Abstract We address the problem of controlling a mobile robot to explore a partially known environment. The robot’s objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially observable Markov decision process (POMDP) with an information-theoretic objective function, and solve it applying forward simulation algorithms with an open-loop approximation. We present a new sample-based approximation for mutual information useful in mobile robotics. The approximation can be seamlessly integrated with forward simulation planning algorithms. We investigate the usefulness of POMDP based planning for exploration, and to alleviate some of its weaknesses propose a combination with frontier based exploration. Experimental results in simulated and real environments show that, depending on the environment, applying POMDP based planning for exploration can improve performance over frontier exploration.
Information on the location of the robot and environment features, or landmarks, may be modelled by a multivariate Gaussian distribution. The SLAM problem can then be solved for example by applying the Extended Kalman Filter. Exploration with such feature-based maps was studied by who describe an A-optimal exploration method, i.e. they minimize the trace of the state covariance matrix. They discretize the location of the robot to a grid and plan an informative trajectory in open loop as a sequence of discrete positions via a breadth-first search. A similar objective function was used by @cite_10 , adopting a model predictive control (MPC) approach for optimization over multiple time steps. Discretization of the action space was also applied by @cite_12 , who applied reinforcement learning to learn parameterized robot trajectories for exploration. A somewhat complementary approach was adopted in @cite_18 , where a set of candidate exploration targets were evaluated based on an utility function designed to balance exploration of unknown areas and seeing known landmarks to help maintain good localization information. However, an explicit information-theoretic quantification of the information gain was avoided.
@cite_18 @cite_10 @cite_12
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We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70 compared to a closest frontier exploration strategy and 57 compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state. Abstract We address the problem of controlling a mobile robot to explore a partially known environment. The robot’s objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially observable Markov decision process (POMDP) with an information-theoretic objective function, and solve it applying forward simulation algorithms with an open-loop approximation. We present a new sample-based approximation for mutual information useful in mobile robotics. The approximation can be seamlessly integrated with forward simulation planning algorithms. We investigate the usefulness of POMDP based planning for exploration, and to alleviate some of its weaknesses propose a combination with frontier based exploration. Experimental results in simulated and real environments show that, depending on the environment, applying POMDP based planning for exploration can improve performance over frontier exploration. In this paper, the possibility and necessity of multi-step trajectory planning in Extended Kalman Filter (EKF) based SLAM is investigated. The objective of the trajectory planning here is to minimize the estimation error of the robot and landmark locations subject to a given time horizon. We show that the problem can be regarded as an optimization problem for a gradually identified model. A numerical method is proposed for trajectory planning using a variant of the nonlinear Model Predictive Control (MPC). The proposed method is optimal in the sense that the control action is computed using all the information available at the time of decision making. Simulation results are included to compare the results from the one-step look-ahead trajectory planning and the proposed multi-step look-ahead technique. Autonomous exploration under uncertain robot location requires the robot to use active strategies to trade-off between the contrasting tasks of exploring the unknown scenario and satisfying given constraints on the admissible uncertainty in map estimation. The corresponding problem, namely active SLAM (Simultaneous Localization and Mapping) and exploration, has received a large attention from the robotic community for its relevance in mobile robotics applications. In this work we tackle the problem of active SLAM and exploration with Rao-Blackwellized Particle Filters. We propose an application of Kullback-Leibler divergence for the purpose of evaluating the particle-based SLAM posterior approximation. This metric is then applied in the definition of the expected information from a policy, which allows the robot to autonomously decide between exploration and place revisiting actions (i.e., loop closing). Extensive tests are performed in typical indoor and office environments and on well-known benchmarking scenarios belonging to SLAM literature, with the purpose of comparing the proposed approach with the state-of-the-art techniques and to evaluate the maturity of truly autonomous navigation systems based on particle filtering.