A simple approach to the control of locomotion in self-reconfigurable robots

Abstract

In this paper we present role-based control which is a general bottom-up approach to the control of locomotion in self-reconfigurable robots. We use role-based control to implement a caterpillar, a sidewinder, and a rolling track gait in the CONRO self-reconfigurable robot consisting of eight modules. Based on our experiments and discussion we conclude that control systems based on role-based control are minimal, robust to communication errors, and robust to reconfiguration.

Introduction

Reconfigurable robots are robots made from a possibly large number of independent modules connected to form a robot. If the modules from which the reconfigurable robot is built are able to connect and disconnect without human intervention the robot is a self-reconfigurable robot. Refer to Fig. 1 for an example of a module of a self-reconfigurable robot or refer to one of the other physical realized systems described in [7], [8], [10], [11], [12], [13], [14], [15], [17], [21], [23].

Several potential advantages of self-reconfigurable robots over traditional robots have been pointed out in literature:

• Versatility. The modules can be combined in different ways making the same robotic system able to perform a wide range of tasks.

• Adaptability. While the self-reconfigurable robot performs its task it can change its physical shape to adapt to changes in the environment.

• Robustness. Self-reconfigurable robots consist of many identical modules and therefore if a module fails it can be replaced by another.

• Cheap production. When the final design for the basic module has been obtained it can be mass produced. Therefore, the cost of the individual module can be kept relatively low in spite of its complexity.

Self-reconfigurable robots can solve the same tasks as traditional robots, but as Yim et al. [23] point out; in applications where the task and environment are given a priori it is often cheaper to build a special purpose robot. Therefore, applications best suited for self-reconfigurable robots are applications where some leverage can be gained from the special abilities of self-reconfigurable robots. The versatility of these robots make them suitable in scenarios where the robots have to handle a range of tasks. The robots can also handle tasks in unknown or dynamic environments, because they are able to adapt to these environments. In tasks where robustness is of importance it might be desirable to use self-reconfigurable robots. Even though real applications for self-reconfigurable robots still are to be seen, a number of applications have been envisioned [17], [23]: fire fighting, search and rescue after an earthquake, battlefield reconnaissance, planetary exploration, undersea mining, and space structure building. Other possible applications include entertainment and service robotics.

The potential of self-reconfigurable robots can be realized if several challenges in terms of hardware and software can be met. In this work we focus on one of the challenges in software: how do we make a large number of connected modules perform a coordinated global behavior? Specifically we address how to design algorithms that will make it possible for self-reconfigurable robots to locomote efficiently. In order for a locomotion algorithm to be useful it has to preserve the special properties of these robots. From the advantages and applications mentioned above we can extract a number of guidelines for the design of such a control algorithm. The algorithm should be distributed to avoid having a single point of failure. Also the performance of the algorithm should scale with an increased number of modules. It has to be robust to reconfiguration, because reconfiguration is a fundamental capability of self-reconfigurable robots. Finally, it is desirable to have homogeneous software running on all the modules, because it makes it possible for any module to take over if another one fails.

It is an open question if a top-down or a bottom-up approach gives the best result. We find that it is difficult to design the system at the global level and then later try to make an implementation at the local level, because often properties of the hardware are ignored and a slow robotic system might be the result. Therefore, we use a bottom-up approach where the single module is the basic unit of design. That is, we move from a global design perspective to a bottom-up one where the important design element is the individual module and its interactions with its neighbors. The global behavior of the system then emerges from the local interaction between individual modules. A similar approach is also used by Bojinov et al. [1], [2] and Butler et al. [4].