Nonholonomic Mobile Manipulators Research Articles

Whole Body Control of an Autonomous Mobile Manipulator Using Series Elastic Actuators

The flexible joint robot has superior attractive features because of its high mobility, high load ratiohigh torque fidelity, robustness for external disturbance, task adaptability, and safety. In this paper, an autonomous mobile manipulator driven by the designed series elastic actuators (SEAs) is developed. A complete dynamic model of nonholonomic mobile manipulator including a mobile platform and manipulator with joint flexibility operating simultaneously is proposed. An integrated whole body trajectory control framework is proposed for such robot to perform mobile manipulation tasks. Considering the nonholonomic and holonomic constraints in the mobile manipulation, the whole-body dynamics is formulated and reduced. To address the highly nonlinear of the dynamics and model uncertainty, a novel integral Lyapunov function (ILF)-based adaptive neural network control for task tracking under uncertainties of the flexible joint robot model is proposed. Compared with existing methods, the proposed method provides an alternative for controlling flexible joint robots. The feasibility of the proposed method is verified by the extensive trajectory tracking experiment results in our developed flexible joint manipulator.

Reactive mobile manipulation based on dynamic dual-trajectory tracking

The letter presents an online obstacle avoidance strategy for a nonholonomic mobile manipulator, including dual-trajectory generation for the mobile base(MB) and end effector(EE), as well as whole-body reactive obstacle avoidance. The feasible region of the manipulator’s workspace is generated by traversing the path of the MB, and the spatio-temporal trajectory of the EE is generated by quadratically constrained quadratic programming(QCQP) planning, satisfying the physical constraints and obstacle avoidance constraints. The reactive obstacle avoidance strategy is based on quadratic programming(QP), using singularity avoidance and EE trajectory tracking tasks as soft constraints with different priorities. Considering the nonholonomic constraints and motion differences of MB, we employ a dynamic tracking approach to avoid obstacles of MB. Additionally, an adaptive weight method is utilized to adjust the priority of MB path tracking task, effectively preventing joint velocity jitter during the insertion process. Our strategy has been proven to generate obstacle avoidance trajectories for EE in real-time and ensure safe joint velocities for MB and the manipulator through simulation and real-world scenarios.

Dynamic Optimization Fabrics for Motion Generation

Optimization fabrics are a geometric approach to real-time local motion generation, where motions are designed by the composition of several differential equations that exhibit a desired motion behavior. We generalize this framework to dynamic scenarios and nonholonomic robots and prove that fundamental properties can be conserved. We show that convergence to desired trajectories and avoidance of moving obstacles can be guaranteed using simple construction rules of the components. In addition, we present the first quantitative comparisons between optimization fabrics and model predictive control and show that optimization fabrics can generate similar trajectories with better scalability, and thus, much higher replanning frequency (up to 500 Hz with a 7 degrees of freedom robotic arm). Finally, we present empirical results on several robots, including a nonholonomic mobile manipulator with 10 degrees of freedom and avoidance of a moving human, supporting the theoretical findings.

Motion Planning of Mobile Manipulator for Navigation Including Door Traversal

Door traversal is an essential task for a mobile manipulator to achieve autonomous navigation in indoor environment. However, it remains challenging since it requires the coupled motion of the robot and door and is composed of several sub-problems such as approaching, opening, passing through, and closing the door. This paper proposes a planning framework that formulates these sub-problems as a single problem and computes a path for the mobile manipulator to navigate from the initial position, traverse through the door, and arrive at the target position. The proposed framework obtains the path of the whole-body configuration in two steps. First, the path for the pose of the mobile robot and the path for the door angle are computed by using the graph search algorithm. In graph search, an integer variable called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">area indicator</i> is introduced as an element of state, which indicates where the robot is located relative to the door. Especially, the area indicator expresses a process of door traversal. In the second step, the configuration of the manipulator is computed by the Inverse Kinematics (IK) solver from the path of the mobile robot and door angle. The proposed framework has a distinct advantage over the existing methods that manually determine several parameters such as which direction to approach the door and the angle of the door required for passage. The effectiveness of the proposed framework was validated through experiments with a nonholonomic mobile manipulator.

Whole-Body Control of an Autonomous Mobile Manipulator Using Model Predictive Control and Adaptive Fuzzy Technique

Whole-body control (WBC) has emerged as an important framework in manipulation for mobile manipulators. However, most existing WBC frameworks require known dynamics. Considering whole-body manipulation and optimization with unknown dynamics, this article presents the WBC of a nonholonomic mobile manipulator using model predictive control (MPC) and fuzzy logic system. First, by constructing a dynamics-based feedback linearized robotic multi-input-multi-output (MIMO) system, an MPC-based WBC strategy is proposed for mobile manipulator. Such a strategy can provide the optimal control inputs with the specified optimization index and constraints. Thereafter, a primal-dual neural network effectively addresses the constrained quadratic programming (QP) problem over a finite receding horizon brought by the MPC. Then, in order to convert the intermediate control signals into the optimal control torques that can be executed by actuators, an adaptive FLS is employed to approximate the unknown dynamics. The novel elements of the current design control approach refer to the dynamics-based feedback linearized robotic MIMO system and the combination of an MPC module with an adaptive fuzzy controller. Finally, the trajectory tracking experiments performed on a mobile dual-arm robot demonstrate the effectiveness of the proposed method.

Potential Field Control of a Redundant Nonholonomic Mobile Manipulator with Corridor-Constrained Base Motion

This work proposes a solution for redundant nonholonomic mobile manipulator control with corridor constraints on base motion. The proposed control strategy applies an artificial potential field for base navigation to achieve joint control with desired trajectory tracking of the end effector. The overall kinematic model is created by describing the nonholonomic mobile platform and the kinematics of the manipulator. The objective function used consists of a primary control task that optimizes the joint variables to achieve the desired pose or trajectory of the end effector and a secondary control task that optimizes the joint variables for the base to support the arm and stay within the corridor. As a last priority, an additional optimization is introduced to optimize the maneuverability index. The proposed baseline navigation has global convergence without local minima and is computationally efficient. This is achieved by an optimal grid-based search on a coarse discrete grid and a bilinear interpolation to obtain a continuous potential function and its gradient. The performance of the proposed control algorithm is illustrated by several simulations of a mobile manipulator model derived for a Pal Tiago mobile base and an Emiko Franka Panda robotic manipulator.

Cooperation and Coordination Transportation for Nonholonomic Mobile Manipulators: A Distributed Model Predictive Control Approach

This article addresses the problem of cooperation and coordination transportation for decoupling nonholonomic mobile manipulators (NMMs) in a workspace with obstacles. We propose a distributed model predictive control (MPC) approach for a team of NMMs to transport a target object while satisfying significant constraints and limitations, such as the feasible state and control input constraints, parameter synchronization constraints, and obstacles within the workspace. First, under the framework of the decoupling dynamics, an auxiliary dynamics model for task-space end-effectors and null-space mobile bases is obtained by the nonlinear feedback technique based on the Euler–Lagrange description of the NMMs. Using the modified virtual structure method, the cooperation and coordination transportation problem for NMMs is simplified as two independent synchronization tracking control problems for task-space end-effectors and null-space mobile bases. A distributed constrained optimization problem is established by taking the parameter synchronization and system constraints into the cost function. A general projection neural network (GPNN) approach is employed to solve the optimization problem and obtain the optimal control input. Moreover, a sufficient condition that guarantees the stability of the closed-loop system is further developed. Simulation results show that the proposed cooperation and coordination transportation strategy is feasible and effective.

Output feedback tracking control for nonholonomic wheeled mobile manipulator systems with full-output constraints

An output feedback trajectory tracking control scheme for nonholonomic mobile manipulator robot systems with full-output constraints is presented in this article. Different from the previous methods, the kinematic model of the system is transformed into a special chained form by a rotation-like matrix. A globally exponentially stable speed observer with generalized dynamic scaling function is constructed to obtain the velocity estimation of the system. The output feedback tracking constrained controller is successfully constructed by Barrier Lyapunov function, backstepping and the excellent observer, which considering the system dynamics and the full-output constraints is achieved with only measurable outputs.

Dynamics of Mobile Manipulators Using Dual Quaternion Algebra

Abstract This article presents two approaches to obtain the dynamical equations of mobile manipulators using dual quaternion algebra. The first one is based on a general recursive Newton–Euler formulation and uses twists and wrenches, which are propagated through high-level algebraic operations and works for any type of joints and arbitrary parameterizations. The second approach is based on Gauss’s Principle of Least Constraint (GPLC) and includes arbitrary equality constraints. In addition to showing the connections of GPLC with Gibbs–Appell and Kane’s equations, we use it to model a nonholonomic mobile manipulator. Our current formulations are more general than their counterparts in the state of the art, although GPLC is more computationally expensive, and simulation results show that they are as accurate as the classic recursive Newton–Euler algorithm.

Robust position/force control of nonholonomic mobile manipulator forconstrained motion on surface in task space

In this paper, a robust controller is developed for a mobile manipulator (MM) to track reference position/force trajectories. Nonholonomic and holonomic constraints are considered for the mobile platform and manipulator, respectively. Additionally, the control design considers the uncertainties in parameters of the dynamics of the mobile manipulator with a bounded time varying additive disturbance (unmodelled effects, external disturbances). A Lyapunov-based stability analysis is used to prove semiglobal uniform ultimate boundedness of the tracking error signals and the position/force of the system track to an arbitrarily small neighborhood of the reference trajectories. Numerical results for a mobile manipulator, which is formed from a differential drive mobile platform and 2 DOF Revolute-Revolute (RR) manipulator, show that the position of the end-effector and the applied force track the desired position and the force trajectories, respectively.

Adaptive Door Opening Control Algorithm of Bio-Inspired Mobile Manipulator Based on Synchronous Sensing.

At present, the research of robot door opening method is basically realized by identifying the door handle through the synchronous sensing system on the premise that the bio-inspired mobile manipulator is located in front of the door. An adaptive door opening strategy of a bio-inspired mobile manipulator based on a synchronous sensing system is proposed. Firstly, the random delay distribution in clock synchronization technology is analyzed in detail, and its distribution is verified on the experimental platform of adjacent nodes. Based on the Gaussian distribution of random delay, the relative frequency offset and relative phase offset of adjacent nodes are calculated. The clock synchronization of network cable sensor nodes is realized. Secondly, based on the information data of synchronous sensing system, this article realizes target detection and tracking based on depth network. In addition, based on the sliding mode control theory, the dynamic model of the nonholonomic bio-inspired mobile manipulator is applied. Finally, a robust adaptive sliding mode control method for nonlinear systems with input gain uncertainty and unmatched uncertainty is proposed by combining adaptive backstepping with sliding mode control. By adding sliding mode control in the last step of adaptive backstepping, the uncertainty of the system is compensated, and the system trajectory is maintained on the specified sliding mode manifold. The tracking control and stability control of the nonholonomic bio-inspired mobile manipulator are simulated. The experimental and simulation results show that the control method proposed in this article is effective and feasible.

Motion Planning of Nonholonomic Mobile Manipulators with Manipulability Maximization Considering Joints Physical Constraints and Self-Collision Avoidance

The motion of nonholonomic mobile manipulators (NMMs) is inherently constrained by joint limits, joint velocity limits, self-collisions and singularities. Most motion planning algorithms consider some of the aforementioned constraints, however, a unified framework to deal with all of them is lacking. This paper proposes a motion planning solution for the kinematic trajectory tracking of redundant NMMs that include all the constraints needed for practical implementation, which improves the manipulability of both the entire system and the manipulator to prevent singularities. Experiments using a 10-DOF NMM demonstrate the good performance of the proposed method for executing 6-DOF trajectories while satisfying all the constraints and simultaneously maximizing manipulability.

Robust adaptive tracking control for uncertain nonholonomic mobile manipulator

In the research put forth, a robust adaptive control method for a nonholonomic mobile manipulator robot, with unknown inertia parameters and disturbances, was proposed. First, the description of the robot’s dynamics model was developed. Thereafter, a novel adaptive sliding mode control was designed, to which all parameters describing involved uncertainties and disturbances were estimated by the adaptive update technique. The proposed control ensures a relatively good system tracking, with all errors converging to zero. Unlike conventional sliding mode controls, the suggested is able to achieve superb performance, without resulting in any chattering problems, along with an extremely fast system trajectories convergence time to equilibrium. The aforementioned characteristics were attainable upon using an innovative reaching law based on potential functions. Furthermore, the Lyapunov approach was used to design the control law and to conduct a global stability analysis. Finally, experimental results and comparative study collected via a 05-DoF mobile manipulator robot, to track a given trajectory, showing the superior efficiency of the proposed control law.

Whole-Body Control of an Autonomous Mobile Manipulator Using Series Elastic Actuators

The flexible-joint robot has superior attractive features because of its high mobility, high load ratio, high torque fidelity, robustness for external disturbance, task adaptability, and safety. In this article, an autonomous mobile manipulator driven by the designed series elastic actuators is developed. A complete dynamic model of a nonholonomic mobile manipulator including a mobile platform and a manipulator with joint flexibility operating simultaneously is proposed. An integrated whole-body trajectory control framework is proposed for such robot to perform mobile manipulation tasks. Considering the nonholonomic and holonomic constraints in the mobile manipulation, the whole-body dynamics is formulated and reduced. To address the highly nonlinear of the dynamics and model uncertainty, a novel integral Lyapunov function based adaptive neural network control for task tracking under uncertainties of the flexible-joint robot model is proposed. Compared with existing methods, the proposed method provides an alternative for controlling flexible-joint robots. The feasibility of the proposed method is verified by the extensive trajectory tracking experiment results in our developed flexible joint manipulator.

Handling of large and heavy objects using a single mobile manipulator in combination with a roller board

This paper presents a method for autonomous loading, transportation, and unloading of large objects using a nonholonomic mobile manipulator. Here, the size of the transported object is considerably larger than the size of the mobile platform, which is made possible through the use of a roller board. In this way, the mobile manipulator can handle objects that exceed the manipulator’s payload. The robot can load and unload the object onto its platform using the differential kinematics of the system for a null space motion to maintain the object’s position in space. In order to localise the object, we apply 3D-perception using a depth-camera. While transporting the object to its destination, the robot is considered a tractor-trailer-wheeled system and can navigate using SLAM. Kinematic modelling and practical evaluation prove that the system can potentially take over arduous transportation tasks.

An intelligent optimal control approach for motion/force control of constrained non-holonomic mobile manipulators

This paper presents motion/force control problem of constrained non-holonomic mobile manipulators in the presence of uncertainties and external disturbances. The paper proposes an intelligent contr...

An optimal control approach for hybrid motion/force control of coordinated multiple nonholonomic mobile manipulators using neural network

An optimal control approach for hybrid motion/force control of coordinated multiple nonholonomic mobile manipulators using neural network

Robust Position/Force Control of Nonholonomic Mobile Manipulator for Constrained Motion on Surface in Task Space

Robust Position/Force Control of Nonholonomic Mobile Manipulator for Constrained Motion on Surface in Task Space

An optimal control approach for hybrid motion/force control of coordinated multiple nonholonomic mobile manipulators using neural network

An optimal control approach for hybrid motion/force control of coordinated multiple nonholonomic mobile manipulators using neural network

An intelligent optimal control approach for motion/force control of constrained non-holonomic mobile manipulators

An intelligent optimal control approach for motion/force control of constrained non-holonomic mobile manipulators