Motion planning and control of a swimming machine

This article presents an original motion control strategy for robot manipulators based on the coupling of the inverse dynamics method with the so-called second-order sliding mode control approach. Using this method, in principle, all the coupling non-linearities in the dynamical model of the manipulator are compensated, transforming the multi-input non-linear system into a linear and decoupled one. Actually, since the inverse dynamics relies on an identified model, some residual uncertain terms remain and perturb the linear and decoupled system. This motivates the use of a robust control design approach to complete the control scheme. In this article the sliding mode control methodology is adopted. Sliding mode control has many appreciable features, such as design simplicity and robustness versus a wide class of uncertainties and disturbances. Yet conventional sliding mode control seems inappropriate to be applied in robotics since it can generate the so-called chattering effect, which can be destructive for the controlled robot. In this article, this problem is suitably circumvented by designing a second-order sliding mode controller capable of generating a continuous control law making the proposed sliding mode controller actually applicable to industrial robots. To build the inverse dynamics part of the proposed controller, a suitable dynamical model of the system has been formulated, and its parameters have been accurately identified relying on a practical MIMO identification procedure recently devised. The proposed inverse dynamics-based second-order sliding mode controller has been experimentally tested on a COMAU SMART3-S2 industrial manipulator, demonstrating the tracking properties and the good performances of the controlled system.

In this paper, we present a modular framework for solving a motion planning problem among a group of robots. The proposed framework utilizes a finite set of low-level motion primitives to generate motions in a gridded workspace. The constraints on allowable sequences of motion primitives are formalized through a maneuver automaton . At the high level, a control policy determines which motion primitive is executed in each box of the gridded workspace. We state general conditions on motion primitives to obtain provably correct behavior so that a library of safe-by-design motion primitives can be designed. The overall framework yields a highly robust design by utilizing feedback strategies at both the low and high levels. We provide specific designs for motion primitives and control policies suitable for multirobot motion planning; the modularity of our approach enables one to independently customize the designs of each of these components. Our approach is experimentally validated on a group of quadrocopters.

Real-time motion planning and control of multi-joint robot manipulators are studied using neural networks. The proposed neural network approach consists of two modules: one for real-time collision-free motion planning, the other for real-time fine control of the robot manipulators. The motion planning module is a biologically inspired, parallel connected, topologically organised neural network, where each neuron is characterised by a shunting equation, whose state space is the robot configuration joint space. This module is capable of planning a real-time optimal path for robot manipulators through the dynamic activity landscape of the neural network. The motion control module involves of a feedforward neural network together with a traditional PD feedback loop. It is capable of achieving real-time fine motion control of robot manipulators under significant uncertainties and without any prior knowledge of the robot dynamics. This module can quickly compensate sudden changes in the robot dynamics. The real-time fine control of robot manipulators is achieved through the online learning of the neural network. Both the motion planning and motion control modules are computationally efficient, and their global stabilities are proved using Lyapunov stability theory.

Automated manipulation of nanowires and nanotubes would enable the scalable manufacturing of nanodevices for a variety of applications, including nanoelectronics and biological applications. In this paper, we present an electric-field-based method for motion control, planning, and manipulation of nanowires in liquid suspension with a simple, generic set of electrodes. We first present a dynamic model and a vision-based motion control of the nanowire motion in dilute suspension with a set of $N\times N$ controllable electrodes. Since the motion planning of a nanowire from one position to the target location is NP-hard, two heuristic algorithms are presented to generate near-optimal motion trajectories. We compare the heuristic motion planning algorithms with other existing algorithms such as the rapidly exploring random tree (RRT) and $A^{\ast}$ algorithms. The comparisons show that the proposed heuristic algorithms obtain near-optimal minimum time trajectories. Finally, we demonstrate a single, integrated process to position, orient, and deposit multiple nanowires onto the substrate. Extensive experimental and numerical results are presented to confirm the motion control and planning algorithms.

<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints.</div><div class="htmlview paragraph"><b>Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and Control for Autonomous Vehicles</b> presents a hybrid framework that combines data-driven insights with the robustness of physical models. It identifies key challenges in fusing these disparate methods and outlines potential solutions in developing robust fusion strategies, establishing generalized mixed dynamics models, and designing multi-objective robust control systems. In addition, the report explores future research directions to enhance learning efficiency, improve adaptability to rare but critical scenarios, and ultimately pave the way for secure, efficient, and human-like autonomous driving systems.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

For autonomous driving, motion planning and motion control are two significant modules. In this paper, an iterative trajectory optimization (ITO) method is proposed to improve the motion controller’s tracking performance and increase the physical and operational feasibility of the motion planning trajectory. Based on the proposed scheme, a feedback connection is built from the motion controller to the motion planner. Different from traditional motion controllers, the motion controller in the proposed scheme is divided into two sub-modules, iterative motion simulator, and motion control operator. In the iterative motion simulator, the vehicle trajectory response is simulated while updating a trajectory offset iteratively. This offset will correct the vehicle response closer to the reference trajectory. After the iterative trajectory adjustment finishes, the simulated tracking error and trajectory offset are sent back to motion planner. The motion planner will first evaluate the simulated trajectory in terms of the response effectiveness, and then send the trajectory with the offset to the motion control operator to calculate the vehicle control maneuver. Comparing with tradition motion planning schemes, the proposed ITO approach can guarantee the trajectory’s physical and operational feasibility, make the motion controller have better tracking performance, and have a predictive evaluation on the vehicle response. The simulation results demonstrate the effectiveness of the proposed method.

This paper deals with motion planning and control for mobile robots. The various components of an integrated architecture for the mobile robot Hilare pulling a trailer are presented. The nonholonomic path planner is based on an original steering method accounting for the small-time controllability of the system. Then the path is transformed into a trajectory by including the dynamical constraints of the system (bounded velocity and bounded acceleration). Finally, the motion control is addressed. Due to the geometric transformation for a virtual robot, we show how to reduce the problem to a classical approach of trajectory tracking for a mobile robot moving forward only. The experimental results presented include two types of robot-trailer connection systems.

The chained form system is a controllable nonholonomic system, wherein the motion planning and control had been often researched. Therefore, based on the chained form transformation, the motion planning and control of nonholonomic system can be researched by the motion planning and control of chained form system. According to this thought, motion planning and control of nonholonomic multi-joint manipulator were researched in this paper. A method of nonholonomic motion planning and control was proposed by the chained form transformation, and validity of the method was proved by computer simulation

The development of a practical motion planning and control algorithm for under-actuated robots in an unknown disturbance is a very important issue in robotics research. In the case of under actuated underwater vehicles, developing such an algorithm has been particularly problematic for several reasons. First, not only the kinematical characteristics of the motion but also the dynamical characteristics of the underwater vehicle must be considered in the motion planning calculation. Second, it is very difficult to ascertain the exact distribution of the velocity of non-uniform sea flow around obstacles on the seabed before the mission. Third, the effect of the sea flow on the motion of an underwater vehicle is very large because the speed of sea flow is very high compared with the vehicle's. This paper proposes a new method based on the application of reinforcement learning to solve these problems. Reinforcement learning based on the Markov decision process (MDP) is known to be suitable for acquiring motion control algorithms for robots acting in a stochastic environment with disturbance. However, to apply reinforcement learning method, the robot's motion must be suitably digitized and the learning environment must be equal to the robot's mission environment. This paper introduces a motion digitizing method based on an artificial neuron model and a method for making up for the difference between learning and mission environments. The performance of the proposed algorithm is examined by the dynamical simulation of an under-actuated underwater vehicle cruising in an environment with an obstacle and an unknown non-uniform flow simulated by potential flow.

Motion primitives play an important role in motion planning for autonomous vehicles, as they effectively address the sampling challenges inherent in nonholonomic motion planning. Employing motion primitives (MPs) is a widely accepted approach in nonholonomic motion planning based on sampling. This study specifically addresses the problem of learning from human-driving data to create human-like trajectories from predefined start-to-end states, which then serve as MP within the sampling-based nonholonomic motion planning framework. In this paper, we propose a deep learning-based method for generating MP that capture human-driving trajectory data features. By processing human-driving trajectory data, we create a Motion Primitive dataset that uniformly covers typical urban driving scenarios. Based on this dataset, a vehicle model long short-term memory neural network model is constructed to learn the features of the human-driving trajectory data. Finally, a framework for the generation of MP for practical applications is given based on this neural network. Our experiments, which focus on the dataset, the MMP generation network, and the generation process, demonstrate that our method significantly improves the training efficacy of the MP generation network. Additionally, the MP generated by our method exhibit higher accuracy compared to traditional methods.

This paper discusses the modeling and control of a spatial mobile manipulator which consists of a robotic manipulator mounted upon a wheeled mobile platform. By neglecting slip of the wheeled platform’s tires, nonholonomic constraints are introduced into the equations of motion which complicates the control problem. The dynamic equations of the mobile manipulator are derived using the Lagrange-d’Alembert formulation. In this formulation, the equations of motion are projected onto the subspace of allowable motions. Then, the solvability of tracking problems for the mobile manipulator is investigated by using input-output feedback linearization. The feasibility of the control approach is demonstrated through computer simulation.

Surface cracks exist in many civil infrastructures such as road and bridge deck surfaces, parking lots, and building surfaces, etc. To prevent the crack growth and further deterioration, it is necessary to fill these cracks with appropriate materials on time. We present a robotic crack filling system that can effectively and efficiently deliver fluidic repairing materials to fill surface cracks. Motion planning and cracking filling control are the two main tasks presented in this paper. We present a graph-based crack coverage (GCC) algorithm to solve motion planning problem. The filling motion control is captured by a model predictive control (MPC) scheme given the mobile platform trajectory. We also develop a crack-filling robotic prototype. Simulation and experimental results are presented to demonstrate the system design and performance evaluation.

Wheeled motion kinematics and control of a hybrid mobility CENTAURO robot

In this paper, a sliding mode control (SMC) design approach using embedding process, measure theory and optimal control techniques is proposed. In this method, a state trajectory starting from a given initial point reaches a definite point on a surface, the so-called sliding surface, in a finite time, and then tends to the origin along the sliding surface (SS). A measure theory approach with embedding process is used to solve such a problem in two phases. In the first phase, using an appropriate measure, an embedding is constructed to solve an appropriate optimal control problem such that the system trajectory reaches the SS in a finite time, and in the second phase, using measure theory, a control is usually designed such that the system trajectory tends to the origin along the SS. It is well-known that the main disadvantage of the use of the traditional SMCs is the chattering phenomenon. The proposed SMC is piecewise continuous and chattering free. Also, the sliding mode reaching point is selected prior to the design of the SMC and it is not necessary to verify the sliding mode reaching condition as it is guaranteed during the design process. In addition, a numerical example is presented to illustrate the effectiveness of the proposed method.

A motion planner is developed for guiding a small aerobatic fixed-wing unmanned aerial vehicle to a desired goal region in a highly constrained, three-dimensional, known environment with static obstacles. The planner is based on the Rapidly-Exploring Random Trees (RRT) algorithm, and pieces together feasible trajectories from a library of motion primitives. Among other more conventional motion primitives, the library includes three extreme maneuvers: a cruise-to-hover transition, a hover-to-cruise transition, and an aggressive turn-around. The algorithm is efficient; it can be run in real-time to rapidly generate a plan starting from the aircraft's configuration at run-time. The motion planner is closely coupled to a feedback controller. Simulations using an aircraft dynamics model demonstrate the effectiveness of the system to guide and control the aircraft to a desired goal region. Preliminary flight test results are also presented.