Simultaneous Trajectory Estimation and Planning via Probabilistic Inference
نویسندگان
چکیده
We provide a unified probabilistic framework for trajectory estimation and planning. The key idea is to view these two problems, usually considered separately, as a single problem. At each time-step the robot is tasked with finding the complete continuous-time trajectory from start to goal. This can be quite difficult; the robot must contend with a potentially high-degreeof-freedom (DOF) trajectory space, uncertainty due to limited sensing capabilities, model inaccuracy, and the stochastic effect of executing actions, and the robot must find the solution in (faster than) real time. To overcome these challenges, we build on recent probabilistic inference approaches to continuous-time localization and mapping and continuous-time motion planning. We solve the joint problem by iteratively recomputing the maximum a posteriori trajectory conditioned on all available sensor data and cost information. Finally, we evaluate our framework empirically in both simulation and on a mobile manipulator.
منابع مشابه
STEAP: Towards Online Estimation and Replanning
In this work, we present simultaneous trajectory estimation and planning (STEAP) a unified approach to solving continuous-time trajectory estimation and planning problems. Although, these problems are usually considered separately, within our framework we show how estimation and planning can benefit from each other and remove redundancy during computation. Each time-step the robot is tasked wit...
متن کاملHigh-dimensional Motion Planning using Latent Variable Models via Approximate Inference
In this work, we present an efficient framework to generate a motion trajectory of a robot that has a high degree of freedom (e.g., a humanoid robot). High-dimensionality of the robot configuration space often leads to difficulties in utilizing the widely-used motion planning algorithms because the volume of the decision space increases exponentially with the number of dimensions. To handle com...
متن کاملContinuous-Time Gaussian Process Motion Planning via Probabilistic Inference
We introduce a novel formulation of motion planning, for continuous-time trajectories, as probabilistic inference. We first show how smooth continuous-time trajectories can be represented by a small number of states using sparse Gaussian process (GP) models. We next develop an efficient gradient-based optimization algorithm that exploits this sparsity and Gaussian process interpolation. We call...
متن کاملMotion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs
With the increased use of high degree-of-freedom robots that must perform tasks in real-time, there is a need for fast algorithms for motion planning. In this work, we view motion planning from a probabilistic perspective. We consider smooth continuous-time trajectories as samples from a Gaussian process (GP) and formulate the planning problem as probabilistic inference. We use factor graphs an...
متن کاملSparse Gaussian Processes for Continuous-Time Trajectory Estimation on Matrix Lie Groups
Continuous-time trajectory representations are a powerful tool that can be used to address several issues in many practical simultaneous localization and mapping (SLAM) scenarios, like continuously collected measurements distorted by robot motion, or during with asynchronous sensor measurements. Sparse Gaussian processes (GP) allow for a probabilistic non-parametric trajectory representation th...
متن کامل