Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain

Abstract

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modeling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion‐planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and it predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an onboard depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and nontraversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and we report results from nearly 300 experimental trials using a planetary rover platform in a Mars‐analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.

Enhanced Stochastic Mobility Prediction on Unstructured Terrain Using Multi-output Gaussian Processes

“…In our prior work, λ(s, a) encoded the variations of rover's attitude and configuration angles experienced between s and s , predicted using a kinematic model (see [3]). Since the exact path between s and s is not known in advance, these predictions were made at discrete locations along a straight line drawn between the initial state s and the average resultant state s = s + Δs a for this action in the training data (see Sect.…”

“…In our recent work [3] we proposed a mobility prediction method that learns a stochastic transition model from previous experience. This method considers the effects of terrain interaction on the macroscopic behaviour of the rover without modelling detailed wheel-soil interactions.…”

“…In prior work [3], we proposed a path planning approach that accounts for control uncertainty by learning a Stochastic Mobility Prediction Model (SMPM) from experience. In this paper, we build on this previous work by tackling two main limitations: (1) considering the control uncertainty in multiple dimensions jointly, rather than each dimension separately, and (2) taking into account the variability of the paths taken by the rover when collecting the input information about the terrain.…”

“…Addressing these challenges is important in enabling the application of planetary rovers that operate outside the possibility of human intervention, especially, since the most interesting scientific tasks must often be performed in the most difficult terrain [1]. In recent work [2,3], we explored mobility prediction using Gaussian process (GP) regression models where heading (bearing) and distance uncertainty are treated independently. Here we consider the case, where heading distance uncertainty are coupled, and model this coupling using a multi-output GP.…”

A Spatiotemporal Optimal Stopping Problem for Mission Monitoring with Stationary Viewpoints

“…There are many examples of such tasks, including undersea surveys [38,21], monitoring our natural environment [18], autonomous farming [5] and planetary exploration [33]. Monitoring allows for rapid response to failures and to important information that the robot may discover during the progress of its mission [22,23,7].…”