Fundamental Problems of Multiple Mobile Robotics

3.1.2.1

Coordinated Exploration and Mapping

The problem of exploring and mapping an unknown environment is a fundamental subject of

multiple mobile robotics. The goal is to complete the exploration process in a minimum

period of time and with increased mapping accuracy (Simmons, Apfelbaum et al. 2000;

Burgard, Moors et al. 2005; Fox, Ko et al. 2006). The multi-robot exploration and mapping

are generally studied and applied as an essential part of robotic operations. Related

applications of the multi-robot exploration and mapping include: reconnaissance and

surveillance (Saptharishi, Spence Oliver et al. 2002), search and rescue (Murphy 2004),

mowing (Sahin and Guvenc 2007) and cleaning (Altshuler, Yanovski et al. 2009).

In the processes of exploration and mapping, each robot keeps a record of explored area – the

local map of the environment and continually updates the mapping data while it is exploring

different parts of the environment. These localised maps are then combined into a global map

by a central agent. As the exploration process continues, the coverage of this global map is

extended till the entire environment is completely navigated by robots. A hierarchical

structure is applied here when the maps of individual robots are combined by a centralised

agent. In addition, the central agent also manages a bidding scheme to assign different robots

to explore different areas of the environment (Simmons, Apfelbaum et al. 2000). The central

agent makes the decisions of assignment using current local maps obtained by individual

robots. The task allocation of such kind coordinates robot behaviour, which minimises

overlapping areas of exploration and, hence, enhance group efficiency of robots. The central

agent is expected to be one of the robots, which coordinates the exploration and mapping

67 The distributed approach of exploration and mapping has been further developed to enhance

effective coordination among multiple robots, to produce a shared map at the early stage of

the exploration and to deal with limited communications between robots and the central agent

(Fox, Ko et al. 2006). To improve the coordination of robots, the global map is not only

produced as a result of the exploration, but also shared among robots during the process to

provide real-time information of the explored parts of the environment. When exploring large

area of environment, the communications among robots, between robots and the central agent

are constrained and when failures occur, individual robots are expected to be able to carry on

exploring on their own. The distributed exploration approach provides robots with certain

level of independence to deal with the failure caused by limited communications. In this way

the team of robots is sufficiently robust to adapt into ad hoc situations.

Furthermore, (Franchi, Freda et al. 2007) presented a fully decentralised random strategy that

uses a multi-robot Sensor-based Random Trees (SRT) method to record and maintain the

local maps of explored area with an associated safe region. The multi-robot SRT method is a

multiple version of the single-robot SRT method, which provides a collection of data

structures to keep track of explored area of multiple robots. Robots are able to conveniently

access the data structure of such kind to make use of the information for their further

exploration and mapping tasks. With respect to the nature of decentralisation, the randomised

strategy is accomplished with cooperation to increase efficiency, coordination to avoid

conflicts, as well as communications to support effective cooperation and coordination

among robots. Different from the hierarchical structure applied via a central agent in

(Simmons, Apfelbaum et al. 2000; Fox, Ko et al. 2006), the full decentralisation require no

central management of tasks. The global map of the environment is generated through the

multi-robot SRT method, and there is no task decomposition and/or allocation involved in the

68 Moreover, the randomly generated data structures provide robots with reliable and up-to-date

information of the explored area, which enable robots to be flexible and robust when dealing

with individual robot malfunctions and communication failures.

3.1.2.2

Pattern Formations of Robotics

Pattern formation is the emergent result of local actions and multiple interactions among

individual robots and between robots and the environment. Generating and maintaining a

pattern formation is another popular subject of research in the multi-robot systems. The

generation processes of pattern formations vary between collective robotics and

swarm/metamorphic robotics, as the former is composed of individually independent robots

and the latter is a team of physically connected robots. Both are decentralised, and are

emerged from individual behaviour of robots through swarm-inspired mechanisms of

cooperation – self-organisation, stigmergy and multiple communications.

(Bayindir and Sahin 2007) refer pattern formation to be one of the problems of interest in

swarm robotics research. Existing research indicate that the processes of generating pattern

formations involve both individual level adaptation and group level adaptation (Bahceci,

Soysal et al. 2003). Information received from local sensing and limited communications

among individuals is used to generate an open-loop style of formation of robots (Fredslund

and Mataric 2002). Additionally, earlier research of robot formations learn from the flocking

of birds, schooling of fish and ant foraging pattern, and construct goal-directed reactive

control approach to generate and maintain different formation patterns (Balch and Arkin

69 The research works mentioned here exhibits some general requirements of collective

problem-solving:

 The problem-solving approaches require effective cooperation of individual robots, and the group capabilities are emerged from simple individual behaviour of robots;

 Robots should be able to interact at multiple levels, both among individuals and with the environment;

 Acquiring and accessing real-time information on the environment is important to ensure effective and efficient accomplishment of multi-robot systems;

 The goal and goal-defined tasks can be exploited and programmed in a form of individual behaviour of robots, as well as mechanisms for cooperation and

coordination to fulfil the problem requirements with respect to the nature of

decentralised intelligent systems.

Briefly, in the context of collective problem-solving, multi-robot systems and multi-agent

systems are two independent subjects but also share common characteristic properties in the

design, modelling, communications, analytical studies and related problems. Next section

presents the mutual contributions of the two subjects by firstly describing the taxonomy of

multi-agent systems, and then explaining how agent-based methodologies are deployed in the

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