A model of ant route navigation driven by scene familiarity

Abstract

In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints.

Figure 1. Navigating with a perfect memory.

A) Three separate routes (red lines) learned in an environment containing both small and large objects. For each of the three routes, that consisted of between 700 and 980 views taken every 1 cm, we show three recapitulations (black lines). During route recapitulations the headings at each step were subject to normally distributed noise with a standard deviation of . The panels to the right of the main figure show example views from points (indicated by squares) along the training route. B,C,D) Various sections of the middle route at a variety of different scales. The figures show the result of running the navigation algorithm at each point within a grid and indicate what action would be taken by an agent placed at that location. The white line indicates the training path and the white arrows indicate the directions that would be chosen from those locations. The underlying pseudocolour plot indicates the quality of the best match to the stored views for each position, with darker hues indicating a better match.

Figure 2. Including learning walks prevents return paths from overshooting the goal.

A) Without a learning walk the simulated ant overshoots and carries on in the direction it was heading as it approached the nest location. B) By including the views experienced during a learning walk the simulated ant, instead of overshooting, gets repeatedly drawn back to the location of the nest. Red lines training paths, black lines recapitulations.

Figure 3. Navigating using a trained artificial neural network to assess scene familiarity.

A) Successful return paths for three different routes. The panels to the right of the main figure show example views from points (indicated by squares) along the training route. B,C,D) Various sections of the middle route at a variety of different scales. The pseudocolour plots indicate the familiarity of the best view as was output from the trained network, with darker hues indicating increased familiarity. Conventions as in Figure 1.

Figure 4. Navigational performance in a sparse environment with a tussock density of 0.05 .

The left panel shows the training (red) and test (black) paths for a 12 m route. The right panel shows example views from points (indicated by squares) from the training route. The combined learning walk and training route consisted of 520 views that were used to train the network.

Figure 5. Navigational performance in a cluttered environment with a tussock density of 0.75 .

The left panel shows the training (red) and test (black) paths for a 12 m route, squares indicate points where example views from the training run (right panel) are taken from. The combined learning walk and training route consisted of 520 views that were used to train the network.

Figure 6. Route following improves with experience.

Performance improves as more training runs are performed. Performance is shown following one, two, four and eight training runs. In each figure the training runs used for learning are shown in red while the attempts to recapitulate the route are shown in black. As previously, noise is added to paths during route recapitulation. Of the 4 attempts (black lines) shown in each panel 2, 4, 3 and 4 were successful after one, two, four and eight runs respectively.

Figure 7. Learning multiple routes.

A) Route recapitulation performance (black lines) for each of three routes (red lines) that are learned with the same network. Testing of each of the routes is performed immediately following training on that route and prior to any subsequent learning. The order in which the routes were learnt is indicated by the numbers next to the training routes. B) Performance on the first two routes following learning of all routes, indicating that the route knowledge gained during the first two phases of learning is retained. Having learnt all 3 routes the network must encode 30 m of route information. This increases the likelihood of visual aliasing as is evidenced by the failed recapitulations following learning of all three routes.

Figure 8. The simulation environment.

A,B) Two views of a typical simulated environment used in our experiments. In B the small squares indicate the positions from which the views that are shown in C are taken. C) Five example views taken approximately 2 m apart along a typical route used for learning. The views are oriented so that North (straight up in B) is at the centre of the unwrapped images. D) Typical high-resolution view of the world from an ant's perspective. E) Low-resolution representation of the view shown in D.

Figure 9. Constructing tussocks from a base model.

A) Side and top view of the base model. B) Side and top view of randomly perturbed base model forming a tussock.

Figure 10. Trees, bushes and the distant panorama.

A) Randomly generated tree. B) Randomly generated bush. C) Randomly generated distant panorama.

Figure 11. Path integration modulated by obstacle avoidance.

A,B,C) Obstacle avoidance is achieved by biasing movements towards low points on the horizon. D) A Gaussian distribution is centred on the home direction. E) The Gaussian is multiplied by the proportion of sky raised to the power of 4 and then normalised. This distribution is then sampled from to determine a movement direction.

Figure 12. Artificial learning walks.

The artificial learning walks are structured so that the outbound sections of the paths are curved while the returns are straight. Behavioural modulation of learning is achieved as views are only consistent during the straight inbound sections.

Figure 13. The effects of noise.

A) A random walk with normally distributed noise with a standard deviation of added to the current heading at each timestep and a stepsize of 10 cm. B) A directed walk with a fixed heading of 0 and normally distributed noise with a standard deviation of added at each timestep with a stepsize of 10 cm.

Figure 14. The Infomax model.

Circles represent units and arrows denote connections between the input units on the left and the novelty units on the right. There is no output from the network as such since the response of the network is a function of novelty unit activations. Following we therefore do not draw an output layer.