This paper presents a high-accuracy local positioning system (LPS) for an autonomous robotic greens mower. The LPS uses a sensor tower mounted on top of the robot and four active beacons surrounding a target area. The proposed LPS concurrently determines robot location using a lateration technique and calculates orientation using angle measurements. To perform localization, the sensor tower emits an ultrasonic pulse that is received by the beacons. The time of arrival is measured by each beacon and transmitted back to the sensor tower. To determine the robot's orientation, the sensor tower has a circular receiver array that detects infrared signals emitted by each beacon. Using the direction and strength of the received infrared signals, the relative angles to each beacon are obtained and the robot orientation can be determined. Experimental data show that the LPS achieves a position accuracy of 3.1 cm RMS, and an orientation accuracy of 0.23° RMS. Several prototype robotic mowers utilizing the proposed LPS have been deployed for field testing, and the mowing results are comparable to an experienced professional human worker.

In recent years, the success of single-robot SLAM has led to more multi-robot SLAM (MR-SLAM) research. A team of robots with MR-SLAM can explore an environment more efficiently and reliably; however, MR-SLAM also raises many challenging problems, including map fusion, unknown robot poses and scalability issues. The first two problems can be considered as an optimization problem of finding a consistent joint map based on robots’ relative poses and sensory data. This optimization problem exhibits a similar property of a singlerobot topological/metric mapping. To exploit this property, we propose a multi-robot SLAM (MR-SLAM) algorithm, which builds a graph-like topological map with vertices representing local metric maps and edges describing relative positions of adjacent local maps. In this MR-SLAM algorithm, the map fusion between two robots can be naturally done by adding an edge that connects two topological maps, and the estimation of relative robot pose is simply performed by optimizing this edge. For the third scalable problem, the proposed algorithm is also scalable to the number of robots and the size of an environment. Computer simulations with a public data set and experimental work on Pioneer 3-DX robots have been conducted to validate the performance of the proposed MR-SLAM algorithm.

Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar environment/structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can use the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save the exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be used to speed up the SLAM process and build a more accurate map. We have also derived the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM on a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in indoor environments

Traditionally, the SLAM problem solves the localization and mapping problem in explored and sensed regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can utilize the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be utilized to localize the robot and speed up the SLAM process. We also derive the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM in a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real-time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in an indoor environment

In this paper, we investigate energy-time-efficient dispatching methods for ant-like robots to cover an unmapped region effectively. These ant-like robots have very limited energy and sensor ability, making them practical and inexpensive to build. Our dispatching model was based on bio-inspired algorithms from an ant colony system. We assumed that all the ant-like robots start from their home starting point, the nest, and the region is composed of floor tiles that can be modelled as vertices in a graph. In this dispatching system, the ant-like robots leave pheromone on the tiles and use this information to cover the region. We developed and analyzed two different adaptive dispatching algorithms with different communication methods to the nest. We further compared these two adaptive dispatching algorithms with two non-adaptive methods. Extensive computer simulations validated the proposed adaptive algorithms, showing that they can dispatch ant-like mobile robots to cover an unmapped region with energy-time efficiency.

Simultaneous localization and map building (SLAM) is a fundamental and complex problem in mobile robot research. In SLAM, Kalman-filter-like implementations are widely adopted to localize a mobile robot and build a map simultaneously and incrementally. However, this approach requires extensive computations of order O(N/sup 2/), where N is the total number of landmarks. To make the computations more manageable, we propose a logarithmic map partitioning algorithm that partitions the global map into one local region and several sub-maps. The size of each sub-map is based on its distance from the mobile robot, and in each sub-map, a centroid landmark is selected to represent all the landmarks in the sub-map for SLAM computations. With this logarithmic-map partitioning, it maintains correlation updates with each sub-map and provides an efficient suboptimal solution to the SLAM problem. The number of landmarks reduces from N to a logarithm-based function of N, and the computational requirement reduces from O(N/sup 3/) to O(N/sup 2/), where N/sub L/ is the number of local landmarks. Furthermore, utilizing the compressed extended Kalman filter, the real-time computational complexity reduces to O(N/sub L//sup 2/). Computer simulation results showed that the proposed algorithm is consistent and efficient for a large number of landmarks.