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.

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This paper considers the trajectory planning problem for line-feature based SLAM in structured indoor environments. The robot poses and line features are estimated using Smooth and Mapping (SAM) which is found to provide more consistent estimates than the Extended Kalman Filter (EKF). The objective of trajectory planning is to minimise the uncertainty of the estimates and to maximise coverage. Trajectory planning is performed using Model Predictive Control (MPC) with an attractor incorporating long term goals. This planning is demonstrated both in simulation and in a real-time experiment with a Pioneer2DX robot.

-This paper provides a solution to the optimal trajectory planning problem in target localisation for multiple heterogeneous robots with bearing-only sensors. The objective here is to find robot trajectories that maximise the accuracy of the locations of the targets at a prescribed terminal time. The trajectory planning is formulated as an optimal control problem for a nonlinear system with a gradually identified model and then solved using nonlinear Model Predictive Control (MPC). The solution to the MPC optimisation problem is computed through Exhaustive Expansion Tree Search (EETS) plus Sequential Quadratic Programming (SQP). Simulations were conducted using the proposed methods. Results show that EETS alone performs considerably faster than EETS+SQP with only minor differences in information gain, and that a centralised approach outperforms a decentralised one in terms of information gain. We show that a centralised EETS provides a near optimal solution. We also demonstrate the significance of using a matrix to represent the information gathered.Index Terms -bearing only target localisation, multi-robot optimal trajectory planning, Extended Information Filter, Model Predictive Control, Sequential Quadratic Programming

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