Active SLAM using Model Predictive Control and Attractor based Exploration

Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as model predictive control (a.k.a. receding horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone

I. Introduction

Planning actions for SLAM (Simultaneous Localisation and Mapping) requires fast algorithms that can adapt to changes in the environment. Changes occur when new features or obstacles are detected and when the state estimates in the EKF (Extended Kalman Filter) are updated. Platforms such as UAVs (Unmanned Aerial Vehicles) have hard real-time constraints. Local planning strategies, such as Model Predictive Control (MPC), are well suited to these systems and systems with highly dynamic environments. MPC is simple and fast, thus enabling continuous replanning to incorporate feedback and up to date knowledge of the system state and the environment.