Mark Blanchard

Collision avoidance using a model of the locust LGMD neuron

Abstract The lobula giant movement detector (LGMD) system in the locust responds selectively to objects approaching the animal on a collision course. In earlier work we have presented a neural network model based on the LGMD system which shared this preference for approaching objects. We have extended this model in order to evaluate its responses in a real-world environment using a miniature mobile robot. This extended model shows reliable obstacle detection over an eight-fold range of speeds, and raises interesting questions about basic properties of the biological system.

IQR: a distributed system for real-time real-world neuronal simulation

Abstract IQR is a new simulator which allows neuronal models to control the behaviour of real-world devices in real-time. Data from several levels of description can be combined. IQR uses a distributed architecture to provide real-time processing. We present the key features of IQR and highlight successful projects which have used this simulator.

Using a mobile robot to study locust collision avoidance responses.

The visual systems of insects perform complex processing using remarkably compact neural circuits, yet these circuits are often studied using simplified stimuli which fail to reveal their behaviour in more complex visual environments. We address this issue by testing models of these circuits in real-world visual environments using a mobile robot. In this paper we focus on the lobula giant movement detector (LGMD) system of the locust which responds selectively to objects which approach the animal on a collision course and is thought to trigger escape behaviours. We show that a neural network model of the LGMD system shares the preference for approaching objects and detects obstacles over a range of speeds. Our results highlight aspects of the basic response properties of the biological system which have important implications for the behavioural role of the LGMD.

Ada: constructing a synthetic organism

Despite immense progress in neuroscience, we remain restricted in our ability to construct autonomous behaving robots that match the competence of even simple animals. The barriers to the realisation of this goal include: the lack of knowledge of system integration issues, engineering limitations and organisational constraints common to many research laboratories. In this paper we describe our approach to addressing these issues by constructing an artificial organism within the framework of the Ada project - a large-scale public exhibit for the Swiss Expo.02 national exhibition.

How accurate need sensory coding be for behaviour? Experiments using a mobile robot

Abstract This paper argues that for those neuronal systems which control behaviour, reliable responses are more appropriate than precise responses. We illustrate this argument using a mobile robot controlled by the responses of a neuronal model of the locust LGMD system, a visual system which responds to looming objects. Our experiments show that although the responses of the model LGMD vary widely as the robot approaches obstacles, they still trigger avoidance responses.

Research, robots, and reality: A statement on current trends in biorobotics

While robotics has benefited from inspiration gained from biology, the opposite is not the case: there are few if any cases in which robotic models have lead to genuine insight into biology. We analyze the reasons why biorobotics has been essentially a one-way street. We argue that the development of better tools is essential for progress in this field.

Collision avoidance in a robot using looming detectors from a locust

The superb aerial performance of flying insects is achieved with comparatively simple neural machinery. We have been investigating the pathway in the locust visual system that signals the rapid approach of an object towards the eye. Two identified neurons have been shown to respond selectively to the images of an object approaching the locusts eye. A neural network based on the input organization of these neurons responds directionally when challenged with approaching and receding objects and reveals the importance of a critical race, between excitation passing down the network and inhibition directed laterally, for the rapid build-up of excitation in response to approaching objects. The strongest response is given to an object approaching on a collision course with they eye, when collision is imminent. Like the neurons, the network is tightly tuned to a collision trajectory. We have incorporated this network into the control structure of a small mobile Kephera robot using the IQR 4021 software we developed. The network responds to looming motion and is effective at evoking avoidance maneuvers in the robot, moving at speeds from 1-12.5cm/s. Our aim is to use the circuit as an artificial looming detector for use in moving vehicles.