Behaviour Based Mobile Robot Navigation with Dynamic Weighted Voting Technique

Abstract

Designing a mobile robot that operates in our everyday environment is a challenging task. Complexity of the environment set strict requirements for both the hardware and software components of the robot. A robot needs sensors and sensor data processing to keep updating the environment. Furthermore, the robot has to integrate task execution with fast reaction to unexpected situations. These problems are fundamental to embodied autonomous systems that have to interact with unknown dynamic environments. To overcome this problem, various types of architectural framework of mobile robot have been introduced. These methods range from centralized sense-model-plan-act architectures to distributed behaviour based architectures. Behaviour-based architecture has emerged as an alternative to traditional approaches for designing autonomous mobile robots (Maes, 1989). It consists of a collection of task-achieving modules or behaviours, which achieve goals and run independently. Each behaviour can take inputs from the robot sensors and send outputs to the robot actuators. Intelligence emerges from the interaction of the behaviours of the system. Thus the coordinator plays an important role to combine the outputs from several conflicting behaviours. This is known as the action selection problem (ASP) and following is the definition of ASP: “How can such an agent select ‘the most appropriate’ or ‘the most relevant’ next action to take at a particular moment, when facing a particular situation?” (Pirjanian, 1998). A dynamic weighted voting technique is introduced to solve the problem in multiple behaviour coordination. It proposes a satisfactory mechanism for action selection that covers the three criteria, namely capability of dealing with multiple problems, multi-valued behaviour, and dynamic priority. The use of voting technique for command fusion allows the mobile robot to deal with multiple problems. It takes a shared control approach where each behaviour module concurrently shares control of the mobile robot by generating votes for every possible motor command. A centre arbiter will then perform command fusion to choose the most appropriate action. Besides, the generated votes are between 0 and 1, with vote zero being the least desired action and vote one is the most desired action. It employs the concept of multi-value rather than simple binary value. The votes are generated in this manner to show the possibility for each action to achieve behaviour’s goal. With the weight generation module, the behaviours’ weights are generated based on the readings from various sensors. In different situations, the behaviours will have different weights. Therefore, the priority of each