combining individual partial map of robot units acquired from different parts of the mission environment. Proposed approach is designed not only for structured work areas, but also it is designed for unstructured and complex environment such as wide collapsed buildings...
Safe and Efficient Path Planning for Omni-directional Robots using an Inflated Voronoi Boundary
Aldahhan, Mohammed Rabeea Hashim; Schmidt, Klaus Verner (2019-11-01)
Path planning algorithms for mobile robots are concerned with finding a feasible path between a start and goal location in a given environment without hitting obstacles. In the existing literature, important performance metrics for path planning algorithms are the path length, computation time and path safety, which is quantified by the minimum distance of a path from obstacles. The subject of this paper is the development of path planning algorithms for omni-directional robots, which have the ability ...
Citation Formats
O. Gur and U. Saranlı, “Model-Based Proprioceptive State Estimation for Spring-Mass Running,” ROBOTICS: SCIENCE AND SYSTEMS VII, pp. 105–112, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53509.