Determining the specification of a control system from that of its environment

Well understood methods exist for developing programs from given specifications. A formal method identifies proof obligations at each development step: if all such proof obligations are discharged, a precisely defined class of errors can be excluded from the final program. For a class of closed systems such methods offer a gold standard against which less formal approaches can be measured. For open systems -those which interact with the physical world- the task of obtaining the program specification can be as challenging as the task of deriving the program. And, when a system of this class must tolerate certain kinds of unreliability in the physical world, it is still more challenging to reach confidence that the specification obtained is adequate. We argue that widening the notion of software development to include specifying the behaviour of the relevant parts of the physical world gives a way to derive the specification of a control system and also to record precisely the assumptions being made about the world outside the computer.

Branching genes are conserved across species. Genes controlling a novel signal in pea are coregulated by other long-distance signals

Physiological and genetic studies with the ramosus (rms) mutants in garden pea (Pisum sativum) and more axillary shoots (max) mutants in Arabidopsis (Arabidopsis thaliana) have shown that shoot branching is regulated by a network of long-distance signals. Orthologous genes RMS1 and MAX4 control the synthesis of a novel graft-transmissible branching signal that may be a carotenoid derivative and acts as a branching inhibitor. In this study, we demonstrate further conservation of the branching control system by showing that MAX2 and MAX3 are orthologous to RMS4 and RMS5, respectively. This is consistent with the longstanding hypothesis that branching in pea is regulated by a novel long-distance signal produced by RMS1 and RMS5 and that RMS4 is implicated in the response to this signal. We examine RMS5 expression and show that it is more highly expressed relative to RMS1, but under similar transcriptional regulation as RMS1. Further expression studies support the hypothesis that RMS4 functions in shoot and rootstock and participates in the feedback regulation of RMS1 and RMS5 expression. This feedback involves a second novel long-distance signal that is lacking in rms2 mutants. RMS1 and RMS5 are also independently regulated by indole-3-acetic acid. RMS1, rather than RMS5, appears to be a key regulator of the branching inhibitor. This study presents new interactions between RMS genes and provides further evidence toward the ongoing elucidation of a model of axillary bud outgrowth in pea.

Functional analysis of a real-time protocol for networked control systems

Traditional real-time control systems are tightly integrated into the industrial processes they govern. Now, however, there is increasing interest in networked control systems. These provide greater flexibility and cost savings by allowing real-time controllers to interact with industrial processes over existing communications networks. New data packet queuing protocols are currently being developed to enable precise real-time control over a network with variable propagation delays. We show how one such protocol was formally modelled using timed automata, and how model checking was used to reveal subtle aspects of the control system's dynamic behaviour.

Multi-robot control in highly dynamic, competitive environments

The control and coordination of multiple mobile robots is a challenging task; particularly in environments with multiple, rapidly moving obstacles and agents. This paper describes a robust approach to multi-robot control, where robustness is gained from competency at every layer of robot control. The layers are: (i) a central coordination system (MAPS), (ii) an action system (AES), (iii) a navigation module, and (iv) a low level dynamic motion control system. The multi-robot coordination system assigns each robot a role and a sub-goal. Each robot’s action execution system then assumes the assigned role and attempts to achieve the specified sub-goal. The robot’s navigation system directs the robot to specific goal locations while ensuring that the robot avoids any obstacles. The motion system maps the heading and speed information from the navigation system to force-constrained motion. This multi-robot system has been extensively tested and applied in the robot soccer domain using both centralized and distributed coordination.