Control Problem Of Mobile Robots Research Articles

Sequential control barrier functions for mobile robots with dynamic temporal logic specifications

We address a motion planning and control problem for mobile robots to satisfy rich, time-varying tasks expressed as Signal Temporal Logic (STL) specifications. The specifications may include tasks with nested temporal operators or time-conflicting requirements (e.g., achieving periodic tasks or tasks defined within the same time interval). Moreover, the tasks can be defined in locations changing with time (i.e., dynamic targets), and their future motions are not known a priori. This unpredictability requires an online control approach which motivates us to investigate the use of control barrier functions (CBFs). The proposed CBFs take into account the actuation limits of the robots and a feasible sequence of STL tasks. They define time-varying feasible sets of states the system must always stay inside. We show the feasible sequence generation process that even includes the decomposition of periodic tasks and alternative scenarios due to disjunction operators. The sequence is used to define CBFs, ensuring STL satisfaction. We also show some theoretical results on the correctness of the proposed method. We illustrate the benefits of the proposed method and analyze its performance via simulations and experiments with aerial robots.

Distributed Disturbance-and-Leader Estimation for Controlling Networks of Nonholonomic Mobile Robots

This work studies the formation control problem for mobile robots. The distinguishing feature of this work is considering the leader and the followers dynamics to be non-ideal, i.e. subject to disturbances/unmodelled terms. Distributed joint disturbance-and-leader estimators are designed to solve the problem, allowing to reconstruct the leader’s signals in a distributed way. The estimates of the leader and the follower disturbances provided by the estimators are embedded in the control law to reject such disturbances and achieve the formation asymptotically. Lyapunov technique is employed to analyze stability of the overall distributed formation control algorithm. Simulations are conducted to illustrate the theoretical results.

Continuous Safety Control of Mobile Robots in Cluttered Environments

This letter studies the safety control problem for mobile robots working in cluttered environments. A compact set is employed to represent the obstacles, and a direction-distance function is used to describe the obstacle-measurement model. The major contribution is a nontrivial modification of the quadratic programming (QP) approach for continuous safety control of integrator-modeled mobile robots. In particular, a refinement of the Moreau-Yosida method is proposed to regularize the measurement model while retaining feasibility and safety. The second contribution is the development of a new feasible set shaping technique with a positive basis for a QP-based continuous safety controller. Physical experiments are employed to verify the proposed method.

Flocking Control of Mobile Robots with Obstacle Avoidance Based on Simulated Annealing Algorithm

Flocking control problem of mobile robots under environment with unknown obstacles is addressed in this paper. Based on the simulated annealing algorithm, a flocking behaviour for mobile robots is achieved which converges to alignment while avoiding obstacles. Potential functions are designed to evaluate the positional relationship between robots and obstacles. Unlike the existing analytical method, simulated annealing algorithm is utilized to search the quasi-optimal position of robots in order to reduce the potential functions. Motion control law is designed to drive the robot move to the desired position at each sampling period. Experiments are implemented, and the results illustrate the effectiveness of the proposed flocking control method.

Angle-Constrained Formation Control for Circular Mobile Robots

In this letter, we investigate the formation control problem of mobile robots moving in the plane where, instead of assuming robots to be simple points, each robot is assumed to have the form of a disk with equal radius. Based on interior angle measurements of the neighboring robots' disk, which can be obtained from low-cost vision sensors, we propose a gradient-based distributed control law and show the exponential convergence property of the associated error system. By construction, the proposed control law has the appealing property of ensuring collision avoidance between neighboring robots. We also present simulation results for {a team} of four circular mobile robots forming a rectangular shape.

Computational Modeling of Emotion-Motivated Decisions for Continuous Control of Mobile Robots

Immediate rewards are usually very sparse in the real world, which brings a great challenge to plain learning methods. Inspired by the fact that emotional reactions are incorporated into the computation of subjective value during decision-making in humans, an emotion-motivated decision-making framework is proposed in this article. Specifically, we first build a brain-inspired computational model of amygdala–hippocampus interaction to generate emotional reactions. The intrinsic emotion derives from the external reward and episodic memory and represents three psychological states: 1) valence; 2) novelty; and 3) motivational relevance. Then, a model-based (MB) decision-making approach with emotional intrinsic rewards is proposed to solve the continuous control problem of mobile robots. This method can execute online MB control with the constraint of the model-free policy and global value function, which is conducive to getting a better solution with a faster policy search. The simulation results demonstrate that the proposed approach has higher learning efficiency and maintains a higher level of exploration, especially, in some very sparse-reward environments.

Calibration-Free Image-Based Trajectory Tracking Control of Mobile Robots With an Overhead Camera

To make the controller implementation easier and to enhance the system robustness and control performance in the presence of the camera parameter uncertainties, it is very desired to develop vision-based control approaches without any offline or online camera calibration. In this article, we propose a new calibration-free image-based trajectory tracking control scheme for nonholonomic mobile robots with a truly uncalibrated fixed camera. By developing a novel camera-parameter-independent kinematic model, both offline and online camera calibration can be avoided in the proposed scheme, and any knowledge of the camera is not needed in the controller design. The proposed trajectory tracking control scheme can guarantee exponential convergence of the image position and velocity tracking errors. To illustrate the performance of the proposed scheme, experimental results are provided in this article. Note to Practitioners —This article was motivated by the vision-based motion control problem of mobile robots in uncalibrated environments. Existing vision-based motion control approaches for nonholonomic mobile robots generally depend on offline precise/coarse or online numerical/adaptive calibration of the camera intrinsic and extrinsic parameters and require precise or coarse knowledge of the camera in their implementation. This article presents a novel calibration-free image-based trajectory tracking control scheme, which can be implemented easily in real environments without any offline or online calibration of the camera parameters and can be used to efficiently control the motion of nonholonomic mobile robots with an arbitrarily placed and truly unknown overhead camera. Experimental results show that the proposed scheme can achieve satisfactory trajectory tracking control performance despite the lack of any knowledge about the camera intrinsic and extrinsic parameters and the presence of unknown camera lens distortions, and hence, can provide a simple but efficient solution to the vision-based motion control problem of nonholonomic mobile robots.

Model Predictive Tracking Control of Nonholonomic Mobile Robots With Coupled Input Constraints and Unknown Dynamics

This paper addresses a trajectory-tracking control problem for mobile robots by combining tube-based model predictive control (MPC) in handling kinematic constraints and adaptive control in handling dynamic constraints. In order to handle kinematic constraints, the tube-based MPC scheme is introduced, which includes the state feedback controller to suppress the external disturbance in the velocity level. The tube-based MPC is transformed to a constrained quadratic programming (QP) problem, and then the QP problem can be efficiently solved by a primal-dual neural network over a finite receding horizon so as to obtain the optimal control velocity. Besides, an adaptive controller employing the neural network technology is proposed to acquire the approximation of the uncertain robotic dynamics. Moreover, an auxiliary control is developed in order to deal with actuator saturation, and a disturbance observer is designed to reject the external disturbance online in the dynamic level. Subsequently, through Lyapunov function synthesis, the stability of the closed-loop system have been guaranteed. Finally, in order to verify the effectiveness, the experimental studies are carried out using an actual mobile robot.

Big Bang–Big Crunch optimization based interval type-2 fuzzy PID cascade controller design strategy

Fuzzy logic control is a recognized approach for handling the faced uncertainties within control applications. However, type-1 fuzzy controllers using crisp type-1 fuzzy sets might not be able to fully handle the high levels of uncertainties and nonlinear dynamics associated with real world control applications. On the other hand, interval type-2 fuzzy controllers using Interval Type-2 Fuzzy Sets (IT2-FSs) might be able to handle such uncertainties to produce a better control performance. However, the systematic design of interval type-2 fuzzy controllers is still a challenging problem due to the difficulty in determining the parameters of the IT2-FSs. In this paper, we will present the novel application of Big Bang–Big Crunch optimization (BB–BC) approach to optimize the antecedent membership parameters of Interval Type-2 Fuzzy PID (IT2-FPID) controllers in a cascade control structure. Since the IT2-FPID control structure involves more design parameters compared to its type-1 counterpart, it is beneficial to employ the BB–BC method which has a low computational cost and a high convergence speed. The presented BB–BC based optimized IT2-FPID cascade structure will be compared with its Type-1 Fuzzy PID (T1-FPID) and conventional PID controller counterparts which were also optimized with the BB–BC optimization. In addition, the proposed IT2-FPID structure will be compared with a self-tuning T1-FPID control structure. We will then present the novel application of the cascade control architecture to solve the path tracking control problem of mobile robots which inherits large amounts of uncertainties caused by the internal dynamics and/or feedback sensors of the controlled system. Several experiments were performed in simulation and in real world using the PIONEER 3-DX mobile robot which will act as a platform to evaluate the proposed control systems in this paper. The results illustrated that the IT2-FPID structure enhanced significantly the control performance even in the presence of uncertainties and disturbances when compared to the PID, T1-FPID and self-tuning T1-FPID structures. Moreover, it has been shown that the reason for the superior control performance of the IT2-FPID under high levels of uncertainty and noise is not merely for its use of extra parameters, but rather its different way of dealing with the uncertainties and noise present in real world environments by comparing with a self-tuning T1-FPID structure.

Robust formation tracking control of mobile robots via one-to-one time-varying communication

We solve the formation tracking control problem for mobile robots via linear control, under the assumption that each agent communicates only with one ‘leader’ robot and with one follower, hence forming a spanning-tree topology. We assume that the communication may be interrupted on intervals of time. As in the classical tracking control problem for non-holonomic systems, the swarm is driven by a fictitious robot which moves about freely and which is a leader to one robot only. Our control approach is decentralised and the control laws are linear with time-varying gains; in particular, this accounts for the case when position measurements may be lost over intervals of time. For both velocity-controlled and force-controlled systems, we establish uniform global exponential stability, hence consensus formation tracking, for the error system under a condition of persistency of excitation on the reference angular velocity of the virtual leader and on the control gains.

Distributed network-based formation control

This article investigates a formation control problem of mobile robots. Two main issues of the formation control problem are addressed; one is the implementation of distributed formation control and another is the analysis of effect of network communication on the closed-loop systems. The distributed formation control is obtained by modelling the formation control problem as a linear-quadratic Nash differential game through the use of graph theory. The obtained distributed controller is a state feedback controller that requires both local state and neighbour robot states. As the neighbour robot states are obtained via networks, the network performance affects the control system. Network-induced delay is considered in this article. The stability analysis of the proposed distributed formation controller under network-induced delay is given. A compensation scheme for network-induced delay is proposed and analysed. Mobile robots with double integrator dynamics are used in the formation control simulation. Simulation results are provided to justify the models and solutions.

Control of a Mobile Manipulator with Uncertainties

A mobile manipulator is basically a robot manipulator mounted on a wheeled vehicle. Limiting the wheels to roll without slipage adds nonholonomic constraints to the equations of motion. This makes the planning and control problem of mobile robots a challenging one. Taking into consideration the dynamic effect of the motion of the manipulator on the motion of the vehicle and vice versa together with the uncertain nature of contact parameters between the wheels and ground call for a robust control method. In this paper, a dynamic model for the, whole system is derived. Here, the coupling effects as well as a class of uncertainties are included. For control, all nonlinear terms and uncertainties are considered as external disturbances acting on a linear system. These disturbances are estimated via an observer and then compensated. Trajectory tracking controller can then be built as an outer loop around the inner linear loop.