Autonomous Collision-Free Navigation of Microvehicles in Complex

Aug 12, 2017 - Self-propelled micro- and nanoscale robots represent a rapidly emerging and fascinating robotics research area. However, designing auto...
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Autonomous Collision-Free Navigation of Microvehicles in Complex and Dynamically Changing Environments Tianlong Li,†,‡,§ Xiaocong Chang,†,‡,§ Zhiguang Wu,†,‡,§ Jinxing Li,‡,§ Guangbin Shao,† Xinghong Deng,† Jianbin Qiu,† Bin Guo,† Guangyu Zhang,† Qiang He,† Longqiu Li,*,† and Joseph Wang*,‡ †

State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China Department of Nanoengineering, University of California San Diego, La Jolla, California 92093, United States

‡

S Supporting Information *

ABSTRACT: Self-propelled micro- and nanoscale robots represent a rapidly emerging and fascinating robotics research area. However, designing autonomous and adaptive control systems for operating micro/nanorobotics in complex and dynamically changing environments, which is a highly demanding feature, is still an unmet challenge. Here we describe a smart microvehicle for precise autonomous navigation in complicated environments and traffic scenarios. The fully autonomous navigation system of the smart microvehicle is composed of a microscope-coupled CCD camera, an artificial intelligence planner, and a magnetic field generator. The microscope-coupled CCD camera provides realtime localization of the chemically powered Janus microsphere vehicle and environmental detection for path planning to generate optimal collision-free routes, while the moving direction of the microrobot toward a reference position is determined by the external electromagnetic torque. Real-time object detection offers adaptive path planning in response to dynamically changing environments. We demonstrate that the autonomous navigation system can guide the vehicle movement in complex patterns, in the presence of dynamically changing obstacles, and in complex biological environments. Such a navigation system for micro/ nanoscale vehicles, relying on vision-based close-loop control and path planning, is highly promising for their autonomous operation in complex dynamic settings and unpredictable scenarios expected in a variety of realistic nanoscale scenarios. KEYWORDS: micro/nanorobot, artificial intelligence, targeted delivery, autonomous navigation, collision-free operation for autonomous path planning in the fields of biomedicine and nanomanipulation. While considerable efforts have been devoted to advancing the motion control of nanoscale motors,39 limited attention has been given for achieving fully autonomous operation of micro/nanovehicles in complex settings and unpredictable traffic scenarios. Therefore, there are growing demands for exploiting an intelligent control system for micro/nanomotors to navigate without colliding with obstructions in a complicated environment. While selfdriving vehicles are at the heart of industrial and academic research activities, no efforts have been devoted for realizing such autonomous movement of micro/nanoscale vehicles. Achieving autonomous operation of nanovehicles could benefit a wide range of relevant nanoscale movement challenges

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rtificial micro/nanomachines that convert local fuels or external energies into kinetic movement for overcoming viscous drag at low Reynolds number regimes have markedly extended the reach of human beings in biomedical and nanomanipulation tasks.1−13 The operation of micro/nanoscale machines and propellers has proved to be useful for drug delivery,14−19 biosensing,20,21 environmental remediation,22−24 active materials assembly,25−28 and nanoscale lithography and imaging.29,30 Although the fabrication, locomotion, and functionalization of micro/nanomachines have been thoroughly investigated, the control and navigation of micro/nanomachines are restricted to simple closed-loop control.31−38 By using the closed-loop control system, the micromotors were commanded to move along a predefined route toward a fixed point in two-dimensional (2D) or threedimensional (3D) spaces. However, moving micro/nanomotors on demand in complicated and dynamically changing environments without human assistance is still an unmet challenge. In addition, the lack of artificial intelligence (AI) impedes their © 2017 American Chemical Society

Received: June 28, 2017 Accepted: August 12, 2017 Published: August 12, 2017 9268

DOI: 10.1021/acsnano.7b04525 ACS Nano 2017, 11, 9268−9275

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ACS Nano associated with unpredictable nanoscale “traffic” settings, e.g., nanomotor traffic jams during active transport operation in narrow microchannels.40 Path planning problems arise in diverse fields such as robotics, manufacturing, and virtual prototyping.41,42 Such problems involve searching the unknown space for a collisionfree path that connects given start and target configurations, while satisfying the constraints imposed by complicated obstacles. The process of path planning can be classified into global path planning and local path planning. Global path planning methods are usually conducted in a completely known environment. Different attempts to address this problem have been carried out in connection to an artificial potential field and search algorithm.43,44 On the other hand, local path planning techniques are more efficient in robot navigation in an unknown and dynamically changing setting. Neural network and fuzzy logic approaches, which offer an attractive alternative for building reactive systems, are efficient local path planning methods.45−48 Hence, combining global path planning with local path planning can offer effective navigation in complex dynamically changing environments. Herein, we introduce a smart microvehicle for precise autonomous navigation in complicated and dynamically changing environments through optimal path planning. The autonomous navigation system of a microvehicle is composed of an artificial intelligence planner, a microscope-coupled CCD camera, and a magnetic field generator, and addresses the challenge of accurate path planning in unknown environments. The CCD camera provides the real-time localization of the microvehicle and environmental detection. On the basis of an artificial potential field, searching algorithms, and the fuzzy logic approach, an AI planner can generate an optimal collision-free path and offer high localization accuracy. The locomotion of the microvehicle along the proposed route is navigated by the electromagnets. Similar to their large-vehicle counterparts, the autonomous navigation of microvehicles entails collision-free movement in dynamic environments. We demonstrate that such autonomous control diverts the vehicle route away from stationary and moving obstacles, and navigates it in complicated environments. Such a control system allows handling realistic nanoscale “traffic” scenarios where the dynamics of other traffic participants or unexpected events must be considered. By adding the visual recognition ability to the path planner, we demonstrate that the control system is able to distinguish different biological entities, such as cancer cells and red blood cells, and then further guide the microvehicle to a desired disease site. Such a microvehicle based on an autonomous navigation system can open the door for a wide variety of applications ranging from nanomanipulation to precise medical treatment.

Figure 1. Schematic of autonomous navigation system for adaptive control of microvehicle. (a) Schematic of the autonomous navigation system for a microvehicle. (b) Overall process flow of the closed-loop feedback autonomous navigation mechanism for the microvehicle. (c) Schematic of the autonomous navigation mechanism for the microvehicle: (I) sensing, (II) decision, and (III) action.

the microvehicle movement along a predesigned path. As shown in Figure 1b, digital microscopy images from the camera are sent back to the feature extraction processor to identify features and compute the global occupancy map and local occupancy map. Thereafter, these maps are used as inputs to the AI planner, which gives instructions to the magnetic field generator for local navigation of the microvehicle. Our control method thus consists of the following three steps (see Figure 1c): (I) sensing and detecting the unknown environment, followed by building a map representing the unoccupied and occupied spaces of the environment; (II) reconstructing collision-free microvehicle trajectories in the global occupancy map through the AI planner; (III) optimizing the input signal for the magnetic coils generating a desired orientation of the magnetic field. The autonomous obstacle avoidance ability of the microvehicle was demonstrated first in the presence of a stationary object (see Figure 2). The navigation strategy is shown in Figure 2b. First, the computer vision module of the autonomous navigation system is triggered to sense a single object and then creates a global map, representing the unoccupied and occupied spaces of the environment surrounding the microvehicle. Subsequently, the motion planner reconstructs the most favorable collision-free trajectory for the microvehicle. An artificial potential field, expressed in the Cartesian workplace of the microvehicle, is employed to obtain the optimal path between the initial and target points,

RESULTS AND DISCUSSION The setup of the autonomous navigation system is shown in Figure 1a, which is composed of a CCD camera, an AI planner, and a magnetic field generator. The microscope-coupled camera provides real-time visual detection for the AI planner. The AI planner is split into three functional modules: (a) a computer vision module for tracking the microvehicle and detecting obstacles in its environment; (b) a motion planner, which consists of an artificial potential field module, a search algorithm module, and a fuzzy logic module to generate an optimal obstacle-free path between starting point and destination; (c) a magnetic motion controller to manipulate 9269

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solid line and black circles show the planning path generated by the AI planner and the tracked microvehicle position at 1 s intervals, respectively (see Supplementary Video 1). Here, the average path error between the planning path and actual trajectory is found to be approximately 0.8 body length. Figure 2n displays the dependence of the collision probability on the safety distance between the microvehicle and the stationary obstacle with the microvehicle velocity using safety distances between 0.2 and 1.0 body length, and speeds ranging from 2 to 10 body length/s. As expected, the collision probability increases upon increasing the velocity of the microvehicle and decreasing the safety distance. For a microvehicle moving at a speed higher than 6 body lengths/s and using a safety distance smaller than 0.25 vehicle body length, the collision probability is 1, indicating that the microvehicle cannot avoid the obstacle under autonomous navigation system control. Path searching and planning are crucial for the successful operation of autonomous vehicles. To evaluate the autonomous navigation ability of the smart microvehicle, two different complex maze-like microstructures were fabricated using photolithography to serve as a swimming circumstance. As shown in Figure 3a, similar to the navigation in the presence of

Figure 2. Autonomous navigation in capability of the microvehicle to avoid a stationary obstacle. (a, b) Schematic illustration of microvehicle locomotion in the presence of a stationary obstacle (a) without and (b) with AI control. Trajectories locomoted by autonomous navigation of the microvehicle in a visualization of the stationary obstacle map at 0 s (c), 8 s (d), 16 s (e), 24 s (f), and 32 s (g). Scale bar, 20 μm. Fuel level, 5% H2O2. Microvehicle location at 0 s (h), 8 s (i), 16 s (j), 24 s (k), and 32 s (l). Scale bar, 20 μm. (m) Comparison between the estimated route and actual path of autonomous navigation for the microvehicle. (n) Collision probability as a function of the microvehicle/stationary obstacle safety distance and the microvehicle velocity. Conditions: Janus microsphere vehicles (4.74-μm-diameter) propelling in an aqueous solution containing 5% H2O2 fuel.

using a repulsive potential and attractive potential for the obstacle and target, respectively. These potentials are combined to form a composite potential to generate collision-free trajectories between the initial point and target destination. Finally, the optimal control signal is applied to the magnetic field generator, steering the moving microvehicle along the selected collision-free pathway. To investigate the navigation behavior of the microvehicle, a stationary obstacle, with dimensions of 100 × 100 × 10 μm, has been fabricated by a photolithography technique. A 4.74-μmdiameter Janus sphere micromotor, propelling in an aqueous solution containing 5% H2O2 fuel, has been used to demonstrate the concept. The Janus micromotors are prepared by coating silica spheres with thin hemispherical Ti/Ni/Pt metallic layers for magnetic steering and catalytic propulsion in the H2O2 solution. The real-time binary images (following image processing) and the corresponding path planning are presented in Figure 2c−g. The manipulation aimed to transport this microvehicle autonomously to the target point. By using the proposed path planner, a collision-free path was obtained, guiding the microvehicle to bypass the single obstacle under the controllable magnetic field. Trajectory images from the CCD camera are shown in Figure 2h−l. The estimated planning path and actual trajectory are displayed in Figure 2m, where the red

Figure 3. Autonomous navigation of a microvehicle in a complicated environment. (a) Schematic illustrating the autonomous navigation for the microvehicle moving in a micromaze: (I) sensing, (II) path planning, (III) decision, and (IV) action. (b) Chiral micromaze. Scale bar, 50 μm. (c) Initial position of the microvehicle. Scale bar, 50 μm. (d) Real-time path planning. Scale bar, 50 μm. (e) Trajectories steered by autonomous navigation of the microvehicle through a chiral micromaze in a 5% H2O2 fuel solution. Scale bar, 20 μm. (f) Complex micromaze with multiple paths. Scale bar, 50 μm. (g) Initial position of the microvehicle. Scale bar, 50 μm. (h) Real-time path planning. Scale bar, 50 μm. (i) Trajectories steered by autonomous navigation of the microvehicle through the complex micromaze after optimized path planning. Scale bar, 50 μm. Conditions: Janus microsphere motors (4.74-μmdiameter) propelling in an aqueous solution containing 5% H2O2 fuel.

a stationary obstacle, the autonomous navigation in a complicated environment consists of sensing, decision, and action. However, due to the narrow corridor effect, the microvehicle guided by the potential field could behave erratically, oscillating in a sustained manner between the two walls of the corridor. Path planning in complicated environ9270

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Figure 4. Smart microvehicle avoiding a collision with a dynamically moving object. (a) Schematic illustrating dynamic movable obstacle avoidance for a microvehicle in the condition of the target point on the right and obstacle on the top. (b) Real-time path planning. Scale bar, 20 μm. (c) Trajectories steered by autonomous navigation of the microvehicle, avoiding collisions with a dynamic obstacle. Scale bar, 20 μm. (d) Schematic illustrating dynamic movable obstacle avoidance for a microvehicle in the condition of both target and dynamic obstacle on the top. (e) Real-time path planning. Scale bar, 20 μm. (f) Trajectories steered by autonomous navigation of a microvehicle avoiding collisions with a dynamic obstacle. Scale bar, 20 μm. (g) Schematic illustrating the microvehicle avoidance of the nanowire motor dynamic obstacle. (h) Real-time path planning. Scale bar, 20 μm. (i) Trajectories steered by autonomous navigation of a microvehicle avoiding collisions with a dynamic obstacle. Conditions: Janus microsphere motors (4.74-μm-diameter) propelling in an aqueous solution containing 5% H2O2 fuel.

decision making enable the microvehicle to avoid collisions with such dynamically moving objects. The fuzzy logic approach is employed to avoid the dynamic obstacle. This approach is credited with being an adequate methodology for designing robust controllers that can deliver a satisfactory performance in the presence of large amounts of variability in the parameters.45,46 The rule base of the fuzzy logic approach is constructed based on human experience. Here, triangular membership functions were applied to change the performance of the system. The performance of the fuzzy logic based path planner for avoiding dynamic moving objects is demonstrated using different working conditions. As shown in Figure 4a, the destination is on the right, while the dynamic Janus motor obstacle locomotes from the top (see Figure 4a). Trajectory images from a CCD camera and the feature extraction of the vehicle motion are shown in Figure 4b and c, respectively. When the microvehicle starts to move to the target point, the states of the obstacle-avoidance (OA) behaviors in the front, right, and left are far since the dynamic obstacle is distant (see details in the Supporting Information). When the dynamic obstacle is approaching the microvehicle, the states of OA behaviors in the front, right, and left become middle, far, and near, respectively. The microvehicle will turn left to avoid the dynamic obstacle. The real-time turn angle can be calculated by the fuzzy logic based path planner. When the microvehicle is far from the dynamic obstacle, the states of OA behaviors in the front, right, and left become far. The microvehicle locomotes toward the target point under the control of the AI planner (see Supplementary Video 3). Another example is presented in Figure 4d, with both the target point and the dynamic obstacle on the top, and the

ments thus requires different techniques that guide the movement through narrow corridors. The Dijkstra algorithm is an attractive algorithm that is mainly used for determining the shortest and optimal paths.44 The algorithm modifies the breadth-first strategy by expanding the lowest-cost leaf node first, as measured by the path cost function. Figure 3b−e demonstrate the actual navigation of a vehicle in a chiral micromaze containing a single exit. The microvehicle is initially prelocated at the center of the chiral maze. Trajectory images from the CCD camera and after feature extraction of the autonomous microvehicle navigation are shown in Figure 3d and e, respectively. Due to the presence of noise, the microvehicle can drift from the planning path. However, the AI planner is able to measure the error and steer the microvehicle simultaneously. Under autonomous navigation, the microvehicle can be guided to the exit of the chiral micromaze without any collision (see Supplementary Video 2). The autonomous navigation system can also steer the microvehicle in a complex micromaze structure with multiple possible paths, with the Dijkstra algorithm based path planner selecting the most favorable route among all potential collisionfree paths. Figure 3f−i display the optimized autonomous navigation of a microvehicle passing thorough a micromaze with two possible paths. These images illustrate that the navigation control offers collision-free movement to the target exit destination along the optimal and shorter path (see Supplementary Video 2). Avoiding dynamic obstacles is expected to be a major concern in realistic future nanoscale traffic settings. Our system can sense its surroundings and detect dynamic obstacles, such as other microvehicles. The real-time video tracking and 9271

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ACS Nano microvehicle is moving from the bottom in the opposite direction. Under the real-time fuzzy logic inference, the microvehicle can avoid the dynamic obstacle and locomote toward the target point. Trajectory images (from the CCD camera) of the vehicle locomotion before and after feature extraction under the autonomous navigation system are shown in Figure 4e and f, respectively (see Supplementary Video 3). The fuzzy logic based path planner can also be used for other dynamic obstacle avoidance using different obstacle shapes (e.g., nanowire vehicles). As illustrated in Figure 4g, the target point is on the left, and the nanowire dynamic obstacle vehicle locomotes from the top. The fuzzy logic based path planner performs well toward real-time collision-free path planning (see Supplementary Video 3). Figure 4h and i display the real-time planned path and corresponding trajectory images of the microvehicle movement after such feature extraction, respectively. The microvehicles are able to respond to moving obstacles within 0.05 s and avoid collisions with a speed of 6 body lengths/s. Further improvements in the nanoscale traffic safety under dynamically changing conditions (e.g., sudden stop of one of the vehicles) based on adding autonomous-braking or distance-control capabilities are currently being investigated. The growing sophistication of our autonomously moving microvehicles indicates considerable potential in the biomedicine field. However, the lack of visual recognition based autonomous navigation limits the utility of smart microvehicles for disease diagnostics, delivery of therapeutic agents, and precision surgery. Besides autonomous navigation, our smart microvehicles are capable of identifying the features of biological targets. By integrating a visual recognition capacity, the microvehicle is capable of distinguishing different cells, such as cancer cells and red blood cells, thus enabling the precision control of the microvehicle toward the desired biological target. As shown in Figure 5a, our approach consists of three steps, including sensing the red blood cells and cancer cells through visual recognition, determining the collision-free trajectories, and instructing the magnetic field generator to steer the microvehicle along the proposed trajectory toward cancer cells. The original images from CCD of cancer cells, red blood cells, and mixed cells are shown in Figure 5b, c, and d, respectively. The features of the red blood cells and cancer cells, particularly their size and morphology, were extracted using Canny edge detection.49 The first step of the Canny algorithm is to smooth the image using a two-dimensional Gaussian function. The second step is to calculate the magnitude and direction of the image gradient. After acquiring the gradient magnitude image, the system performs nonmaximum suppression on the image to accurately define the edges. Finally, the edges are checked and connected. The binary images of the cancer cells, red blood cells, and mixed cells are shown in Figure 5b, c, and d, respectively. As shown Figure 5e, f, and g, the diameter of the red blood cells and cancer cells are 5 and 20 μm, respectively. As shown in Figure 5h, the red blood cells and cancer cells can be distinguished by the AI planner by recognizing differences in their shape and size. The red blood cells and cancer cells are regarded as the obstacle and goal point, respectively, i.e., surrounding the cancer cells and red blood cells by attractive and repulsive potential functions, respectively. An optimal collision-free path is obtained between the start and goal points. The motion controller performs as a magnetic generator to steer the microvehicle along the generated path. Figure 5i displays the actual trajectory of the microvehicle steered by the autonomous navigation system toward the cancer cells. This

Figure 5. Feature recognition ability of a smart microvehicle for theranostic applications. (a) Schematic illustrating autonomous navigation for a microvehicle in a theranostic application: (I) sensing, (II) decision, and (III) action. (b, c, d) Original images of cancer cells, red blood cells, and mixed cells, respectively. Scale bar, 50 μm. (e, f, g) After identification images of cancer cells, red blood cells, and mixed cells, respectively. Scale bar, 50 μm. (h) Real-time path planning by microvehicle in mixed cells solution. Scale bar, 50 μm. (i) Trajectories steered by autonomous navigation of a microvehicle in the mixed cells solution. Final location of the smart microvehicle in the cancer cell with a red blood cell solution mixture; scale bar, 50 μm. Experiments were performed with 10μm-diameter Janus micromotors propelling in an aqueous solution with 2.5% H2O2 as chemical fuel.

image indicates that the smart microvehicle can successfully recognize the cancer cells and efficiently approach the target (see Supplementary Video 4).

CONCLUSIONS We introduced an advanced autonomous navigation system for microvehicles with high-performance path planning in diverse environments and realistic settings. This system was composed of a camera, AI planner, and magnetic field generator. The microscope-coupled camera can provide real-time visual feedback. The AI planner can detect dynamic obstacles, generate the optimal obstacle-free path, and steer the microvehicle along the generated path. This autonomous navigation system guides the microvehicle to locomote in complex and dynamically changing environments through optimal path planning. By visual recognition, the microvehicle is able to distinguish different biological targets and steer toward its predetermined target in a biological environment. Future efforts should lead to more sophisticated autonomous vehicles, capable of interacting with the environment, and to further improvements and minimization of trajectory errors. These will include features such as steering actuations, autonomous braking, adaptive “cruise” (speed) control, and lane-keeping. By further minimizing trajectory errors, these advances could address major challenges associated with nanoscale traffic jams and high-volume congestion. Such an 9272

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the setup of the autonomous navigation system, and the relative experiments were performed at 37 °C by using a miniheater. Magnetic Field Generator. The magnetic field setup consisted of a laptop computer, a power amplifier, and a set of eight inductors (see Supplementary Figure 1). The output signal from the laptop computer was an analog voltage. A 50 W audio amplifier was used to amplify the analog signal. The amplified signal was used to drive a set of eight inductors, which generated a uniform magnetic field in the plane of the space between inductors. Autonomous Navigation Process. The CCD camera connected with the microscope can provide visual feedback. Images from the CCD camera are inputs to the feature extraction process, which recognizes features and computes the global occupancy map and local occupancy map. The Canny edge detection is employed for feature extraction. Thereafter, these maps are used as inputs to the AI planner for path planning. The artificial potential field, searching algorithms, and fuzzy logic approach are employed to generate a collision-free trajectory in a stationary obstacle, complicated environment, and dynamic obstacle, respectively. Finally, the AI planner selects new waypoints and passes these waypoints to the field generator, which rotates the microvehicle from its current direction to the new direction. Equipment. Template electrochemical deposition of the microvehicles was carried out with a CHI 660D potentiostat (CH Instruments, Austin, TX, USA). Videos were captured at 45 frames per second by an upright optical microscope (Nikon Eclipse Ni−U), coupled with a 40× objective (CFI Plan Fluor, Nikon, NA 0.75) and a Hamamatsu C11440 digital camera. The autonomous navigation system is run with a Thinkpad P50 (Lenovo Group Beijing, China). A compact 50 W audio amplifier (SMSL, SA-S3) was used to amplify the signal. The magnetic field was generated by 10 mH iron core inductors (Erse Audio, 266-580).

innovative autonomous navigation approach thus dramatically enhances the intelligence of micro/nanorobotic systems toward practical applications in diverse biomedical operations and nanoscale manipulation.

METHODS/EXPERIMENTAL Synthesis of Smart Microvehicles. The smart microvehicles were prepared by using silica microspheres (with diameters of 4.74 and 10 μm, Bangs Laboratories, Fishers, IN, USA) as the base particles. For the microvehicle, the 4.74 and 10 μm silica microspheres were placed onto glass slides and coated with a 20 nm titanium layer at 1 Å s−1, a 80 nm nickel layer at 2 Å s−1, and a 10 nm platinum layer at 1 Å s−1 using a Temescal BJD 1800 e-beam evaporator. After a brief sonication in ultrapure water, the microvehicles were released from the glass slide and dispersed into ultrapure water. The microvehicles were stored in ultrapure water until use. Synthesis of Dynamic Obstacles. The Janus microvehicle based dynamic obstacles were prepared by using silica microspheres (10 μm mean diameter, Bangs Laboratories, Fishers, IN, USA) as the base particles. The 10 μm silica microspheres were placed onto glass slides and coated with a 20 nm titanium layer at 1 Å s−1 and a 10 nm platinum layer at 1 Å s−1 using a Temescal BJD 1800 e-beam evaporator. After a brief sonication in ultrapure water, the Janus microvehicle dynamic obstacles were released from the glass slide, dispersed into ultrapure water, and stored in the ultrapure water until use. The nanowire motor based dynamic obstacles composed of Au and Pt segments were prepared by electrodepositing the corresponding metals into a 400 nm polycarbonate (PC) membrane template (catalogue no. 800282; Whatman, Clifton, NJ, USA). A thin silver film was first sputtered on the back side of the PC membrane to serve as a working electrode. The membrane was assembled in a plating cell with aluminum foil serving as an electrical contact for the subsequent electrodeposition. A sacrificial silver layer was electrodeposited into the branched area of the membrane using a silver plating solution (1025 [email protected] troy per gallon; Technic Inc., Anaheim, CA, USA) and a total charge of 3.0 C at −0.90 V (versus Ag/AgCl, in connection to a Pt wire counter electrode). This was followed by an electrodeposition of gold for 1.5 C from a gold plating solution (Orotemp 24 RTU RACK; Technic Inc.) at −1.0 V. Subsequently, the Pt segment was galvanostatically deposited at −2 mA for 120 min using a platinum plating solution (Platinum RTP; Technic Inc.). The sputtered and sacrificial silver layers were simultaneously removed by mechanical polishing using cotton-tipped applicators soaked with 35% HNO3. Then, the PC membrane was removed in methylene chloride for 15 min to completely release the nanowire vehicle based dynamic obstacles. The corresponding nanowire dynamic obstacle vehicles were collected by centrifugation at 6000 rpm for 1 min and washed repeatedly with methylene chloride (two times), followed by ethanol (three times) and ultrapure water (three times). In Vitro Experiment. The culturing of HeLa cells was carried out according to the standard cell culture procedure. Briefly, the HeLa cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM), 1% penicillin and streptomycin, and 10% fetal calf serum at 37 °C in an atmosphere of 5% CO2. The cells were diverted to a 60 mm polystyrene (PS) Petri dish for test by the standard trypsin technique. The red blood cells were collected from Harbin Institute of Technology hospital and rinsed with phosphate-buffered saline (PBS) three times. Prior to the movement of the microvehicle in the cell media, the HeLa cells, cultured in a 60 mm PS Petri dish, and the red blood cells were washed twice with PBS to remove the DMEM medium and plasmas, respectively, followed by the addition of the red blood cells into the HeLa media. Then PBS containing 2.5% H2O2 and the microvehicle were incubated with the HeLa/red blood cell media. The viability of HeLa cells was evaluated by staining the cells with propidium iodide, which indicates that most of the HeLa cells are viable after 30 min. Therefore, the Petri dish was transferred directly to

ASSOCIATED CONTENT S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.7b04525. Additional figures as described in the text (PDF) Navigation capacity for guiding a microvehicle around a stationary obstacle (AVI) Autonomous navigation of a microvelicle in a complicated environment (AVI) Navigation capacity for guiding a microvehicle with a dynamically moving obstacle (AVI) Smart microvehicle for theranostic applications (AVI)

AUTHOR INFORMATION Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Zhiguang Wu: 0000-0002-0570-0757 Joseph Wang: 0000-0002-4921-9674 Author Contributions §

T. Li, X. Chang, Z. Wu, and J. Li contributed equally to this work. Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS This project received support from National Natural Science Foundation of China (51521003 and 51175129), Key Laboratory of Microsystems and Microstructures Manufacturing of Ministry of Education (2016KM004), Self-Planned Task 9273

DOI: 10.1021/acsnano.7b04525 ACS Nano 2017, 11, 9268−9275

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ACS Nano

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of State Key Laboratory of Robotics and System (HIT) (SKLRS 201706A), Program of Introducing Talents of Discipline to Universities (Grant No. B07018), and the U.S. Defense Threat Reduction Agency Joint Science and Technology Office for Chemical and Biological Defense (Grant No. HDTRA1-14-1-0064).

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DOI: 10.1021/acsnano.7b04525 ACS Nano 2017, 11, 9268−9275

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DOI: 10.1021/acsnano.7b04525 ACS Nano 2017, 11, 9268−9275