Motion Planning for Multiple Mobile Manipulator System in Complex Flipping Manipulation

Multiple robot systems are favored for object manipulation and transportation, especially for large objects. However, in more complex manipulation such as flipping, these systems encounter a new challenge, configuration disconnectivity of manipulators. Grasping objects by manipulators will impose closed-chain constraints on the system, which in turn limits the feasible motions of manipulators and further compromises the configuration connectivity. Multiple mobile manipulator systems show much more flexibility in object manipulation with the mobility of the mobile platform and have the potential to address the above problem. In this paper, a novel planning framework is proposed for complex flipping manipulation by incorporating platform motions and regrasping. Firstly, two types of trajectories, mobile manipulator planning and regrasping planning, are classified and can be assigned different priorities for different tasks. Secondly, corresponding planning methods are designed for each type of trajectory. Specifically, in mobile manipulator planning, the configuration of the platform is determined through optimization to ensure connectivity when the manipulator approaches configuration boundaries. In regrasping planning, closed-chain constraints are temporarily disregarded and the manipulation capabilities are prioritized to facilitate subsequent planning. Finally, the structure of the overall planning framework is provided. Experimental results demonstrate that the proposed planner efficiently plans the motions of the system to accomplish flipping manipulation. Additionally, a comprehensive experiment emphasizes the significance of our planner in extending the capabilities of multiple mobile manipulator systems in complex tasks.

FHT-Map: Feature-based Hierarchical Topological Map for Relocalization and Path Planning

Topological maps are favorable for their small storage compared to geometric map. However, they are limited in relocalization and path planning capabilities. To solve this problem, a feature-based hierarchical topological map (FHT-Map) is proposed along with a real-time map construction algorithm for robot exploration. Specifically, the FHT-Map utilizes both RGB cameras and LiDAR information and consists of two types of nodes: main node and support node. Main nodes will store visual information compressed by convolutional neural network and local laser scan data to enhance subsequent relocalization capability. Support nodes retain a minimal amount of data to ensure storage efficiency while facilitating path planning. After map construction with robot exploration, the FHT-Map can be used by other robots for relocalization and path planning. Experiments are conducted in Gazebo simulator, and the results demonstrate that the proposed FHT-Map can effectively improve relocalization and path planning capability compared with other topological maps. Moreover, experiments on hierarchical architecture are implemented to show the necessity of two types of nodes.

Forward Kinematics of Object Transport by a Multi-Robot System with Deformable Sheet

We present object handling and transport by a multi-robot team with a deformable sheet as a carrier. Due to the deformability of the sheet and the high dimension of the whole system, it is challenging to clearly describe all the possible positions of the object on the sheet for a given formation of the multi-robot system. A complete forward kinematics (FK) method is proposed in this paper for object handling by an $N$-mobile robot team with a deformable sheet. Based on the virtual variable cables model, a constrained quadratic problem (CQP) is formulated by combining the form closure and minimum potential energy conditions of the system. Analytical solutions to the CQP are presented and then further verified with the force closure condition. With the proposed FK method, all possible solutions are obtained with the given initial sheet shape and the robot team formation. We demonstrate the effectiveness, completeness, and efficiency of the FK method with simulation and experimental results.

A Novel Graph-based Motion Planner of Multi-Mobile Robot Systems with Formation and Obstacle Constraints

Multi-mobile robot systems show great advantages over one single robot in many applications. However, the robots are required to form desired task-specified formations, making feasible motions decrease significantly. Thus, it is challenging to determine whether the robots can pass through an obstructed environment under formation constraints, especially in an obstacle-rich environment. Furthermore, is there an optimal path for the robots? To deal with the two problems, a novel graphbased motion planner is proposed in this paper. A mapping between workspace and configuration space of multi-mobile robot systems is first built, where valid configurations can be acquired to satisfy both formation constraints and collision avoidance. Then, an undirected graph is generated by verifying connectivity between valid configurations. The breadth-first search method is employed to answer the question of whether there is a feasible path on the graph. Finally, an optimal path will be planned on the updated graph, considering the cost of path length and formation preference. Simulation results show that the planner can be applied to get optimal motions of robots under formation constraints in obstacle-rich environments. Additionally, different constraints are considered.

Hierarchical Motion Planning Framework for Cooperative Transportation of Multiple Mobile Manipulators

Multiple mobile manipulators show superiority in the tasks requiring mobility and dexterity compared with a single robot, especially when manipulating/transporting bulky objects. When the object and the manipulators are rigidly connected, closed-chain will form and the motion of the whole system will be restricted onto a lower-dimensional manifold. However, current research on multi-robot motion planning did not fully consider the formation of the whole system, the redundancy of the mobile manipulator and obstacles in the environment, which make the tasks challenging. Therefore, this paper proposes a hierarchical framework to efficiently solve the above challenges, where the centralized layer plans the object's motion offline and the decentralized layer independently explores the redundancy of each robot in real-time. In addition, closed-chain, obstacle-avoidance and the lower bound of the formation constraints are guaranteed in the centralized layer, which cannot be achieved simultaneously by other planners. Moreover, capability map, which represents the distribution of the formation constraint, is applied to speed up the two layers. Both simulation and experimental results show that the proposed framework outperforms the benchmark planners significantly. The system could bypass or cross obstacles in cluttered environments, and the framework can be applied to different numbers of heterogeneous mobile manipulators.

Obstacle Crossing by Multi-mobile Robots in Object Transportation with Deformable Sheet

Multi-robot transportation (MRT) is to transport the object to the destination by the cooperation of multiple robots. In the process of object transportation, obstacle avoidance is an indispensable feature. In traditional local planners, obstacles are usually considered insurmountable, so the robot team bypasses the obstacles as a whole. However, many obstacles can be crossed in real situation. Studying the obstacle crossing ability of robot team can improve the efficiency of MRT and increase the planning success rate in complex environment. Inspired by the patient transfer through bed sheet, this paper focuses on the object transportation by multi-mobile robots with deformable sheet. A new local planner with obstacle crossing capability is proposed, which consists of three parts: deformable sheet modeling, formation optimization and local path generation. It can successfully find an obstacle crossing path in complex scenarios where other planners fail. The effectiveness and the versatility of the planner is verified by a case study with three mobile robots in the experiment and a simulation with four robots.

A real-time global re-localization framework for 3D LiDAR SLAM

Simultaneous localization and mapping (SLAM) has been a hot research field in the past years. Against the backdrop of more affordable 3D LiDAR sensors, research on 3D LiDAR SLAM is becoming increasingly popular. Furthermore, the re-localization problem with a point cloud map is the foundation for other SLAM applications. In this paper, a template matching framework is proposed to re-localize a robot globally in a 3D LiDAR map. This presents two main challenges. First, most global descriptors for point cloud can only be used for place detection under a small local area. Therefore, in order to re-localize globally in the map, point clouds and descriptors(templates) are densely collected using a reconstructed mesh model at an offline stage by a physical simulation engine to expand the functional distance of point cloud descriptors. Second, the increased number of collected templates makes the matching stage too slow to meet the real-time requirement, for which a cascade matching method is presented for better efficiency. In the experiments, the proposed framework achieves 0.2-meter accuracy at about 10Hz matching speed using pure python implementation with 100k templates, which is effective and efficient for SLAM applications.