Differential Wheeled Robot Research Articles

Prescribed-Time Fractional Order Control of DWR via Time-Varying Scaling Function

This study focuses on a differential wheeled robot’s (DWR) prescribed-time fractional order position control. Firstly, based on the kinematic model of DWR, a distance-related orientation error is designed using the inverse sine function. Based on this, an improved linear velocity constraint function is proposed. Compared with existing methods, while ensuring the correlation between velocity and orientation error, the multibalance point risk caused by large angle errors is avoided. Then, a prescribed-time fractional order position controller based on a time-varying scaling function is proposed to stabilize the kinematic system of DWR in the prescribed time. This controller can stabilize the position control system of the DWR in a prescribed time by adjusting the prescribed-time parameter, avoiding the infinite gain risk in traditional prescribed-time controllers. Finally, through numerical simulation, we verify that the proposed control law can converge the system status of DWR to the bounded interval in the prescribed time.

Long-Term Dynamic Window Approach for Kinodynamic Local Planning in Static and Crowd Environments

Local planning for a differential wheeled robot is designed to generate kinodynamic feasible actions that guide the robot to a goal position along the navigation path while avoiding obstacles. Reactive, predictive, and learning-based methods are widely used in local planning. However, few of them can fit static and crowd environments while satisfying kinodynamic constraints simultaneously. To solve this problem, we propose a novel local planning method. The method applies a long-term dynamic window approach to generate an initial trajectory and then optimizes it with graph optimization. The method can plan actions under the robot's kinodynamic constraints in real time while allowing the generated actions to be safer and more jitterless. Experimental results show that the proposed method adapts well to crowd and static environments and outperforms most state-of-the-art approaches.

Prioritized Safe Interval Path Planning for Multi-Agent Pathfinding With Continuous Time on 2D Roadmaps

We address a challenging multi-agent pathfinding (MAPF) problem for hundreds of agents moving on a 2D roadmap with continuous time. Despite its known potential for producing better solutions compared to typical grid and discrete-time cases, few approaches have been established to solve this problem due to the intractability of collision checks on a large scale. In this work, we propose Prioritized Safe-Interval Path Planning with Continuous-Time Conflicts (PSIPP/CTC) that extends a scalable prioritized planning algorithm to work on the 2D roadmap and continuous-time setup by alleviating intensive collision checks. Our approach involves a novel concept named Continuous-Time Conflict (CTC), which describes a pair among vertices and edges associated with continuous-time intervals within which collisions can happen between agents. We pre-compute CTCs using geometric neighbor-search and sweeping techniques and annotate roadmaps with the CTCs just once before planning starts. Doing so allows us to efficiently enumerate collision-free time intervals for all vertices and edges and find each agent's path with continuous time in prioritized planning. Extensive experimental evaluations demonstrate that PSIPP/CTC significantly outperforms existing methods in terms of planning success rate and runtime while maintaining an acceptable solution quality. As a proof of concept, we also confirmed the effectiveness of the proposed approach on a physics simulation with differential wheeled robots.

Navigation of a Differential Wheeled Robot Based on a Type-2 Fuzzy Inference Tree

This paper presents a type-2 fuzzy inference tree designed for a differential wheeled mobile robot that navigates in indoor environments. The proposal consists of a controller designed for obstacle avoidance, a controller for path recovery and goal reaching, and a third controller for the real-time selection of behaviors. The system takes as inputs the information provided for a 2D laser range scanner, i.e., the distance of nearby objects to the robot, as well as the robot position in space, calculated from mechanical odometry. The real performance is evaluated through metrics such as clearance, path smoothness, path length, travel time and success rate. The experimental results allow us to demonstrate an appropriate performance of our proposal for the navigation task, with a higher efficiency than the reference methods taken from the state of the art.

Consensus Stability of a Large-Scale Robotic Network Under Input and Transmission Delays

A large scale network of differential-wheeled robots seeking consensus is considered in the presence of input and transmission delays. Consensus stability of this nonlinear network dynamics holds if its linear part can be shown to achieve consensus stability despite the delays. We present theoretical and computational results on this linear large scale system. From a theoretical point of view, conditions on the amount of input delays are revealed such that stability is guaranteed independent of any transmission delay, and in the case this is not possible, a computationally-efficient algorithm is proposed to reveal the largest transmission delay that the network can accommodate without losing stability.

Safety Embedded Differential Dynamic Programming Using Discrete Barrier States

Certified safe control is a growing challenge in robotics, especially when performance and safety objectives must be concurrently achieved. In this work, we extend the barrier state (BaS) concept, recently proposed for safe stabilization of continuous time systems, to safety embedded trajectory optimization for discrete time systems using discrete barrier states (DBaS). The constructed DBaS is embedded into the discrete model of the safety-critical system integrating safety objectives into the system's dynamics and performance objectives. Thereby, the control policy is directly supplied by safety-critical information through the barrier state. This allows us to employ the DBaS with differential dynamic programming (DDP) to plan and execute safe optimal trajectories. The proposed algorithm is leveraged on various safety-critical control and planning problems including a differential wheeled robot safe navigation in randomized and complex environments and on a quadrotor to safely perform reaching and tracking tasks. The DBaS-based DDP (DBaS-DDP) is shown to consistently outperform penalty methods commonly used to approximate constrained DDP problems as well as CBF-based safety filters.

Visual-Inertial RGB-D SLAM With Encoders for a Differential Wheeled Robot

Recent years have seen multiple impressive results in visual-inertial odometry (VIO) techniques, by which accurate state estimation can be achieved via the extended Kalman filter (EKF) or nonlinear optimization. However, these approaches are rarely open source, and they tend to fail in real experiments due to the temporary lack of feature points and fast motion. Therefore, in this study, we used encoders to overcome the temporary failure of purely vision-based simultaneous localization and mapping (SLAM). Here, we propose a generative measurement model for encoders and derive an expression for the maximum a posteriori (MAP) estimate and necessary Jacobians for optimization. We use our theory to present a novel tightly coupled visual-inertial encoder RGB-Depth (RGB-D) SLAM system. Tests on our system on an in-house dataset confirmed that our modeling effort led to accurate (with a root mean squared error (RMSE) of approximately 2–7 cm) and robust state estimation in real time. The source code and our dataset containing the encoder information have been published for verification.

Hybrid genetic algorithm based smooth global-path planning for a mobile robot

The goal of the global path planning task is to produce an optimal safe path for robot respect to the given map. There are many global path planning methods that have been studied. In this paper, we propose a novel Hybrid Genetic Algorithm (HGA) that is used to generating smooth paths for differential wheeled robots. The main idea of HGA is to provide the dynamic mutation rate and switchable global-local search method to the mutation operator of ordinary genetic algorithm. By deploying these modifications, the premature convergence of the generic genetic algorithm and the high time-consuming fitness calculation of the memetic algorithm are reduced. HGA also takes care of chromosome length (defined by the size of a set of points that construct a path) by applying the population replacement method. Our algorithm satisfies the important criteria in the path planning task: safe and minimum-length. Based on continuous-curvature Piecewise Cubic Bezier Curve, HGA directly provides the smoothed paths for differential wheeled robots. Therefore, our proposed algorithm does not need any third-party algorithm for smoothing planned path. Several experiments regarding the proposed algorithm on our robot and its results are analyzed and presented.

ROS 기반의 모바일 로봇 시스템 개발 및 실내 자율 주행 실험

In this paper, we describe the development of a ROS-based mobile robot system and experimental results for indoor autonomous driving. The developed mobile robot system is modularized, so that ROS can be used for perception, decisions, planning and control. The platform used in this study is a differential wheeled robot, which consists of sensors for indoor mapping and localization and motor drivers for control. To verify the feasibility of the developed mobile robot system, we performed indoor autonomous driving experiments. It is found that the mobile robot can autonomously reach its destination without experiencing any collisions while localizing itself in indoor environment, recognizing obstacles, planning local paths for obstacle avoidance.

Algorithm for Base Action Set Generation Focusing on Undiscovered Sensor Values

Previous machine learning algorithms use a given base action set designed by hand or enable locomotion for a complicated task through trial and error processes with a sophisticated reward function. These generated actions are designed for a specific task, which makes it difficult to apply them to other tasks. This paper proposes an algorithm to obtain a base action set that does not depend on specific tasks and that is usable universally. The proposed algorithm enables as much interoperability among multiple tasks and machine learning methods as possible. A base action set that effectively changes the external environment was chosen as a candidate. The algorithm obtains this base action set on the basis of the hypothesis that an action to effectively change the external environment can be found by observing events to find undiscovered sensor values. The process of obtaining a base action set was validated through a simulation experiment with a differential wheeled robot.

Ziegler-Nichols Based Proportional-Integral-Derivative Controller for a Line Tracking Robot

<p>Line tracking robots have been widely implemented in various applications. Among various control strategies, a proportional-integral-derivative (PID) algorithm has been widely proposed to optimize the performance of a line tracking robot. However, the motivation of using a PID controller, instead of a proportional (P) or a proportional-integral (PI) controller, in a line tracking task has seldom been discussed. Particularly, the use of a systematic tuning approach e.g. closed loop Ziegler Nichols rule to optimize the parameters of a PID controller has rarely been investigated. Thus, this paper investigates the performance of P, PI, and PID controllers in a line tracking task, and the ability of Ziegler Nichols rule to optimize the parameters of the P, PI, and PID controllers. First, the ultimate gain value, K<sub>u</sub> and ultimate period of oscillation, P<sub>u</sub> were estimated using a proposed approach. Second, the values of K<sub>P</sub>, K<sub>I</sub> and K<sub>D</sub> were estimated using the Ziegler Nichols formulae. The performance of a differential wheeled robot in the line tracking task was evaluated using three different speeds. Results indicate that the Ziegler Nichols rule coupled with the proposed method is able to identify the parameters of the P, PI, and PID controllers systematically in the line tracking task. Findings indicate that the mobile robot coupled with a proportional controller achieved the best performance compared to PI and PID controllers in the line tracking process when the estimated initial parameters were used.</p>

Input Design for Fault Detection Using Extended Kalman Filter and Reinforcement Learning

The paper deals with an active fault detection problem for jump Markov nonlinear systems with Gaussian noises. The problem is formulated as a functional optimization problem over an infinite-time horizon with a general discounted detection criterion. The design of an active fault detector is performed in two steps. First, the original problem is recast as a perfect state information problem by complementing the nonlinear system with a suboptimal state estimator based on a bank of extended Kalman filters. Then, a temporal-difference learning algorithm is used to train the active fault detector such that the criterion is minimized. A simulation example of a differential wheeled robot is used to illustrate the performance of the proposed design.

A unified dynamic algorithm for wheeled multibody systems with passive joints and nonholonomic constraints

This paper presents a systematic approach to develop a generalized symbolic/numerical dynamic algorithm for modeling and simulation of multibody systems with branches and wheels. The proposed dynamic algorithm includes the direct kinematic and inverse dynamic models of the wheeled systems with prismatic/revolute as well as actuated/passive degrees of freedom. Using the geometric configuration of the system through modified Denavit–Hartenberg convention, symbolic equations in general algorithmic form are developed for kinematic constraints associated with the wheel–ground contacts. The Newton–Euler equations are used to develop an algorithm for the inverse dynamic model of the multibody system. The complete algorithm is then used to solve the kinematics and dynamics of the system, and computes: (i) the kinematics of the external/internal passive degrees of freedom of the system, (ii) the Lagrange multipliers associated with the wheel–ground contacts, and (iii) the driving forces/torques of the actuated degrees of freedom. Some examples are solved with the help of the proposed algorithm, using MATLAB, to illustrate its implementation on different wheeled systems. These examples include a differential wheeled robot, a snake-like wheeled system, and a bicycle.

Event-Based Control Strategy for Mobile Robots in Wireless Environments.

In this paper, a new event-based control strategy for mobile robots is presented. It has been designed to work in wireless environments where a centralized controller has to interchange information with the robots over an RF (radio frequency) interface. The event-based architectures have been developed for differential wheeled robots, although they can be applied to other kinds of robots in a simple way. The solution has been checked over classical navigation algorithms, like wall following and obstacle avoidance, using scenarios with a unique or multiple robots. A comparison between the proposed architectures and the classical discrete-time strategy is also carried out. The experimental results shows that the proposed solution has a higher efficiency in communication resource usage than the classical discrete-time strategy with the same accuracy.

Mathematical modelling of turning delays in swarm robotics

We investigate the effect of turning delays on the behaviour of groups of differential wheeled robots and show that the group-level behaviour can be described by a transport equation with a suitably incorporated delay. The results of our mathematical analysis are supported by numerical simulations and experiments with E-Puck robots. The experimental quantity we compare with our revised model is the mean time for robots to find the target area in an unknown environment. The transport equation with delay better predicts the mean time to find the target than the standard transport equation without delay.

Simulation Experiment on Travelling Path Control of Differential Wheeled Pipeline Robot

For the travelling path control problem of differential wheeled pipeline robot, this paper proposes an approach to measure the velocities of drive wheels by photoelectric encoder, and gets trajectory curves of drive wheels on the experimental platform. Based on simulation of trajectory curves, fuzzy PID control of pipeline robot is fulfilled in straight travelling process. After PID parameters are implemented on the experimental robot fine control effectiveness is achieved.

Edge-weighted consensus-based formation control strategy with collision avoidance

SUMMARYIn this paper, a consensus-based control strategy is presented to gather formation for a group of differential-wheeled robots. The formation shape and the avoidance of collisions between robots are obtained by exploiting the properties of weighted graphs. Since mobile robots are supposed to move in unknown environments, the presented approach to multi-robot coordination has been extended in order to include obstacle avoidance. The effectiveness of the proposed control strategy has been demonstrated by means of analytical proofs. Moreover, results of simulations and experiments on real robots are provided for validation purposes.

A Nonlinear Control Design for Tracked Robots with Longitudinal Slip

AbstractThis paper presents an adaptive control strategy for a tracked mobile robot, in which the longitudinal slip of the left and right tracks are described by two unknown parameters. It is assumed that the kinematic model of the tracked robot is approximated by the one of a differential wheeled robot. An adaptive nonlinear feedback control law that compensates for the longitudinal slip is proposed to achieve a given trajectory tracking objective. Asymptotic stability of the close-loop system is ensured using an appropriate Lyapunov function. Numerical results show the benefits of the proposed approach.

Design of a Nonlinear Control Law for a Group of Differential-Wheel Robots Performing Rendezvous

The problems related to the coordination of mobile robots have been widely studied in the past and, because of the possible real scenarios, they are still considered one of the most interesting field of research. Even if many methods have been used to solve such problems, a new approach based on results borrowed from graph theory has been introduced in recent works. In particular, the coordination between agents relies on the possibility of solving the rendezvous problem and to drive all the agents to a predefined final state. Starting from these considerations, in this paper the stability of a group of differential-wheeled robots performing rendezvous by exploiting only local measurements is demonstrated and supported by means of simulations.