The rapidly exploring random tree and its variants (RRTs) have been widely adopted as the motion planning algorithms for mobile robots. However, the trap space problem, such as mazes and S-shaped corridors, hinders their planning efficiency. In this article, we present a generalized Voronoi diagram (GVD)-based heuristic path planning algorithm to generate a heuristic path, guide the sampling process of RRTs, and further improve the motion planning efficiency of RRTs. Different from other heuristic algorithms that only work in certain environments or depend on specified parameter setting, the proposed algorithm can automatically identify the environment feature and provide a reasonable heuristic path. First, the given environment is initialized with a lightweight feature extraction from the GVD, which guarantees that any state in the free space can be connected to the feature graph without any collision. Second, to remove the redundancy of feature nodes, a feature matrix is proposed to represent connections among feature nodes and a corresponding feature node fusion technique is utilized to delete the redundant nodes. Third, based on the GVD feature matrix, a heuristic path planning algorithm is presented. This heuristic path is then used to guide the sampling process of RRTs and achieve real-time motion planning. The proposed GVD feature matrix can be also utilized to improve the efficiency of the replanning. Through a series of simulation studies and real-world implementations, it is confirmed that the proposed algorithm achieves better performance in heuristic path planning, feature extraction of free space, and real-time motion planning.

In this paper, we propose a novel path planning framework for autonomous exploration in unknown environments using a mobile robot. A graph structure is incrementally constructed along with the exploration process. The structure is the road map that represents the topology of the explored environment. To construct the road map, we design a sampling strategy to get random points in the explored environment uniformly. A global path from the current location of the robot to the target area can be found on this road map efficiently. We utilize a lazy collision checking method that only checks the feasibility of the generated global path to improve the planning efficiency. The feasible global path is further optimized with our proposed trajectory optimization method considering the motion constraints of the robot. This mechanism can facilitate the path cost evaluation for the next best view selection. In order to select the next best target region, we propose a utility function that takes into account both the path cost and the information gain of a candidate target region. Moreover, we present a target reselection mechanism to evaluate the target region and reduce the extra path cost. The efficiency and effectiveness of our approach are demonstrated using a mobile robot in both simulation and real experimental studies.

In a human-robot coexisting environment, reaching the target place efficiently and safely is pivotal for a mobile service robot. In this paper, a Risk-based Dual-Tree Rapidly exploring Random Tree (Risk-DTRRT) algorithm is proposed for the robot motion planning in a dynamic environment, which provides a homotopy optimal trajectory on the basis of a heuristic trajectory. A dual-tree framework consisting of an RRT tree and a rewired tree is proposed for the trajectory searching. The RRT tree is a time-based tree, considering the future trajectory predictions of the pedestrians, and this tree is utilized to generate a heuristic trajectory. However, the heuristic trajectory is usually nonoptimal. Then, a line-of-sight (LoS) control checking algorithm is proposed to detect whether two time-based nodes can be rewired with the least cost. On the basis of the LoS control checking algorithm, a tree rewiring algorithm is proposed to optimize the heuristic trajectory. The tree generated in the tree rewiring process is called the rewired tree. The trajectory generated by the Risk-DTRRT algorithm proves to be optimal in the homotopy class of the heuristic trajectory. The navigation run time and the lengths of the planned trajectories are selected to demonstrate the effectiveness of the proposed algorithm. The experimental results in both simulation studies and real-world implementations reveal that our proposed method achieves convincing performance in both static and dynamic environments.

In this paper, we propose a gait recognition method for service robots to conduct human following tasks. A walking sequence segmentation method is designed to extract the consecutive gait cycles from an arbitrary walking sequence. Based on the segmentation results, a novel hybrid gait feature is proposed to capture the static, dynamic, and trajectory features for each segmented key and supplementary gait cycles. A dataset of 25 human subjects is collected to evaluate the proposed method in three different walking paths with various walking directions. Experimental results show that the proposed method achieves satisfactory performance in terms of identification accuracy and Fcomb indexes on our dataset. Compared with five state-of-the-art gait recognition methods, the proposed method achieves the best performance on human gait recognition based on the walking sequences defined in our proposed dataset.

In this article, based on the rapidly-exploring random tree (RRT), we propose a novel and efficient motion planning algorithm using bidirectional RRT search. First, a RRT extend function is used to organize the sampled states under kinodynamic constraints. Meanwhile, the bidirectional search strategy is implemented to grow a forward tree and backward tree simultaneously in the tree extension process. When these two trees meet each other, the backward tree will act as a heuristic to guide the forward tree to continuously grow toward the goal state, where the algorithm switches to unidirectional search mode. Therefore, the two-point boundary value problem (BVP) in the connection process is avoided, and the extension process gets much accelerated. We also prove that probabilistic completeness is guaranteed. Numerical simulations are conducted to demonstrate that the proposed algorithm performs much better than the state-of-the-art algorithms in different environments. Note to Practitioners —The motivation of this work is to develop an efficient sampling-based motion planning algorithm for mobile robots. Conventional sampling-based algorithms are time-consuming to find a feasible solution under differential constraints. When applying bidirectional search strategy to improve them, the complex 2-point BVP is required to solve. In this article, the backward free is regarded as a heuristic to guide the tree growth. On the one hand, the advantage of bidirectional search is retained. On the other hand, the 2-point BVP is avoided. Therefore, the bidirectional-unidirectional technique can achieve efficient robot motion planning. The proposed algorithm can be extended to other specified sampling-based algorithms to further improve their performance. Besides, it can be also applied to autonomous driving, service robot and medical robots to achieve efficient motion planning.