Receding horizon estimation and control with structured noise blocking for mobile robot slip compensation

Abstract

The control of field robots in varying and uncertain terrain conditions presents a challenge for autonomous navigation. Online estimation of the wheel-terrain slip characteristics is essential for generating the accurate control predictions necessary for tracking trajectories in off-road environments. Receding horizon estimation (RHE) provides a powerful framework for constrained estimation, and when combined with receding horizon control (RHC), yields an adaptive optimisationbased control method. Presently, such methods assume slip to be constant over the estimation horizon, while our proposed structured blocking approach relaxes this assumption, resulting in improved state and parameter estimation. We demonstrate and compare the performance of this method in simulation, and propose an overlapping-block strategy to ameliorate some of the limitations encountered in applying noise-blocking in a receding horizon estimation and control (RHEC) context.

Experimental Evaluation of a Hierarchical Operating Framework for Ground Robots in Agriculture

“…The long-term planner is based on [6]- [9], in which an energy efficient path is generated by searching over a Probabilistic Roadmap of the environment to find the lowest cost paths between all the goal waypoints-determined using a learnt energy cost of motion metric-and by then solving an asymmetric Travelling Salesman problem over this emergent graph to yield the optimal plan (alternatively, the long-term plan can also be generated using our recent work in [10] on the orienteering problem with replenishment, accounting for when, where, and for how long to recharge resources). This plan takes into consideration the topography of the area, the slip conditions of the terrain, and any previously known non-traversable areas.…”

A Hierarchical Framework for Long-term and Robust Deployment of Field Ground Robots in Large-Scale Farming

“…Our approach combines a long term mission planner with a local dynamic path planner to achieve long term autonomy of a robot travelling between updated objective waypoints in the presence of both static obstacles and dynamic agents. This framework: (1) utilises our prior work in energy efficient path planning [5]- [6] for generating long term plans; (2) integrates this with our prior work on using Generative Recurrent Neural Networks (GRNN) and Monte Carlo Tree Search (MCTS) [7]- [8] as a local path planner; and (3) adopts online, slip-compensating Receding Horizon Motion Control (RHMC) [2]- [4] path tracking. Additionally, our framework generates a real-time updated map of static obstacles and traversable terrain in the environment, used both by the local dynamic planner and by a FS collision avoidance module.…”

“…As in our prior work [2]- [4], a receding horizon estimator (RHE) is adopted to provide an estimate of the robot state and slip conditions of the terrain. Then, based on the proximity to detected dynamic agents and static obstacles, a hierarchical mode switching module-a crucial element in the integration of the global optimal planner and the local dynamic planner-determines whether to source the local reference trajectory from the dynamic planner, or to follow the online update of the global path provided by the long term planner.…”

“…This work is primarily motivated by recent developments in the automation of field operations for unstructured environments such as construction, mining, and agriculture [1], where the application of field robotics has the potential to automate many essential or desirable tasks that are often labour-intensive, tedious or dangerous. To achieve this, however, a number of significant challenges must be addressed, including robust motion control across a variety of terrains [2]- [4], long-term autonomy and efficiency under resource constraints [5]- [6], and safe operation around moving obstacles [7]- [8]. In this paper we propose a principled operational framework capable of handling these challenges, building upon our recent research and development activities in agricultural robotics.…”

“…These trials are conducted in an environment which replicates terrain from real world field trial locations, and are deployed on a dynamic simulation of the University of Sydney's Swagbot agricultural robot platform. Our contributions include (i) a hierarchical framework for the deployment of field robots in unstructured and dynamic environments, integrating our prior work in [2]- [8]; (ii) improvement of collision avoidance around dynamic agents, extending on our recent work [7] for better planning persistence; (iii) comprehensive and high-fidelity validation in simulation, including traversal between waypoints to accomplish an updated set of tasks, whilst ensuring safe and efficient planning around dynamic agents, and adhering to resource and recharging constraints; (iv) demonstration of how this framework can be used to adapt to changing resource constraints by using different local planner approaches to achieve varied resource use.…”