This paper presents a cost-effective motion-planning method for robots operating in complex and realistic environments. While sampling-based path-planning algorithms, such as a rapidly exploring random tree (RRT) and its variants, have been highly effective for general path-planning problems, it is still difficult to find the minimum cost path in a complex space efficiently since RRT-based algorithms extend a search tree locally, requiring a large number of samples before finding a good solution. This paper presents an efficient nonmyopic path-planning algorithm by combining RRT*and a stochastic optimization method, called cross entropy. The proposed method constructs two RRT trees: The first tree is a standard RRT*tree, which is used to determine the nearest node in the tree to be extended to a randomly chosen point, and the second tree contains the first tree with additional long extensions. By maintaining two separate trees, we can grow the search tree nonmyopically to improve the efficiency of the algorithm while ensuring the asymptotic optimality of RRT*. From an extensive set of simulations and experiments using mobile and humanoid robots, we demonstrate that the proposed method consistently finds low-cost paths faster than the existing algorithms.

In this paper, we propose a coordinated localization algorithm for mobile sensor networks with camera sensors to operate under Global Positioning System (GPS) denied areas or indoor environments. Mobile robots are partitioned into two groups. One group moves within the field of views of remaining stationary robots. The moving robots are tracked by stationary robots and their trajectories are used as spatiotemporal features. From these spatiotemporal features, relative poses of robots are computed using multiview geometry and a group of robots is localized with respect to the reference coordinate based on the proposed multirobot localization. Once poses of all robots are recovered, a group of robots moves from one location to another while maintaining the formation of robots for coordinated localization under the proposed multirobot navigation strategy. By taking the advantage of a multiagent system, we can reliably localize robots over time as they perform a group task. In experiment, we demonstrate that the proposed method consistently achieves a localization error rate of 0.37% or less for trajectories of length between 715 cm and 890 cm using an inexpensive off-the-shelf robotic platform.

In this paper, we propose a cost-aware path planning algorithm for mobile robots. As a robot moves from one location to another, the robot is penalized by the cost at its current location. The overall cost of the robot is determined by the trajectory of the robot over the cost map. The goal of the proposed cost-aware path planning algorithm is to find the trajectory with the minimal cost. The cost map of a field can represent environmental parameters, such as temperature, humidity, chemical concentration, wireless signal strength, and stealthiness. For example, if the cost map represents packet drop rates at different locations, the minimum cost path between two locations is the path with the best possible communication, which is desirable when a robot operates under the environment with weak wireless signals. The proposed cost-aware path planning algorithm extends the rapidly-exploring random tree (RRT) algorithm by applying the cross entropy (CE) method for extending motion segments. We show that the proposed algorithm finds a path which is close to the near-optimal cost path and gives an outstanding performance compared to RRT and CE-based path planning methods through extensive simulation.