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Mobile robots are frequently faced with mapping and exploring uncertain environments in surveillance, military, and convenience tasks. Often times, human teleoperation is either inconvenient or infeasible for these kinds missions. Furthermore, these tasks can be improved by cooperative multi-agent systems, where coordinating robotic efforts can be complicated and computationally-expensive. This paper presents a stochastic framework for autonomous exploration and patrol with multiple cooperating robots. The first contribution extends the authors’ prior work in single-robot exact occupancy grid mapping and autonomous exploration in a 2D environment to mapping and exploring in a 3D environment. The proposed 3D occupancy grid map is computed efficiently using an inverse sensor model that accounts for the sensor uncertainty, where we propose how several measurement sources may be fused together by considering depth readings individually. This approach is scalable to larger and more complex scenarios for real-time mapping. Furthermore, this paper shows how important aspects of a 3D map representing a structured environment are projected onto a 2D occupancy grid map, where an autonomous exploration algorithm is designed to select robotic motions that maximize map information gain. The mapping and exploration algorithms are demonstrated with an experiment where a quadrotor autonomously maps and explores an initially-uncertain environment. The second contribution is a novel approach to multi-vehicle cooperative patrol of environments based on map uncertainty. We propose a cooperative autonomous exploration algorithm, which applies a bidding-based framework to coordinate robotic efforts for improving occupancy grid map information gain. Since these exploration approaches are based on probabilistic knowledge about the map, the 3D occupancy grid map is systematically degraded over time to encourage the robots to revisit regions as time passes, thereby patrolling the environment. Furthermore, using a Bayesian framework and receding horizons, the algorithm is robust to dynamic obstacles within the mapping space. The efficacy of the proposed multi-vehicle cooperative patrol is illustrated with a simulation involving three robots patrolling a large floor plan with a non-cooperative person walking around the space.

This paper is focused on probabilistic occupancy grid mapping and motion planning such that a robot may build a map and explore a target area autonomously in real time. The desired path of the robot is developed in an optimal fashion to maximize the information gain from the sensor measurements on its path, thereby increasing the accuracy and efficiency of mapping, while explicitly considering the sensor limitations such as the maximum sensing range and viewing angle. Most current exploration techniques require frequent human intervention, often developed for omnidirectional sensors with infinite range. The proposed research is based on realistic assumptions on sensor capabilities. The unique contribution is that the mapping and autonomous exploration techniques are systematically developed in a rigorous, probabilistic formulation. The mapping approach exploits the probabilistic properties of the sensor and map explicitly, and the autonomous exploration is designed to maximize the expected map information gain, thereby improving the efficiency of the mapping procedure and the quality of the map substantially. The efficacy of the proposed optimal approach is illustrated by both numerical simulations and experimental results.

This paper presents a geometric control system for a quadrotor unmanned aerial vehicle with decoupled attitude controls. In particular, the attitude control system on the special orthogonal group is decomposed into the reduced attitude controls for the total thrust direction evolving on the two-dimensional unit sphere, and for the remaining one-dimensional rotations about the thrust vector corresponding to the yawing motion. Consequently, the yaw dynamics are controlled separately from the roll and pitch dynamics that are critical for the stability of the translational dynamics of the quadrotor. As such, the proposed controller exhibits improved position tracking capabilities especially for large-angle yawing motions. These are constructed directly on the two-sphere and the one-sphere to avoid complexities and singularities associated with local coordinates. Furthermore, the control systems are augmented with integral terms to deal with fixed disturbances. The efficacy of the proposed method is illustrated by numerical simulation.

This paper deals with the aerial exploration for an unknown three-dimensional environment, where Bayesian probabilistic mapping is integrated with a stochastic motion planning scheme to minimize the map uncertainties in an optimal fashion. We utilize the popular occupancy grid mapping representation, with the goal of determining occupancy probabilities of evenly-spaced grid cells in 3D with sensor fusion from multiple depth sensors with realistic sensor capabilities. The 3D exploration problem is decomposed into 3D mapping and 2D motion planning for efficient real-time implementation. This is achieved by projecting important aspects of the 3D map onto 2D maps, where a predicted level of map uncertainty, known as Shannon's entropy, provides an exploration policy that governs robotic motion. Both mapping and exploration algorithms are demonstrated with both numerical simulations and quadrotor flight experiments.

Occupancy grid maps are spatial representations of environments, where the space of interest is decomposed into a number of cells that are considered either occupied or free. This paper focuses on exploring occupancy grid maps by predicting the uncertainty of the map. Based on recent improvements in computing occupancy probability, this paper presents a novel approach for selecting robot poses designed to maximize expected map information gain represented by the change in entropy. This result is simplified with several approximations to develop an algorithm suitable for real-time implementation. The predicted information gain proposed in this paper governs an effective autonomous exploration strategy when applied in conjunction with an existing motion planner to avoid obstacles, which is illustrated by numerical examples.

Occupancy grid maps are spatial representations of environments, where the space of interest is decomposed into a number of cells that are considered either occupied or free. This paper focuses on occupancy grid mapping, which is to estimate the probability of occupancy for each cell based on range measurements from a known location. For a given probabilistic model of a range sensor, we propose a computationally efficient method to obtain an exact inverse sensor model, and it is utilized to construct a probabilistic mapping algorithm according to the Bayesian framework. Compared with the existing occupancy grid mapping techniques that rely on approximate, heuristic inverse sensor models, the proposed approach yields substantially more accurate maps for the same set of measurements. These are illustrated by numerical examples and experiments.