Differential-drive Robot Research Articles

Feasible minimum distance feedback-based-navigation for a differential drive robot in an environment with obstacles

Consider a differential drive robot (DDR) equipped with an omnidirectional sensor that provides the distances from the robot to corners and walls in a simply connected polygonal environment. Furthermore, the robot does not know a global geometric representation of the world and does not know its position in a global reference frame either. This paper addresses the problem of executing the DDR motion with closed-loop controllers to make the center of the robot travel the smallest distance in the environment to attain a goal configuration modeled as a landmark. As a result, the principal contribution of this article is a closed-loop optimal navigation strategy that does not require the availability of a global geometric map. A formal analysis on the optimality of the task is provided and experiments in a physical DDR are also given. These experiments show the practical viability of the proposed theoretical modeling.

Time-Varying Formation Tracking for Second Order Multi-Agent Systems: An Experimental Approach for Wheeled Robots

In this paper, a time-varying formation tracking protocol for second-order multi-sgent systems (MASs) is presented. The time-varying formation considers translation, rotation, and scaling of the geometric pattern that defines the formation. The control law is simple yet effective, and it is composed of a trajectory tracking control and a consensus control that considers the position and velocity feedback of the connected agents in the MAS. The closed-loop system is asymptotically stable, and this was proved using the Gershgoring’s disk theorem. The performance of the protocol was extensively tested in experiments using a dynamic extension of the differential-drive robot model. The protocol was tested for different communication topologies and also dealt with switching topologies. The proposed protocol presented good performance regaring both time-varying formation and topology changes. Moreover, a comparison with an existing controller and with only trajectory tracking control has been provided, thus showing that the proposed protocol preserves the formation for all the tested topologies in a better way.

A Receding Horizon Trajectory Tracking Strategy for Input-Constrained Differential-Drive Robots via Feedback Linearization

This brief proposes a novel solution to the trajectory tracking control problem for input-constrained differential-drive robots. In particular, we develop a robust set-based receding horizon tracking scheme capable of dealing with state-dependent input constraints arising when the vehicle’s dynamics are approached by a standard feedback linearization (FL) technique. First, offline, we characterize the worst case input constraint set and compute an admissible, although not optimal, controller. Then, online, we leverage the knowledge of the robot’s orientation to enlarge the constraint set in a receding horizon fashion and, consequently, improve the tracking performance. Recursive feasibility and constraints’ fulfillment are formally proven. The approach’s effectiveness is experimentally validated on a Khepera IV differential-drive robot by comparing the control performance with several competitor schemes.

Design of a Saving-Energy Fuzzy Logic Controller for a Differential Drive Robot Based on an Optimization

In the problem of designing a Fuzzy Logic Controller (FLC) for robots in general and a Differential Drive Robot (DDR) in particular, the determination of the parameters of Membership Functions (MFs) and Fuzzy Rules (FRs) is a difficult, complicated, and time-consuming problem because this is mainly based on heuristics and the knowledge of experts. Therefore, this paper provides a new method to efficiently design an FLC for the DDR by using the Genetic Algorithm (GA). Here, the GA is used to generate and optimize both the parameters of MFs and the FRs according to the minimum kinetic energy loss criterion. For this purpose, a program is created in Google Colab® by using the Python language with the help of the “Pymoo” library to not only automatically generate all the suboptimal parameters of MFs and the suboptimal FRs but also the simulate and evaluate different used FLCs. This program is published as an open-source code so that all readers can browse, view, run, and modify the code themselves to design their FLC. The simulation results have shown that the designed FLC is much better than other used FLCs in terms of the minimum kinetic energy loss while other control performances are still good.

Continuous-Time Trajectory Estimation for Differentially Flat Systems

Continuous-time estimation using splines on Lie groups has been gaining traction in the literature due to the ability to incorporate high-frequency sensor data without introducing new optimization parameters. However, evaluating time derivatives and Jacobians of Lie group splines is computationally expensive, limiting their use mainly to offline applications. Motivated by the trajectory planning literature, we develop a new estimation technique that leverages the differential flatness property of many dynamic systems to define the spline in the system's flat output space, which is often Euclidean. Doing so has the added benefit of providing a simple and effective way to include system inputs in the estimation process. We show an example of flatness-based estimation for the unicycle dynamic model. We then show that this new method can achieve similar performance as Lie group spline estimation with significantly less computation time, and validate its use in hardware using a differential-drive robot.

A Set-Theoretic Control Approach to the Trajectory Tracking Problem for Input–Output Linearized Wheeled Mobile Robots

This paper proposes a set-theoretic receding horizon control scheme to address the trajectory tracking problem for input-constrained differential-drive robots. The proposed solution is derived starting from an input-output linearized description of the robot kinematics and a worst-case characterization of the orientation-dependent input constraint acting on the feedback linearized model. In particular, offline, given a worst-case characterization of the constraint set, we analytically design the smallest robust control invariant region for the tracking error. Moreover, such a region is recursively enlarged by computing a family of robust one-step controllable sets whose union characterizes the controller’s domain of attraction. Online, such sets and the knowledge of the current robot’s orientation are leveraged to define a non-conservative control law ensuring bounded tracking error. The effectiveness of the proposed strategy is experimentally validated using a Khepera IV robot, and its performance is contrasted with four alternative trajectory tracking algorithms.

A Globally Convergent Adaptive Velocity Observer for Nonholonomic Mobile Robots Affected by Unknown Disturbances

In this letter, we present a novel adaptive observer for nonholonomic differential-drive robots to simultaneously estimate the system’s angular and linear velocities, along with its external matched disturbances. The proposed method is based on the immersion and invariance technique and makes use of a dynamic scaling factor. The stability and convergence proof of the velocity and disturbance errors are performed using a strict Lyapunov function. We present a detailed simulation study to validate the performance of our approach.

Object servoing of differential-drive service robots using switched control

Object servoing is becoming more and more important for service robots. Because of the nonholonomic constraints of a differential-drive service robot and the possibly changed pose of a movable object, it is challenging to design an object servoing scheme for the differential-drive service robot such that it can asymptotically park at a predefined relative pose to that of the movable object. In this paper, a novel object servoing scheme is proposed for the differential-drive service robots using switched control. Each relative online pose is first estimated by using features of the movable object, the estimated pose is an input of an object servoing friendly parking controller. The linear velocity and angular speed are then determined by the proposed controller. Simulation results validate the performance of the proposed object servoing scheme. Due to its low online computational cost, the proposed scheme can be applied for the real-time tasks of differential-drive service robots to movable objects.

단일 모터 및 선형 구동기 기반의 기동성이 향상된 4 륜 선형 조향 구조 설계

In this paper, we propose a new steering structure to improve the structural robustness and maneuverability of the original steering structure designed in our previous study. Most mobile robot platforms are designed with a differential drive or all-wheel steering (AWS) models. Differential-drive robots have a simple structure and are easy to manufacture, but this structure causes considerable slipping when the robots rotate. The AWS model has such a high maneuverability that it can reduce slip, but this structure requires as many steering motors as driving motors. To address these problems, in our previous research, we designed the steering structure to be as highly maneuverable as AWS models using a single steering motor and a solenoid actuator. However, the original structure requires nonlinear control and is too complex to analyze for selecting the proper steering motor. Thus, in this work, we design a steering structure with a simpler mechanical structure that has structural robustness and is easy to analyze, control, and manufacture. In addition, the proposed structure can steer faster than the original one and even move a four-wheel robot horizontally.

Resilient Monitoring in Heterogeneous Multi-Robot Systems Through Network Reconfiguration

We propose a framework for resilience in a networked heterogeneous multirobot team subject to resource failures. Each robot in the team is equipped with resources that it shares with its neighbors, which are identified based on the team’s communication graph. Additionally, each robot in the team executes a task, whose performance depends on the resources to which it has access. When a resource on a particular robot becomes unavailable ( e.g., a camera ceases to function), the team optimally reconfigures its communication network so that the robots affected by the failure can continue their tasks. We focus on a monitoring task, where robots individually estimate the state of an exogenous process. We encode the end-to-end effect of a robot’s resource loss on the monitoring performance of the team by defining a new stronger notion of observability— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one-hop observability</i> . By abstracting the impact that low-level individual resources have on the task performance through the notion of one-hop observability, our framework leads to the principled reconfiguration of information flow in the team to effectively replace the lost resource on one robot with information from another, as long as certain conditions are met. Network reconfiguration is converted to the problem of selecting edges to be modified in the system’s communication graph after a resource failure has occurred. A controller based on finite-time convergence control barrier functions drives each robot to a spatial location that enables the communication links of the modified graph. We validate the effectiveness of our framework by deploying it on a team of differential-drive robots estimating the position of a group of quadrotors.

Decentralized probabilistic multi-robot collision avoidance using buffered uncertainty-aware Voronoi cells

In this paper, we present a decentralized and communication-free collision avoidance approach for multi-robot systems that accounts for both robot localization and sensing uncertainties. The approach relies on the computation of an uncertainty-aware safe region for each robot to navigate among other robots and static obstacles in the environment, under the assumption of Gaussian-distributed uncertainty. In particular, at each time step, we construct a chance-constrained buffered uncertainty-aware Voronoi cell (B-UAVC) for each robot given a specified collision probability threshold. Probabilistic collision avoidance is achieved by constraining the motion of each robot to be within its corresponding B-UAVC, i.e. the collision probability between the robots and obstacles remains below the specified threshold. The proposed approach is decentralized, communication-free, scalable with the number of robots and robust to robots’ localization and sensing uncertainties. We applied the approach to single-integrator, double-integrator, differential-drive robots, and robots with general nonlinear dynamics. Extensive simulations and experiments with a team of ground vehicles, quadrotors, and heterogeneous robot teams are performed to analyze and validate the proposed approach.

Potential Force Algorithm with Kinematic Control as Path Planning for Disinfection Robot

The disinfection robot is a virus-sterilizing robot that uses a nonholonomic robot model. Route planning algorithms are needed to allow disinfection robots to sterilize rooms in unknown areas and perform the task while navigating using a potential field algorithm. There is a problem applying the algorithm to nonholonomic robots: avoiding obstacles. The proposed route planning algorithm has been transformed into a potential force used to plan the path of disinfection robots in static and dynamic environments and environments with static obstacles. A potential field algorithm is used. There are some issues when the potential force algorithm is applied to nonholonomic disinfection robots in the area. Like any other robot, it takes a long time to avoid static obstacles. Therefore, this paper proposed a potential force algorithm that allows a robot to move towards a target point while avoiding static obstacles. The algorithm showed that a modified potential field algorithm with potential force could be applied to differential-driven robots for path planning. The disinfection robot could avoid obstacles with a faster response using this algorithm.

Challenges and opportunities of using differential-drive robots with project-based learning pedagogies

A master's level subject with one open-ended project spanning the full duration of the subject can offer powerful experiential learning opportunities where students apply their knowledge to both detailed sub-system design and system-level integration. Differential-drive robots can provide a basis for formulating projects that are sufficiently complex, both in terms of the range of concepts required to complete the project and the time required for a student team to bring a working prototype to fruition. In this paper, we present a new subject created to utilize project-based learning pedagogies for a semester-long project based on robots performing a task motivated by an autonomous warehouse application. We describe the level-of-detail that automatic control concepts were explicitly taught in the subject versus their application to the project being self-directed by the student team, and we provide discussion on challenges encountered and opportunities for improving our project-based learning implementation.

Intelligent Obstacle Avoidance Controller for QBot2

This paper describes the design process and the steps involved in the implementation of a fuzzy logic-based controller for autonomous navigation of an educational robot called Qbot2. The differential-driven robot is equipped with three ultrasonic sensors (SRF-04) mounted on the front bumper and DC motors with built-in encoders and current sensors. The controller takes the inputs from three ultrasonic sensors and generates the speed commands to avoid any obstacle in its path. PCB is designed to process all the data from the sensors and direct the signal to the motors via motor driver circuits. The low-level implementation of the hurdle avoidance controller is implemented using an inexpensive Arduino UNO in real-time. The controller performance is validated through an experimental run. The designed platform can be used to implement various fuzzy inference systems in real-time and hence can be utilized as laboratory work for testing soft computing algorithms on mobile robotics.

Infinitesimal Shape-Similarity for Characterization and Control of Bearing-Only Multirobot Formations

When organizing robots into formations, the interplay between the underlying network topology of the team and the sensing and communication modalities available to the individual robots has a fundamental effect on what types of formations are possible. The purpose of this article is to characterize the available motions of formations in which relative angles between robots equipped with bearing-only sensors are maintained. First, infinitesimal shape-similarity, a property of frameworks for which maintaining certain angles between robots ensures that the formation is invariant to translation, rotation, and uniform scaling, is examined; the shape-similarity matrix is redeveloped, and results on its nullspace are presented. Second, triangulations, a class of frameworks, are shown to be infinitesimally shape-similar. Finally, the coupling between network topology and robot capabilities is examined through the design of a decentralized heterogeneous formation-control strategy for a class of triangulations in which all robots are equipped with bearing-only sensors and a single robot can measure distances; the formation-control strategy is demonstrated on a team of differential-drive robots.

Sequencing of multi-robot behaviors using reinforcement learning

Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem, combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.

Onboard dynamic RGB‐D simultaneous localization and mapping for mobile robot navigation

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.

Minimum Energy Control of a Unicycle Model Robot

Abstract Point-to-point path planning for a kinematic model of a differential-drive wheeled mobile robot (WMR) with the goal of minimizing input energy is the focus of this work. An optimal control problem is formulated to determine the necessary conditions for optimality and the resulting two point boundary value problem is solved in closed form using Jacobi elliptic functions. The resulting nonlinear programming problem is solved for two variables and the results are compared to the traditional shooting method to illustrate that the Jacobi elliptic functions parameterize the exact profile of the optimal trajectory. A set of terminal constraints which lie on a circle in the first quadrant are used to generate a set of optimal solutions. It is noted that for maneuvers where the angle of the vector connecting the initial and terminal point is greater than a threshold, which is a function of the radius of the terminal constraint circle, the robot initially moves into the third quadrant before terminating in the first quadrant. The minimum energy solution is compared to two other optimal control formulations: (1) an extension of the Dubins vehicle model where the constant linear velocity of the robot is optimized for and (2) a simple turn and move solution, both of whose optimal paths lie entirely in the first quadrant. Experimental results are used to validate the optimal trajectories of the differential-drive robot.

Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot.

This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety of navigation in the shared workspace with people. Nevertheless, traditional methods normally utilize machine learning techniques on hand-crafted geometrical features extracted from individual clusters. Moreover, the datasets used to train the models are usually small and need to manually label every laser scan, increasing the difficulty and cost of deploying people detection algorithms in new environments. To tackle these problems, (1) we propose a novel deep learning-based method, which uses the deep neural network in a sliding window fashion to effectively classify every single point of a laser scan. (2) To increase the speed of inference without losing performance, we use a jump distance clustering method to decrease the number of points needed to be evaluated. (3) To reduce the workload of labeling data, we also propose an approach to automatically annotate datasets collected in real scenarios. In general, the proposed approach runs in real-time and performs much better than traditional methods. Secondly, conventional pure reactive obstacle avoidance algorithms can produce inefficient and oscillatory behaviors in dynamic environments, making pedestrians confused and possibly leading to dangerous reactions. To improve the legibility and naturalness of obstacle avoidance in human crowded environments, we introduce a sampling-based local path planner, similar to the method used in autonomous driving cars. The key idea is to avoid obstacles by switching lanes. We also adopt a simple rule to decrease the number of unnecessary deviations from the reference path. Experiments carried out in real-world environments confirmed the effectiveness of the proposed algorithms.

A game of surveillance between an omnidirectional agent and a differential drive robot

ABSTRACT This paper addresses a surveillance problem between a Differential Drive Robot (DDR) and an Omnidirectional Agent (OA) in a Euclidean plane without obstacles. The agent is initially located inside the robot's detection region, and it wants to escape from it as soon as possible. The DDR, on the other hand, wants to delay the escape as much as possible. The pursuer's detection region is modelled as a circle centred at the pursuer's current location. In this work, the time-optimal motion strategies for the players are presented. Also, based on the players' initial configurations, we solve the decision problem of determining the winner of the game.