Miniature Mobile Robot Research Articles

Unearthing the history with A‐RHex: Leveraging articulated hexapod robots for archeological pre‐exploration

AbstractThis paper aims to develop a miniature mobile robot suitable to assist archeologists in their first exploration of unknown underground tombs. Due to the rather complex and irregular terrains in the tombs and inspired by the classic RHex design, we have developed a two‐segment articulated robot (A‐RHex) with two RHex design units. The robot is compact and lightweight, with dimensions of 25 cm long, 6.5 cm wide, 7 cm high, and weighs 283 g. To assist the robot in entering the tomb, we have also designed a deployment platform that can take the robots underground through a 10‐cm exploration hole. We introduce the overall design, control, and communication methods of A‐RHex, and theoretically analyze how the articulated design can improve the stability of the robot on slopes. Laboratory experiments and field testings at two real archeological excavation sites in China have validated A‐RHex's mechanical design, control strategies, communications, and capabilities for pre‐exploration of open and closed tombs. We believe that this kind of robot with high terrain adaptability and a small profile may become an important tool for field archeology in the future.

Microstructure-Assisted Vision: Adding New Senses to Low-Power Devices

Miniature mobile robots are emerging with new capabilities and skills. Insect-sized and soft robots are just a few inches in size but capable of a range of tasks. They can act as first responders to search for survivors in disaster debris, perform mining or agricultural foraging, and even move heavy objects to organize. When paired with autonomous navigation capabilities, they can enable a wide range of applications, including surveillance, environmental monitoring, precision agriculture and beyond.

Miniature Mobile Robot Using Only One Tilted Vibration Motor.

In miniature mobile robots, reducing the number of actuators can effectively reduce the size and weight of the robot. However, it is challenging to design a robot with as few actuators as possible without losing good motion performance. This work presented a simple-structured low-cost miniature mobile robot. It is driven by only a single tilted motor and yet is fully capable of being controlled to move forward and turn left or right on the ground. Based on the stick–slip mechanism, the robot’s motion is achieved by interplaying between the centrifugal force generated by the vibration motor tilted on the robot and the friction force of the robot. The robot’s speed can be controlled by regulating the magnitude and the period of the applied voltage. Finally, the robot can translate and rotate on the ground and follow various arbitrary paths. The prototype weighs only 11.15 g, costs $6.35, and is 20 mm in diameter and 25 mm in height. The proposed system is experimentally verified and demonstrates the controllability of the robot by the movement along a straight line, a circle, and more arbitrary paths.

Robust formation control for networked robotic systems using Negative Imaginary dynamics

This paper proposes a consensus-based formation tracking scheme for multi-robot systems utilizing the Negative Imaginary (NI) theory. The proposed scheme applies to a class of networked robotic systems that can be modelled as a group of single integrator agents with stable uncertainties connected via an undirected graph. NI/SNI property of networked agents facilitates the design of a distributed Strictly Negative Imaginary (SNI) controller to achieve the desired formation tracking. A new theoretical proof of asymptotic convergence of the formation tracking trajectories is derived based on the integral controllability of a networked SNI systems. The proposed scheme is an alternative to the conventional Lyapunov-based formation tracking schemes. It offers robustness to NI/SNI-type model uncertainties and fault-tolerance to a sudden loss of robots due to hardware/communication fault. The feasibility and usefulness of the proposed formation tracking scheme were validated by lab-based real-time hardware experiments involving miniature mobile robots.

Phototactic Miniature Soft Robots with Terrain Adaptability

AbstractMiniature locomotive robots that can adaptively change shapes to accommodate to various terrains have technologically significant applications ranging from noninvasive operations, environmental monitoring, to biomedical devices. However, existing miniature robots face two practical bottlenecks: limited mobility and low robustness, which severely restrict their applicability. In this work, terrain‐adaptive miniature soft robots that can be powered, guided, and maneuvered on challenging terrains by a versatile phototactic strategy are created. These robots, constructed by a rationally designed liquid crystal elastomer with powerful photomechanical actuation, enable self‐actuation to generate autonomous and self‐sustained rolling locomotion under constant near‐infrared light stimuli without any on‐off switching. They outperform previous light‐driven miniature robots and exhibit exceptional terrain adaptability to traverse a diversity of simple and complex terrains, and even hybrid terrains with varying topology, mechanics, and rheology. The terrain‐adaptive robots can directionally leap over hurdlers, and even exert high jumping to overcome high wall obstacle. It is envisioned that this proposed technique would enable the design of miniature mobile robots that can accommodate varying terrains and fulfill multiple tasks in unpredictable environments.

A Decentralized Cluster Formation Containment Framework for Multirobot Systems

Cooperative control of multirobot systems (MRSs) has earned significant research interest over the past two decades due to its potential applications in multidisciplinary engineering problems. In contrast to a single specialized robot, the MRS can be designed to offer flexibility, reconfigurability, robustness to faults, and cost-effectiveness in solving complex and challenging tasks. In this article, we aim to develop a unified cluster formation containment coordination framework for networked robots that can be decomposed into two layers containing the leaders and the followers. According to the proposed methodology, the leader robots are first distributed into a set of distinct and nonoverlapping clusters depending on the positions and priorities of the targets exploiting a game-theoretic rule. Then, they are steered to attain the desired formations around the corresponding targets. Subsequently, the follower robots are made to converge into the convex hull spanned by the leaders of the individual clusters. A prototype search and rescue operation is considered to highlight the usefulness of the proposed coordination framework. Furthermore, real-time hardware experiments were conducted on miniature mobile robots to validate the feasibility of the theoretical results.

SDP-Based Robust Formation-Containment Coordination of Swarm Robotic Systems with Input Saturation

There are many potential applications of swarm robotic systems in real-world scenarios. In this paper, formation-containment controller design for single-integrator and double-integrator swarm robotic systems with input saturation is investigated. The swarm system contains two types of robots—leaders and followers. A novel control protocol and an implementation algorithm are proposed that enable the leaders to achieve the desired formation via semidefinite programming (SDP) techniques. The followers then converge into the convex hull formed by the leaders simultaneously. In contrast to conventional consensus-based formation control methods, the relative formation reference signal is not required in the real-time data transmission, which provides greater feasibility for implementation on hardware platforms. The effectiveness of the proposed formation-containment control algorithm is demonstrated with both numerical simulations and experiments using real robots that utilize the miniature mobile robot, Mona.

From Motions to Emotions: Can the Fundamental Emotions be Expressed in a Robot Swarm?

This paper explores the expressive capabilities of a swarm of miniature mobile robots within the context of inter-robot interactions and their mapping to the so-called fundamental emotions. In particular, we investigate how motion asnd shape descriptors that are psychologically associated with different emotions can be incorporated into different swarm behaviors for the purpose of artistic expositions. Based on these characterizations from social psychology, a set of swarm behaviors is created, where each behavior corresponds to a fundamental emotion. The effectiveness of these behaviors is evaluated in a survey in which the participants are asked to associate different swarm behaviors with the fundamental emotions. The results of the survey show that most of the research participants assigned to each video the emotion intended to be portrayed by design. These results confirm that abstract descriptors associated with the different fundamental emotions in social psychology provide useful motion characterizations that can be effectively transformed into expressive behaviors for a swarm of simple ground mobile robots.

Mobile robot location algorithm based on image processing technology

To improve the reconfigurable micro mobile robot cluster system based on precision detection, a positioning and tracking system based on computer digital image processing technology was developed. The system consisted of three subsystems: image acquisition and preprocessing subsystem, rapid positioning subsystem based on robot marker recognition, and tracking subsystem based on position estimation. First, after studying the related algorithms in the subsystem, the threshold selection method of adaptive gray weight conversion was proposed for image preprocessing. Then, a fast positioning method based on marker recognition for miniature mobile robots was proposed. The selection of micro-robot markers and the basis for judging the selection of moments were given. A triangular projection positioning method was implemented, and related experimental results were given. Finally, the windowing scanning algorithm was optimized. According to the speed and direction of the robot, a tracking algorithm for position estimation was proposed. Through the simulation experiment, the effect of system positioning tracking and the system reference time in 0.270 s were given. The results showed that the system had high real-time performance.

Occlusion-Based Cooperative Transport with a Swarm of Miniature Mobile Robots

This paper proposes a strategy for transporting a large object to a goal using a large number of mobile robots that are significantly smaller than the object. The robots only push the object at positions where the direct line of sight to the goal is occluded by the object. This strategy is fully decentralized and requires neither explicit communication nor specific manipulation mechanisms. We prove that it can transport any convex object in a planar environment. We implement this strategy on the e-puck robotic platform and present systematic experiments with a group of 20 e-pucks transporting three objects of different shapes. The objects were successfully transported to the goal in 43 out of 45 trials. When using a mobile goal, teleoperated by a human, the object could be navigated through an environment with obstacles. We also tested the strategy in a 3-D environment using physics-based computer simulation. Due to its simplicity, the transport strategy is particularly suited for implementation on microscale robotic systems.

Cooperation control of multiple miniature robots in unknown obstacle environment

This article focuses on the cooperative control problem of a group of miniature mobile robots in the environment with unknown obstacles. Each robot in the group has limited sensing and communication capability. The problem is addressed by separating it into two parts. One part is the formation control of multi-robot and another is navigation control with unknown obstacles. In the first part, a flexible and fault-tolerant formation scheme is introduced. Meanwhile, the dynamic surface method is combined with backstepping sliding mode control as the tracking control law. In the navigation control part, smooth trajectory of obstacle avoidance is obtained based on limit cycle principle and distance sensor data. Finally, the effectiveness of the proposed method is verified by numerical simulations and hardware experiments carried out on the real platform.

A Min–Max Model Predictive Control Approach to Robust Power Management in Ambulatory Wireless Sensor Networks

This paper addresses the problem of transmission power control within a network of resource-constrained wireless sensors that operate within a particular ambient healthcare environment. Sensor data transmitted to a remote base station within the network arrive subject to node location, orientation, and movement. Power is optimally allocated to all channels using a novel resource efficient algorithm. The proposed algorithm is based on a computationally efficient min-max model predictive controller that uses an uncertain linear state-space model of the tracking error that is estimated via local received signal strength feedback. An explicit solution for the power controller is computed offline using a multiparametric quadratic solver. It is shown that the proposed design leads to a robust control law that can be implemented quite readily on a commercial sensor node platform where computational and memory resources are extremely limited. The design is validated using a fully IEEE 802.15.4 compliant testbed using Tmote Sky sensor nodes mounted on fully autonomous MIABOT Pro miniature mobile robots. A repeatable representative selection of scaled ambulatory scenarios is presented that is quite typical of the data that will be generated in this space. The experimental results illustrate that the algorithm performs optimal power assignments, thereby ensuring a balance between energy consumption and a particular outage-based quality of service requirement while robustly compensating for disturbance uncertainties such as channel fading, interference, quantization error, noise, and nonlinear effects.

Hardware opposition-based PSO applied to mobile robot controllers

Adaptation of mobile robot controllers commonly requires the computation of optimal points of operation. Specifically, for miniature mobile robots with serious computational limitations, that are typical of embedded systems, one of the main challenges is the adaptation of efficient computational methods in order to find solutions of complex optimization problems, which demand large execution times. This drawback compels the design of high-performance parallel optimization algorithms which must run over embedded system platforms. This paper describes how adequate hardware implementations of the Particle Swarm Optimization (PSO) algorithm can be useful for real time adaptation of mobile robot controllers. For achieving this, a new architecture is proposed, which is based on an FPGA implementation of the opposition-based learning (OBL) approach applied to the PSO (for short HPOPSO), and which explores the intrinsic parallelism of this algorithm in order to adjust the weights of a neural robot controller in real time according to desired behaviors. The proposed HPOPSO was applied to the learning-from-demonstration problem in which a teacher performs executions of the desired behavior. Effectiveness of the proposed architecture was demonstrated by numerical simulations and the feasibility of the adaptive behavior of the neural robot controller was confirmed for two obstacle avoidance case studies that were preserved when one or more failures on the distance sensors occur. The HPOPSO, which uses the OBL technique, improves the quality of the solutions in comparison with the standard PSO. Comparisons of the adaptation time between hardware and software approaches have demonstrated the suitability of the FPGA implementation of the proposed HPOPSO for attending specific requirements of embedded system applications.

Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms

In this article we describe implementations of various bio-inspired algorithms for obtaining the chemical gas concentration map of an environment filled with a contaminant. The experiments are performed using Khepera III and miniQ miniature mobile robots equipped with chemical gas sensors in an environment with ethanol gas. We implement and investigate the performance of decentralized and asynchronous particle swarm optimization (DAPSO), bacterial foraging optimization (BFO), and ant colony optimization (ACO) algorithms. Moreover, we implement sweeping (sequential search algorithm) as a base case for comparison with the implemented algorithms. During the experiments at each step the robots send their sensor readings and position data to a remote computer where the data is combined, filtered, and interpolated to form the chemical concentration map of the environment. The robots also exchange this information among each other and cooperate in the DAPSO and ACO algorithms. The performance of the implemented algorithms is compared in terms of the quality of the maps obtained and success of locating the target gas sources.

Shape Memory Polymer-Based Flexure Stiffness Control in a Miniature Flapping-Wing Robot

An active flexural hinge has been developed and incorporated into the transmission of a prototype flapping-wing robot. The multilayered flexure, which is constructed from a shape memory polymer and a polyimide film, showed controllable stiffness under change in temperature. At room temperature, the flexure had a bending stiffness of 572 mN·mm; when warmed to 70°C, the stiffness was 11 mN·mm. The resulting single-wing flapping system demonstrated up to an 80% change in generated lift without modification of the waveform of the main driving piezoelectric actuator. Such active stiffness tunable flexure joints could be applied to any flexural miniature mobile robot and device mechanisms.

Development of a low-cost active 3D triangulation laser scanner for indoor navigation of miniature mobile robots

For miniaturized mobile robots that aim at exploring unknown environments, no-contact 3D sensing of basic geometrical features of the surrounding environment is one of the most important capabilities for survival and the mission. In this paper, a low-cost active 3D triangulation laser scanner for indoor navigation of miniature mobile robots is presented. It is implemented by moving both a camera and a laser diode together on the robot’s movable part. The movable part is actuated by a servo motor through a gear train to achieve ±90° scanning view angle. The software module includes image processing and data post-processing. 3D world coordinates are calculated from 2D image coordinates based on the triangulation principle. With a 3D laser scanning method, navigation algorithms for obstacle avoidance and gateway passing are proposed. Finally, experiments are conducted to validate performance of the scanner and to test the efficiency of the navigation algorithms.

Autonomous construction using scarce resources in unknown environments

The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a “real but contrived” experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real world.

Design and Rolling Locomotion of a Magnetically Actuated Soft Capsule Endoscope

This paper proposes a magnetically actuated soft capsule endoscope (MASCE) as a tetherless miniature mobile robot platform for diagnostic and therapeutic medical applications inside the stomach. Two embedded internal permanent magnets and a large external magnet are used to actuate the robot remotely. The proposed MASCE has three novel features. First, its outside body is made of soft elastomer-based compliant structures. Such compliant structures can deform passively during the robot-tissue contact interactions, which makes the device safer and less invasive. Next, it can be actively deformed in the axial direction by using external magnetic actuation, which provides an extra degree of freedom that enables various advanced functions such as axial position control, drug releasing, drug injection, or biopsy. Finally, it navigates in three dimensions by rolling on the stomach surface as a new surface locomotion method inside the stomach. Here, the external attractive magnetic force is used to anchor the robot on a desired location, and the external magnetic torque is used to roll it to another location, which provides a stable, continuous, and controllable motion. The paper presents design and fabrication methods for the compliant structures of the robot with its axial deformation and position control capability. Rolling-based surface locomotion of the robot using external magnetic torques is modeled, and its feasibility is tested and verified on a synthetic stomach surface by using a magnetically actuated capsule endoscope prototype.

A Task of Miniature Mobile Robot Learning for Obstacle Avoidance through Neural Networks

This paper is concerned with Q-learning , a very popular algorithm for reinforcement learning ,for obstacle avoidance through neural networks. The principle tells that the focus always must be on both ecological nice tasks and behaviours when designing on robot. Many robot systems have used behavior-based systems since the 1980’s.In this paper, the Khepera robot is trained through the proposed algorithm of Q-learning using the neural networks for the task of obstacle avoidance. In experiments with real and simulated robots, the neural networks approach can be used to make it possible for Q-learning to handle changes in the environment.

Synchronization of epicardial crawling robot with heartbeat and respiration for improved safety and efficiency of locomotion.

HeartLander is a miniature mobile robot designed to navigate over the epicardium of the beating heart for minimally invasive therapy. This paper presents a technique to decrease slippage and improve locomotion efficiency by synchronizing the locomotion with the intrapericardial pressure variations of the respiration and heartbeat cycles. Respiratory and heartbeat phases were detected in real time using a chest-mounted accelerometer during locomotion in a porcine model in vivo. Trials were conducted over the lateral aspect of the heart surface to test synchronized locomotion against an unsynchronized control. Offline evaluation showed that the respiration and heartbeat algorithms had accuracies of 100% and 88%, respectively. Synchronized trials exhibited significantly lower friction, higher efficiency, and greater total distance traveled than control trials. Synchronization of the locomotion of HeartLander with respiration and heartbeat is feasible and results in safer and more efficient travel on the beating heart.