Chen Wei-dong

Probabilistic Grid Map Based Localizability Estimation for Mobile Robots

Based on the widely used probabilistic grid map, a localizability estimation method for mobile robots is pro- posed. Firstly, the Fisher information matrix (FIM) of robot localization is transformed into discrete form, and a static localizability matrix suitable for off-line estimation based on the known grid map is obtained. On this basis, the impact factor of locally sensed unkown obstacles is adopted to modify the static localizability matrix, and a dynamic localizability matrix is proposed for on-line estimation to deal with unexpected dynamic changes of environments. This matrix describes both the localizability index and localizability direction of mobile robots quantitatively. The results of real robot experiments under different typical environments demonstrate the validity of the proposed method.

Multilayer Matching SLAM for Large-Scale and Spacious Environments

In large-scale and spacious environments, keeping a reliable data association and reducing computational complexity are challenges for the implementation of Simultaneous Localization and Mapping (SLAM). Focused on these problems, a multilayer-matching-based incremental SLAM algorithm is proposed in this article. In this algorithm, SLAM is simplified as a problem composed of a least-square-based optimization problem and data association. Then, it is solved in two steps. Firstly, a multilayer matching method is applied to deal with the data-association problem. Both matching between observation and local map and matching between different local maps are carried out. The uncertainty of the results-matching is described by the Fisher information matrix. Secondly, the robot pose is optimized through an incremental QR decomposition method. This algorithm effectively avoids the local minima caused by the limited observation information, and can build a consistent map of the environment. Meanwhile, the characters (hierarchical and incremental) of the proposed algorithm ensure low computational complexity. Experiments on simulation environments and two kinds of real environments with different sparse features verify that the algorithm is applicable for real-time application in large-scale and spacious environments.

A Rule-Driven Autonomous Robotic System Operating in a Time-Varying Environment

In this paper, the problem concerning how to coordinate concurrent behaviors, when controlling autonomous mobile robots (AMRs), is investigated. We adopt a FSM (finite state machine)-based behavior selection method to solve this problem. It is shown how a hybrid system for an AMR can be modeled as an automaton, where each node corresponds to a distinct robot state. Through transitions between states, robot can coordinate multiple behaviors easily and rapidly under dynamic environment. As an illustration, a soccer task was finished by an AMR system with this method. The robot performed well in the soccer games and won the game in the end.In this paper, the problem concerning how to coordinate concurrent behaviors, when controlling autonomous mobile robots (AMRs), is investigated. We adopt a FSM (finite state machine)-based behavior selection method to solve this problem. It is shown how a hybrid system for an AMR can be modeled as an automaton, where each node corresponds to a distinct robot state. Through transitions between states, robot can coordinate multiple behaviors easily and rapidly under dynamic environment. As an illustration, a soccer task was finished by an AMR system with this method. The robot performed well in the soccer games and won the game in the end.

A behavior-based implicit planning method in competitive environment

The environment of a middle-size autonomous robot soccer system (MARSS) is highly dynamic, competitive and partially observed. To decide quickly, behave smoothly and speedily, the proposed MARSS used a behavior-based implicit planning, and select suitable task according to the hidden state. To overcome imprecise perceptions and actions, several subtle but simple goal-driven behaviors are designed. Tightly integrated with the simple perceptions, these behaviors switch flexibly and robustly by continuous feedback. In the competitive environments, spontaneous interleaving of these behaviors implicitly plans effective behavior sequences to fulfill the games tasks, and exhibits some important emergent behaviors making the system design more simple, robust and competitive. The methods effectiveness is verified by experiments and games.