Reconfigurable multi-robot system kinematic modeling and motion planning

Mobile manipulators have attracted a lot of interest recently. This paper discusses the development of mobile manipulators in recent years in the aspect of motion planning and coordinated control between mobile platform and manipulator. An intelligent controller based on RBF neural network is introduced for the coordinating control of the mobile manipulator, which mimics human behavior. Simulation results was given out and the method has been applied on the Hebut-II mobile robot platform. Experimental results verified the validity of the method.

This paper introduces a novel and general real-time adaptive motion planning (RAMP) approach suitable for planning trajectories of high-DOF or redundant robots, such as mobile manipulators, in dynamic environments with moving obstacles of unknown trajectories. The RAMP approach enables simultaneous path and trajectory planning and simultaneous planning and execution of motion in real time. It facilitates real-time optimization of trajectories under various optimization criteria, such as minimizing energy and time and maximizing manipulability. It also accommodates partially specified task goals of robots easily. The approach exploits redundancy in redundant robots (such as locomotion versus manipulation in a mobile manipulator) through loose coupling of robot configuration variables to best achieve obstacle avoidance and optimization objectives. The RAMP approach has been implemented and tested in simulation over a diverse set of task environments, including environments with multiple mobile manipulators. The results (and also the accompanying video) show that the RAMP planner, with its high efficiency and flexibility, not only handles a single mobile manipulator well in dynamic environments with various obstacles of unknown motions in addition to static obstacles, but can also readily and effectively plan motions for each mobile manipulator in an environment shared by multiple mobile manipulators and other moving obstacles.

PurposeThe purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility.Design/methodology/approachA convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling.FindingsExperimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions.Originality/valueThe algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

As the demand for service robots increases, a mobile manipulator robot which can perform various tasks in a dynamic environment attracts great attention. There are some controllers that control mobile platform and manipulator arm simultaneously for efficient performance, but most of them are difficult to apply universally since they are based on only one mobile manipulator model. This lack of versatility can be a big problem because most mobile manipulator robots are made by connecting a mobile platform and manipulator from different companies. To overcome this problem, this paper proposes a simultaneous controller which can be applied not only to one model but also to various types of mobile manipulator robots. The proposed controller has three main characteristics, which are as follows: (1) establishing a pose that motion planning can be carried out in any position, avoiding obstacles and stopping in a stable manner at the target coordinates, (2) preventing the robot from collision with surrounding obstacles while driving, (3) defining a safety area where the manipulator does not hit the obstacles while driving and executing the manipulation accordingly. Our controller is fully compatible with Robot Operating System (ROS) and has been used successfully with three different types of mobile manipulator robots. In addition, we conduct motion planning experiments on five targets, each in two simulation worlds, and two motion planning scenarios using real robots in real-world environments. The result shows a significant improvement in time compared to existing control methods in various types of mobile manipulator and demonstrates that the controller works successfully in the real environment. The proposed controller is available on GitHub.

Mobile manipulator combines the mobility of mobile platform and the dexterity of manipulator, which has the great application potential in industry scenarios. In practical applications, efficiently planning a path in a given environment is the main objective, but the high degrees of redundancy, which is caused by the coupling of platform and manipulator, will make the planning problem more challenging. Therefore, the motion planning based on the RRT-GoalBias algorithm and path smoothing is proposed in this paper, where the kinematics of mobile manipulator is modeled, and the constraints of configuration and obstacle avoidance are analyzed. Subsequently, the RRT-GoalBias algorithm is applied and modified to generate an initial path for mobile manipulator in a given site, and a novel post-processing algorithm is proposed to smooth and optimize the initial path. Comparative Simulations are carried out with MATLAB and V-REP to verify the effectiveness of the proposed path planning algorithm for mobile manipulator under the given environment.

At present, mobile robotic manipulators have been greatly developed. However, these further promotions are limited by a low load capacity and short operation time. The above problems can be improved by using a hydraulic drive mode and increasing the system energy efficiency. In this paper, a novel energy-efficient wobble plate hydraulic joint is presented, which has the characteristics of having a small size, lightweight, large load capacity, and high energy efficiency. Based on the efficiency analysis in traditional robotic manipulators, this paper presents a novel hydraulic joint with a multi-chamber drive structure. Kinematics model and dynamics model are both established for the analysis of the mechanical characteristics, and the functional relationship between the input and output is depicted by numerical simulation. Based on the structural characteristics and control principle, the load matching controller is designed and specific control processes are formulated. Combined with a strategy of load matching, the servo control system is established and the energy-saving effect is verified by simulation. The result shows that the wobble plate hydraulic joint can change connections between a high-pressure circuit and different working chambers, which realizes the match between the output torque and load torque. With the load matching controller, the energy consumption of the wobble plate joint is greatly reduced, which contributes to a considerably improved energy efficiency. The research in this paper not only lays a theoretical foundation for the development of a wobble plate hydraulic joint, but also provides guidance for the improvement of the hydraulic system energy efficiency in mobile robotic manipulators.

This paper presents cooperation and null-space control for networked mobile manipulators with high degrees of freedom (DOFs). First, kinematic model and Euler-Lagrange dynamic model of the mobile manipulator, which has an articulated robot arm mounted on a mobile base with omni-directional wheels, have been presented. Then, the dynamic decoupling has been considered so that the task-space and the null-space can be controlled separately to accomplish different missions. The motion of the end-effector is controlled in the task-space, and the force control is implemented to make sure the cooperation of the mobile manipulators, as well as the transportation tasks. Also, the null-space control for the manipulator has been combined into the decoupling control. For the mobile base, it is controlled in the null-space to track the velocity of the end-effector, avoid other agents, avoid the obstacles, and move in a defined range based on the length of the manipulator without affecting the main task. Numerical simulations have been addressed to demonstrate the proposed methods.

Due to the advantage of combining mobility, manipulability and high efficiency, the nonholonomic wheeled mobile manipulators (NWMM) have been widely applied in numerous applications. However, the combination of manipulator and mobile platform brings new challenges in force exertion capacity, location precision and the redundancy problem of inverse kinematics. This paper proposes a method to improve the force exertion capability while executing tasks for which the mobile manipulator has kinematic redundancy. Redundancy resolution is formulated as optimal problem subject to carefully designed constraints. The entire framework is implemented under the hybrid position and admittance control (HPAC) for end-effector. Experiments on a two-wheeled nonholonomic mobile manipulator verified the feasibility of the proposed control strategy. The advantage of the force exertion enhancement (FEE) is also verified through comparing the joint efforts.

Mobile manipulators are being deployed for transporting parts between machines and work stations in warehouses and shop floors. To increase the efficiency of operations, these mobile manipulators are required to complete the tasks as fast as possible. Picking up parts with the manipulator while the mobile base is moving decreases the time required to complete the transportation task and increases the efficiency of operations. However, motions of the manipulator on a moving platform can be risky, and hence, it is desired that the manipulator starts and ends its motions as close as possible to the part being picked up. In this article, we present a bidirectional sampling-based scheme for generating such manipulator trajectories for a given mobile base trajectory for pickup and transportation. Our approach implicitly determines the location of the mobile base where the manipulator motion starts and ends as well as where grasping happens. It also determines which grasping pose to use for picking up the part. Furthermore, we have presented the techniques to reduce the manipulator motion time (span time) and the computation time. Our approach enables us to reduce span time on average by 35% with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16\times $ </tex-math></inline-formula> reduction in the computation time compared to the RRT-based baseline methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Transportation of objects is a crucial application in industrial and warehouse environments. Conveyor belts and AGVs are typically used for such applications as they provide an efficient mode of transportation of a large number of objects. However, they may not provide the flexibility which mobile manipulators bring in for small-scale operations. The method presented in this article provides a way to increase the efficiency of operation for mobile manipulators for transportation tasks. We attempt to reduce the risks of the manipulator colliding with expensive equipment and moving obstacles by making sure that the manipulator moves only when needed for picking up objects. Moreover, the method can be used to pick up and transport a variety of parts in different ways using a two-fingered gripper. The method can also easily incorporate different types of grippers and mobile platforms.

Task-constrained motion planning (TCMP) is involved in many practical applications, such as opening the door, opening the drawer, twisting the screw, and so on. Because of the trend of man-machine collaboration and the existence of dynamic in environments, planning the collision-free motions for task-constrained manipulation is a significative problem. This paper explores the TCMP issue for mobile manipulation, which uses a mobile base to enhance the working range and flexibility but simultaneously makes the problem harder than that of single manipulation because of the additional degrees of freedom (dofs). We propose an optimization-based method to plan the obstacle avoidance motion in real-time. First, the global robotic Jacobian matrix that combines the omnidirectional base and the robotic arm is derived. Second, model predictive control is used to plan the control rule in order to maximize the closest distance between the obstacles and the mobile manipulator and minimize the velocities of null space at the same time. We have deduced the model with four differential equations that represent the law of the distance over time. Third, the distances are calculated and sent to the model to calculate the velocity of each joint of the arm and the base using ACADO that is an open-source toolkit. Using the velocities, the mobile manipulator can move away from the approaching people while still fixing the end of the arm to manipulate tool at the same time. Our method is verified on the mobile manipulator that consists of four Mecanum wheels, a base, a UR10 arm, and four kinect cameras in Gazebo simulation with using the ROS operation.

As a substitute for humans, the mobile manipulator has become increasingly vital for on-site rescues at Nuclear Power Plants (NPPs) in recent years. The high energy efficiency of the mobile manipulator when executing specific rescue tasks is of great importance for the mobile manipulator. This paper focuses on the energy consumption of a robot executing the door-opening task, in a scenario mimicking an NPP rescue. We present an energy consumption optimization scheme to determine the optimal base position and joint motion of the manipulator. We developed a two-step procedure to solve the optimization problem, taking the quadric terms of the joint torques as the objective function. Firstly, the rotational motion of the door is parameterized by using piecewise fifth-order polynomials, and the parameters of the polynomials are optimized by minimizing the joint torques at the specified base position using the Quasi-Newton method. Second, the global optimal movement of the manipulator for executing the door-opening task is acquired by means of searching a grid for feasible base positions. Comprehensive door-opening experiments using a mobile manipulator platform were conducted. The effectiveness of the proposed method has been demonstrated by the results of physical experiments.

In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches.

This paper is a summary of an effort to develop a powerful motion planning algorithm for the mobile manipulator. The mobile manipulator is expected to work in partially defined or unstructured environments. In our global/local approach to path planning, joint trajectories are generated for a desired Cartesian space path, designed by the global path planner. For local path planner, inverse kinematics for a redundant system is used. Obstacle avoidance and joint displacement limits for the manipulator links are considered in the motion planner. In an event of failure to obtain feasible trajectories, the task can not be accomplished. In the case of the joint constraint violation, use of Jacobian matrix element as gradient is proposed. At the point of failure, a derivation in the Cartesian space path is obtained and the re-planner gives a new path that would achieve the goal position. To calculate the deviation, a non-linear optimization problem is formulated and solved by standard sequential quadratic programming (SQP) method.

In this paper, the problem of retrieving a target object from a cluttered environment through a mobile manipulator is considered. The task is solved by combining Task and Motion Planning; in detail, at a higher level, the task planner is in charge of planning the sequence of objects to relocate while, at a lower level, the motion planner is in charge of planning the robot movements taking into consideration robot and environment constraints. In particular, the latter provides feedback to the former about the feasibility of object sequences; this information is exploited to train a Reinforcement Learning agent that, according to an objects-weight based metrics, builds a dynamic decision-tree where each node represents a sequence of relocated objects, and edge values are weights updated via Q-learning-inspired algorithm. Three learning strategies differing in how the tree is explored are analysed. Moreover, each exploration approach is performed using two different tree-search methods: the Breadth first and Depth first techniques. Finally, the proposed learning strategies are numerically validated and compared in three scenarios of growing-complexity.

Mobile manipulators have brought a new level of flexibility to traditional automation tasks such as tabletop manipulation, but are not yet capable of the same speed and reliability as industrial automation. We present approaches to 3D perception and manipulator motion planning that enable a general purpose robotic platform to recognize and manipulate a variety of objects at a rate of one pick-and-place operation every 6.7 s, and work with a conveyor belt carrying objects at a speed of 33cm/s.