A Comparison between Mouse, In Silico, and Robot Odor Plume Navigation Reveals Advantages of Mouse Odor Tracking

Mice successfully locate odor source within a non-turbulent chaotic flow chamber

To test mouse navigation within an airborne odor plume, we built a 1 × 1 × 0.3 m flow chamber behavioral arena based on that used by Connor et al. (2018). We introduced two honeycombs on either end to laminarize the airflow established by a vacuum at the outlet end. To generate a controlled complex odor plume within this flow chamber, we inserted a turbulence grid in front of the honeycomb at the inlet end (Fig. 1A). A flow rate of 5 cm/s was established within the flow chamber. For the purposes of this study, we refer to this flow chamber as a SOL. Three odor ports at the inlet end of the flow chamber released odor, generating plumes. We measured the time averaged concentration of odor across the flow chamber within each of the three plumes using a miniature photoionization detector, miniPID (Fig. 1C).

We trained a group of mice on a task to navigate to the source of these airborne odor plumes within the SOL. On any given trial, an odor plume was established from one of the three odor ports for 30 s before the insertion of the animal into the behavioral arena. The task structure required water-regulated mice to locate an odor port releasing IAA (3% in mineral oil) within 45 s to receive a sucrose water reward from an adjacent lick spout (Fig. 1B; Movie 3). Other studies aimed at understanding rodent navigation within airborne odor plumes have found that with experience animals preferentially use a localization strategy in which they serially explore all possible odor source locations, showing a shift away from using solely odor-based cues (Bhattacharyya and Bhalla, 2015; Gire et al., 2016). To ensure that the mice in this study relied only on odor information, we terminated trials when the mouse reached one of three odor ports, providing water reward only if the odor-releasing port (i.e., not the two clean air-releasing ports) was reached. This behavioral design incentivizes mice to make a decision regarding odor source location, rather than testing all possible sources.

Before being tested on this task, animals were trained to associate the localization of an odor port releasing odor with delivery of a sucrose water reward. Animals were able to learn the task following a 6 d of this training and performed consistently above chance starting the eight day of testing (Fig. 1D; one-tailed two-sample t test with Holm–Sidak correction for multiple comparisons, p = 0.047 for day 6, p = 0.047 for day 8, p = 0.0026 for day 9, p = 0.0013 for day 10, p = 0.018 for day 11, p = 0.033 for day 12, p = 0.0026 for day 13, p = 0.047 for day 14, n = 4 mice). Thus, the testing days were classified into two phases of 7 d each, the early phase and the late phase. Thigmotaxis (wall-hugging) behavior indicates an anxiety-like state in mice. Mice decreased the percentage of the 45-s trial spent engaging in wall-hugging behavior over time (Fig. 1E; paired one-tailed t test, late phase vs early phase difference: –27.03 ± 2.79, p = 0.0012, n = 4 mice^a).

Mouse performance remains robust with increased complexity but shows a shift in strategy

To test the effect of increased complexity on odor localization performance, we removed the honeycomb at the inlet side of the flow chamber (Extended Data Fig. 1-1). This allows for the introduction of ambient air complexity into the behavioral arena in addition to that caused by the turbulence grid. We refer to this odor environment as “non-turbulent chaotic” as well as “complex.” When comparing the two environments, we refer to the honeycomb condition interchangeably with “low-complexity” and the no honeycomb condition with “high-complexity” environments. The SD s of the 2-min odor concentration time series at each midline downstream location (six repeats each) were all significantly increased by roughly two- to four-fold (3.9, 2.3, 1.8, and 2.1 times the SD with inlet honeycomb at 10, 30, 50, and 60 cm downstream from the odor tube, respectively). The SD normalized by mean odor concentration was also significantly increased at 10 and 30 cm from the odor tube by 4.0- and 1.9-fold, respectively. Note that instrument noise contribution to the SD was negligible.

Animals perform at a significantly higher % success in the late phase when compared to the early phase and show no change in performance between the late phase and no honeycomb condition (Fig. 2A; paired t test one-tailed, late phase vs early phase difference = 11.65 ± 3.1%, p = 0.016^b, paired t test two-tailed, no honeycomb vs late phase difference = –1.92 ± 2.74%, p = 0.53^c, n = 4 mice). This shows a significant improvement of performance over time in the same odor environment and that with increased odor plume complexity animals show consistent task performance. Additionally, no difference in performance is seen across ports between the late phase and the no honeycomb condition, although there was a small effect of port number (Extended Data Fig. 2-1A; two-way ANOVA, main effect of plume complexity, p = 0.8, main effect of port = 0.039, n = 4 mice). This effect of port number may be because the animals were lick-trained on odor port 1 (although post hoc t tests with Bonferroni correction for multiple comparisons do not reveal a significant difference between ports, port 1 vs port 2 difference: 26.2 ± 10.23%, p = 0.0917^d, port 1 vs port 3 difference: 28.35 ± 10.23%, p = 0.065^e, port 2 vs port 3 difference: 37.67 ± 10.23%, p > 0.99^f, n = 4 mice). To ensure that animals were using odor information for this task, we tested them on a set of ∼30 trials without odor between the late phase and no honeycomb condition. Animals performed at chance levels without odor and their performance was significantly lower than that during the late phase or no honeycomb phase (Fig. 2A; paired t test one-tailed, no odor vs late phase difference: –31.32 ± 6.24, p = 0.0076^g, no odor vs no honeycomb difference: –29.4 ± 5.22, p = 0.0055^h, n = 4 mice).

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Figure 2.

Mice change navigation behavior with increased experience and odor environment complexity. A, Performance (average % successful trials over sessions) across testing phases. Mice are tested on a no-odor condition in addition to the phases with a honeycomb and condition without a honeycomb. Chance level performance is 25% as animals have three ports as options and are not required to choose an odor port on trials. B, Pathlength to target odor port on successful trials. C, Time to target odor port on successful trials. D, Time to target on successful trials over testing days. E, Example traces of successful navigation from the late phase and no honeycomb phase. Traces are color scaled based on velocity. F, Total angle sum of trajectories of late phase and no honeycomb condition. Total angle sum is calculated by using the total sum of angles on turns from frame-to-frame. G, Velocity on successful trials of late phase and honeycomb condition (left). Velocity over the course of successful trajectories resampled to 675 frames (right). H, Change in nose angle per frame (15 Hz) over the course of successful trajectories resampled to 675 frames (left). Change in nose angle on successful trials of late phase and no honeycomb condition (right). I, Ratio of path distance based on nose to path distance based on center of body (left). Example trajectories with ratios of 1.35 (top) and 1.08 (bottom). All plots show mean ± SEM, n = 4 mice. See also Extended Data Figure 2-1. *p < 0.05, **p < 0.01.

We recorded behavior during trials using a camera placed above the flow chamber and imaged through the transparent lid of the behavioral arena. We found that on successful trials, the distance and time to the target odor port decreases between the early and late phase (Fig. 2B–D; paired t test two-tailed, distance to target of late phase vs early phase difference: –60.79 ± 16.8 cm, p = 0.036ⁱ, time to target of early phase vs late phase difference: –4.6 ± 0.73 s, p = 0.008^j, n = 4 mice), showing that animals are taking shorter and faster routes to the correct odor port over time. Additionally, the early phase shows a significant negative linear trend of time to correct odor port over time, whereas the late phase does not show a significant decline. Thus, their behavior has stabilized when entering into the late phase (Fig. 2D; linear regression, R ² = 0.62 early phase, p = 0.0357, R ² = 0.006 late phase, p = 0.71, n = 4 mice).

We measured several parameters associated with the animals’ behavior during the trial, as the level of odor plume complexity could affect the path taken and parameters modulated during the animals’ trajectories. We found that when the honeycomb was removed and complexity was increased, the distance to the target on successful trials remained the same as the late phase, but the time to the target significantly decreased (Fig. 2B,C; paired t test two-tailed, distance to target no honeycomb vs late phase difference: –3.52 ± 7.05 cm, p = 0.65^k, time to target no honeycomb vs late phase difference: –1.99 ± 0.57 s, p = 0.039^l, n = 4 mice). Additionally, the animals traveled at a higher velocity when navigating a more chaotic plume (Fig. 2E,G; paired t test two-tailed, no honeycomb vs late phase difference: 8.044 ± 2.37 cm/s, p = 0.043^m, n = 4 mice).

Casting involving lateral full-body or head movement during odor-based navigation is a behavioral strategy that has been extensively characterized and found to be conserved across several species (Vickers, 2000; Grasso, 2001). Invertebrates including moths, flies, and cockroaches implement this zig-zagging behavior when localizing odor within an airborne odor plume, particularly when attempting to reacquire the odor stream (David et al., 1983; Kennedy, 1983; Baker and Haynes, 1987; Kuenen and Cardé, 1994; Grasso, 2001; Cardé and Willis, 2008; Gomez-Marin et al., 2011; van Breugel and Dickinson, 2014). Additionally, mammals, including both rodents and humans, display lateral head movements when tracking odor trails (Porter et al., 2007; Khan et al., 2012; Catania, 2013). Here we measured “casting” using two parameters. The first is the path curvature as measured by the absolute total sum of turning angles during a trial. Animals did not display any difference in turning behavior between the late phase and no honeycomb condition (Fig. 2F; Extended Data Fig. 2-1B; paired t test two-tailed, no honeycomb vs late phase difference: –27.19 ± 13.39°, p = 0.14ⁿ, two-way ANOVA, total angle sum main effect of plume complexity, p = 0.92, total angle sum main effect of port number, p = 0.63; n = 4 mice). Average total sum of turning angles for both conditions are below 360°, and thus mouse turning behavior remains below a full rotation during navigation, suggesting minimal full-body casting. This lack of casting behavior is in alignment with previous observations in rodents navigating in odor plumes (Bhattacharyya and Bhalla, 2015; Gire et al., 2016). The second form of casting measured was the change in nose angle, thereby measuring sweeps in head movement during odor localization. We found that mice show modest changes in nose angle which are slightly higher when the chaotic nature of the odor plume is increased (Fig. 2H; paired t test two-tailed, no honeycomb vs late phase difference: 2.94 ± 0.83°, p = 0.04^o, n = 4 mice). Additionally, the ratio of the trial pathlength as measured by the nose position to that measured by the body position shows that nose pathlength is greater than body pathlength (Fig. 2I; one-sample t test, μ = 1, late phase mean: 1.14 ± 0.004, p < 0.0001^p, no honeycomb mean: 1.20 ± 0.02, p = 0.0016^q, n = 4 mice). Thus, this suggests that mice do not display lateral body movements, but do exhibit sweeping movements with their head during odor plume navigation. However, these head movements appear to be limited to the initial phase of olfactory search behavior (Fig. 2H).

Interestingly, trajectories from one test session show few differences between the late phase and no honeycomb condition (Fig. 5A). Additionally, animals’ path linearity, as measured by the fraction of distance of a straight-line path to that of the actual path, did not vary across rewarded ports, showing consistency across tested plumes (Extended Data Fig. 2-1C; two-way ANOVA, linearity main effect of plume complexity, p = 0.81, linearity main effect of port number, p = 0.9, n = 4 mice). Overall, these results suggest that increased odor plume complexity does not affect odor navigation performance. However, animals do alter their strategy when navigating a more chaotic plume, where a faster speed may be beneficial for odor localization, whereas modulating parameters that affect trajectory structure may not be as important.

Model-based odor navigation

To compare mouse odor navigation with simple odor localization algorithms, we developed an in silico-simulated robot. The simulated robot has two odor sensors, with a separation distance that can be varied, and can make comparisons between the odor signals at the left and right sensor. It has a virtual frame and moves through a virtual odor plume with a heading θ. If the simulated robot approaches the wall of the virtual arena, it will take corrective measures to reorient toward the open arena (Fig. 3A). We tested this in silico model in a virtual SOL with a center and corner port, analogous to that in which we tested the mice (Fig. 3B). We tested the simulated robot navigation starting at the center of the arena with start angles varying from 90^° to 270^° at 3.6^° increments. Acetone PLIF data were used as the odor plume input for the virtual arena, obtained from Connor et al. (2018). To assess the effect of odor plume complexity on the behavior of our model, we tested the simulated robot using either a static odor plume (i.e., the average of 4 min of odor plume data) or using a dynamic odor plume with real-time fluctuations (Fig. 3C).

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Figure 3.

In silico models show decreased performance with increased odor environment complexity. A, Model virtual chassis moves through space with a heading, θ. Two sensors are separated at a distance ℓ_s and an angle γ (left). If the center of the model reaches d_threshold = 10 cm of the wall, the model will take corrective measures (right; described in Materials and Methods). B, Model is tested at angles ranging from 90° to 270° with a start position in the center of the arena. Model is tested on two plumes, one originating from a center port and one from a corner port. C, Sample frame depicting instantaneous concentration of the dynamic plume normalized to odor source (left), and an image of the stationary concentration gradient in static plume normalized to odor source (right). D, Performance (average % success of all start angles ± SEM) across code and sensor distance for center target port (left) and corner target port (right); n = 20 simulations. E, Linearity score (calculated as the ratio of the Euclidean distance between start point and end point of trajectory and the actual pathlength) across code and sensor distance for center target port (left) and corner target port (right). Plot shows mean linearity score ± SEM, n = 20 simulations. See also Extended Data Figures 3-1, 3-2, 3-3, 3-4, 3-5, and 3-6. *p < 0.05, **p < 0.01, ***p < 0.001.

We created two navigational algorithms to test in silico odor localization. These algorithms were designed to incorporate a minimal interpretation of stereo smell while, in one case, also incorporating features to resolve the fluctuating nature of our odor plume. For both algorithms a baseline reading is collected for each sensor as the average of four readings over 1 s. These two baselines are then averaged to be used for odor-based navigation. In the first algorithm, which we refer to as Code A, if the difference between the instantaneous sensor reading at the left sensor and the right sensor, both corrected for the baseline reading, is greater than the threshold (described in Materials and Methods), the model turns left and moves forward for a subsequent reading. If the difference between the right sensor and the left sensor reading, corrected for the baseline, is greater than the threshold, the model turns right and advances. If neither of these conditions are true, the model moves forward.

The most basic model implemented in a robotics approach aimed at odor plume tracking is one in which the robot with a pair of chemical sensors simply moves in the direction of higher concentration. However, this approach may be limited due to the previously described dynamic nature of odor plumes in which the robot can at one moment sense odor that quickly disappears while remaining stationary (Sandini et al., 1993; Kazadi et al., 2000; Lilienthal and Duckett, 2004; Ishida et al., 2012). Models that rely on averaging several frames on odor intake before determining movement may be more successful at determining concentration gradients (Ishida et al., 2001). Using this logic, we created Code B. In this algorithm, if the difference between the average (of the readings of the two sensors) across two time points is greater than a threshold, the model will move forward, as this indicates the simulated robot is moving up the concentration gradient. Otherwise, Code B defaults to the same rules described for Code A.

In silico-simulated robot navigation is affected by increased plume complexity

As previously mentioned, stereo smell is important for odor navigation in both mammals and invertebrates. The distance between olfactory sensors may play a role in the ability of an animal to accurately detect an odor plume and locate the source. We tested the simulated robot in both the static and dynamic odor plumes with two sensor separation distances, 16 and 8 cm. Model Code A performs at a significantly lower success rate in the presence of increased plume complexity at an 8-cm sensor separation distance regardless of active odor port position (Fig. 3D; Extended Data Fig. 3-6; two-tailed t test center port Code A 8-cm static vs center port Code A 8-cm dynamic difference: 8.37 ± 1.1%, p < 0.0001^r, two-tailed t test corner port Code A 8-cm static vs corner port Code A 8-cm dynamic difference: 3.91 ± 0.84%, p < 0.0001^s, n = 20 simulations). Additionally, Code A at 8 cm shows a decrease in trajectory linearity as an average and across starting angles when the plume complexity increases, suggesting that with increased complexity, paths become more winding (Fig. 3E; Extended Data Fig. 3-6; two-tailed t test center port Code A 8-cm static vs center port Code A 8-cm dynamic difference: 0.065 ± 0.007, p < 0.0001^t, two-tailed t test corner port Code A 8-cm static vs corner port Code A 8-cm dynamic difference: 0.023 ± 0.0035, p < 0.0001^u, n = 20 simulations). Model Code B shows a significant decrease in performance with increased plume complexity at a 16-cm sensor separation distance with a center odor plume and an 8-cm sensor separation distance regardless of plume position (Fig. 3D; Extended Data Fig. 3-6; two-tailed t test center port Code B 8-cm static vs center port Code B 8-cm dynamic difference: 20.11 ± 1.1%, p < 0.0001^v, center port Code B 16-cm static vs center port Code B 16-cm dynamic difference: 3.70 ± 1.1%, p = 0.011^w, corner port Code B 8-cm static vs corner port Code B 8-cm dynamic difference: 5.54 ± 0.84%, p < 0.0001^x, n = 20 simulations). Data from both codes show that at an 8-cm sensor separation distance, algorithms are more susceptible to a decrease in performance due to increased odor plume complexity.

Additionally, linearity as an average and across starting angles for Code B decreases with increasing plume complexity, indicating that with either sensor separation distance, paths become less linear with increased complexity (Fig. 3E; Extended Data Fig. 3-6; two-tailed t test center port Code B 8-cm honeycomb vs center port Code B 8-cm no honeycomb difference: 0.15 ± 0.007, p < 0.0001^y, center port Code B 16-cm honeycomb vs center port Code B 16-cm no honeycomb difference: 0.03 ± 0.007, p = 0.0006^z, corner port Code B 8-cm honeycomb vs corner port Code B 8-cm no honeycomb difference: 0.042 ± 0.003, p < 0.0001^aa, n = 20 simulations). Trajectories within the static odor plume are deterministic as there is a fixed odor plume gradient to climb, whereas there was variation in the paths within the dynamic plume, as expected (Extended Data Figs. 3-1, 3-2, 3-3, 3-4, 3-6; Movies 4, 5, 6, 7, 8, 9, 10, and 11). Interestingly, both the success and linearity of Code B at an 8-cm separation distance in the dynamic plume shows periodicity where the success and linearity decrease and rise every 30° of starting angles (Extended Data Fig. 3-6). This periodicity may be attributed to the 30° turn angle implemented in silico and if the simulated robot is capable of rotating to 180° (facing the odor source) using the increment, it will ultimately be more successful and have a straighter path.

When comparing performance across codes, in the static condition, Code A had a significantly lower % success than Code B at an 8-cm sensor separation distance, however Code B performed significantly worse than Code A at a 16-cm sensor separation distance, showing the interaction between code and sensor separation distance (Fig. 3D; two-tailed t test center port Code A 8-cm static vs Code B 8-cm static difference: –13.04 ± 1.1%, p < 0.0001, center port Code A 16-cm static vs Code B 16-cm static difference: 15.22 ± 1.1%, p < 0.0001^bb, n = 20 simulations). In the dynamic condition, just as in the static condition, Code A performs significantly better than Code B at a 16-cm sensor separation distance (Fig. 3D; Code A 16-cm turbulent vs Code B 16-cm turbulent difference: 21.63 ± 1.1%, p < 0.0001^cc, n = 20 simulations). Together, these findings suggest that with a small sensor separation distance Code B is more successful, however at a larger sensor separation distance Code A is more successful.

Difference in trajectories between static and dynamic conditions can be observed in Figure 5C. Our simulated robot was tested using data collected in the SOL at the same starting position as the mice, therefore we can directly compare performance between the two. Model Code A overall performs with a higher % success than the mice, but there is no significant difference between performance of model code B and the mice [Extended Data Fig. 5-1A, left; two-tailed t test low-complexity mouse vs Code A difference: –25.68 ± 8.74%, p = 0.043^dd, high-complexity mouse vs Code A difference: –25.38 ± 8.74%, p = 0.048^ee, low-complexity mouse vs Code B difference: –21.68 ± 8.74%, p = 0.12^ff, high-complexity mouse vs Code B difference: –16.63 ± 8.74%, p = 0.42^gg, n = 4 mice, n = 4 sessions for each model condition (one session for per combination of sensor distance and target odor port)]. Additionally, mice locate the odor source on successful trials significantly faster than both codes [Extended Data Fig. 5-1B, two-tailed t test low-complexity mouse vs Code A difference: –33.75 ± 3.63 s, p < 0.0001^hh, high-complexity mouse vs Code A difference: –36.59 ± 3.63 s, p < 0.0001ⁱⁱ, low-complexity mouse vs Code B difference: –35.25 ± 3.63, p < 0.0001^jj, high-complexity mouse vs Code B difference: –39.01 ± 3.63 s, p < 0.0001^kk, n = 4 mice, n = 4 sessions for each model condition (one session for per combination of sensor distance and target odor port)]. These findings show that although the Code A outperforms a mouse in terms of % success for the low and high plume complexity conditions, both codes show a decrease in within code performance in the presence of increased complexity, a behavioral shift not seen in mice.

Arduino-based robot shows decrease in performance with increased odor plume complexity

To test how our in silico models perform in a real flow chamber, we tested an Arduino-based robot using Code A and Code B in the previously described SOL behavioral arena. We modified the arena to replace lick spouts with LEDs associated with each odor port which were detected by light sensors on the robot to identify if an odor port had been approached. The Arduino-based robot was equipped with optimized gas sensors attached to a fan that actively sucked air through the sensors. In addition, we attached proximity sensors to avoid contact with the walls of the flow chamber. The gas sensors were optimized for response speed by removing the front of steel mesh cap surrounding the front of the sensor, drilling a hole through the pc-board behind the sensor and fitting a small fan on the back of the hole (Extended Data Fig. 4-1A). The responsiveness of the sensor was improved by an order of magnitude: time from stimulus onset (i.e., the first time the signal crosses 2% of peak amplitude) to 75% of peak (t75 O) was 0.67 s in the unmodified sensor but reduced to 0.07 s when modified, being 1.13 s and 0.11 s (t100 O) to reach peak value, respectively (Extended Data Fig. 4-1C). Decay time from peak to 50% of peak (t50 off) was reduced from 2.41 to 0.47 s, and to 25% of peak (t25 off) from 4.96 to 2.14 s, respectively. The distance between these gas sensors could be varied, as well as the angle at which they were oriented.

We tested the robot starting on the midline of the outlet end of the flow chamber for direct comparison with mouse and in silico model behavior. We used six different starting angles with varying active odor ports based on starting angle (Materials and Methods; Fig. 4A). At this starting position, we tested the robot using Code A and Code B with the honeycomb as well as Code B without the honeycomb. Additionally, we recorded behavior at an alternate start position, which cannot be directly compared to the mouse behavior, in which the start angle of the robot was 270° at the far-right corner of the outlet end of the chamber. In this condition the center port was used for plume generation (Extended Data Fig. 4-1D). At this start position, we tested the robot using both Code A and Code B with and without the honeycomb. At both starting positions we tested the robot with sensor separation distances of 8 and 16 cm and sensor angles of 0°, parallel with the front of the robot, and 45°. Additionally, we tested the robot using 70% ethanol instead of IAA, used with mice, to obtain robust odor readings from the robot’s gas sensors. The task structure for the robot odor-based navigation was nearly identical to that of the mouse; however, the robot was allotted 75 s to reach the odor source.

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Figure 4.

Arduino-based robot navigation varies based on start position and odor environment complexity. A, Robot odor navigation flow chamber, modifications to the SOL. Solid arrows represent five starting angles. Odor ports were coupled to LED lights detected by sensors on the robot (indicated by dotted red arrows). B, Performance (average % successful trials over 8 and 16 cm and 0° and 45° gas sensor distance and angles, respectively) across codes (left). Performance based on gas sensor distance and angle for the honeycomb condition (right). C, Example trajectories from 180° (magenta) starting position in A for honeycomb and no honeycomb condition. D, Performance (average % successful trials over 8 and 16 cm and 0° and 45° gas sensor distance and angles, respectively) with the honeycomb based on starting angle and rewarded port for Code A (left) and Code B (right). Bars are color coded and labeled according to the starting angles in A. E, Robot overall linearity score with honeycomb and without honeycomb using Code B. Plot shows data combined over sensor angle and sensor distance for each odor environment condition (left). Linearity score across starting angles and target ports with and without the honeycomb. All plots show mean ± SEM, n = 4 sessions. See also Extended Data Figure 4-1. *p < 0.05, **p < 0.01.

We studied how the behavior of the robot changed when tested with the two algorithms in the presence of increased complexity by removing the honeycomb at the inlet side of the flow chamber, the exact conditions we tested on the mice. Code A showed a decrease in performance at the corner start position when the honeycomb was removed and Code B show a significant decrease in % success with increased complexity at both start positions (Fig. 4B, left, C; Extended Data Fig. 4-1E, left; paired two-tailed t test, corner start Code A no honeycomb vs Code A with honeycomb difference: –62.5 ± 11.09%, p = 0.011^ll, center start Code B no honeycomb vs Code B with honeycomb difference: –19.64 ± 2.43%, p = 0.004^mm, corner start Code B no honeycomb vs Code B with honeycomb difference: –47.5 ± 6.29%, p = 0.0048ⁿⁿ, n = 4 sessions). Additionally, when implementing Code A with the honeycomb, the robot shows a higher success rate at a greater sensor separation for both sensor angles at a center start position and at a 0^° sensor angle at a corner start position (Fig. 4B, right; Extended Data Fig. 4-1E, right). A larger sensor separation distance may be beneficial for the robot navigation using Code A because larger spatial differences in the concentration gradient can be detected. This finding is in line with that of the in silico model.

Performance of the robot also varies based on starting angle. When the center port is active, the robot performs at a higher % success when oriented directly toward the source than when angled 45° away from the source (Fig. 4D, one-way ANOVA port 2, Code A effect of start angle, p = 0.0021, two-tailed t test, Code A 180° vs Code A 135° difference: 75 ± 10.41%, p = 0.017^oo, Code A 180° vs Code A 225° difference: 55 ± 12.58%, p = 0.067^pp, Code B effect of start angle, p = 0.0055, Code B 180° vs Code B 135^° difference: 72.5 ± 12.5%, p = 0.031^qq, Code B 180° vs Code B 225° difference: 47.5 ± 13.77%, p = 0.12^rr, n = 4 sessions). Increased complexity in the odor environment also caused a change in the path characteristics of the robot. For Code B, the path linearity decreased for several start angles (Fig. 4E; two-tailed t test port 1 135° with honeycomb vs port 1 135° no honeycomb difference: 0.17 ± 0.046, p = 0.0063^ss, two-way ANOVA port 2, interaction between starting angle and plume complexity, p = 0.028, port 2 180° with honeycomb vs port 2 180° without honeycomb difference: 0.18 ± 0.051, p = 0.0068^tt, n = 4 simulations).

When compared to in silico paths, Arduino-tested Code B trajectories are significantly more linear than in silico-tested Code B trajectories in both low-complexity and high-complexity environments (Extended Data Fig. 5-1D; two-tailed t test low-complexity robot Code B vs model Code B difference: 0.22 ± 0.071, p = 0.031^uu, high-complexity robot Code B vs model Code B difference: 0.25 ± 0.071, p = 0.01^vv). This discrepancy maybe be due to the wide range of starting angles tested for each odor port using in silico algorithms. Additionally, there is no significant difference between performance of Code B in silico and in the real flow chamber using the Arduino robot (Extended Data Fig. 5-1A, left; two-tailed t test low-complexity robot Code B vs model Code B difference: –11.07 ± 8.74%, p > 0.99^ww, high-complexity robot Code B vs model Code B difference: –23.74 ± 8.74%, p = 0.07^xx, n = 4 sessions). When model performance is determined selectively for the same start angles as tested on the robot, there is no significant difference between performance with low plume complexity between the robot and the model. Additionally, this subset of model data shows that the robot and the model show similar decreases in performance when the honeycomb is removed (Extended Data Fig. 5-1A, right; two-tailed t test low-complexity robot Code B vs model Code B difference: –34.16 ± 10.18%, p = 0.091, high-complexity robot Code B vs model Code B difference: –49.58 ± 9.48%, p < 0.0001, one-tailed t test robot Code B high vs low-complexity difference: –40.41 ± 11.01%, p = 0.028, one-tailed t test model Code B high vs low-complexity difference: –30.83 ± 11.72%, p < 0.0001, n = 4 conditions). Just as in the in silico model, the robot using Code B takes a significantly longer amount of time to reach the odor source on successful trials and has a significantly lower velocity when compared to mice (Extended Data Fig. 5-1B; two-tailed t test low-complexity mouse vs robot Code B time to target difference: –36.49 ± 3.63 s, p < 0.0001^yy, high-complexity mouse vs Code B time to target difference: –41.55 ± 3.63 s, p < 0.0001^zz, low-complexity mouse vs robot Code B velocity difference: 20.93 ± 1.44 cm/s, p < 0.0001^aaa, high-complexity mouse vs robot Code B velocity difference: 29.06 ± 1.44 cm/s, p < 0.0001^bbb, n = 4 mice, n = 4 sessions). Difference in trajectories between static and dynamic conditions can be observed in Figure 5B, Movies 12, 13, 14, 15, 16, 17, 18, and 19. Overall, our results show that when algorithms selected using in silico testing are implemented in a real flow chamber, our findings are comparable to those in silico. Additionally, just as in our in silico model, robot navigation shows a dramatic decrease in performance with increased odor plume complexity that is not observed in mouse behavior.

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Figure 5.

Mouse, robot, and in silico navigation trajectories. A, Mouse trajectories show consistency with increased odor environment complexity. B, Robot trajectories show decreased success on trials for the same testing conditions with increased odor plume complexity, Code B, sensor distance: 8 cm, sensor angle: 0°. C, In silico trajectories (50 trials with start angles ranging from 90° to 270°) show increased unsuccessful trials for the same testing conditions with increased complexity, Code B, sensor distance: 8 cm. See also Extended Data Figure 5-1.