KEYWORDS: Digital signal processing, Navigation systems, Sensors, Field programmable gate arrays, Buildings, MATLAB, Visualization, Filtering (signal processing), Clocks, Mobile robots
Dead Reckoning (DR) is the process of estimating a robot's current position based upon a previously determined
position, and advancing that position based upon known speed and direction over time. It is therefore a simple way for an
autonomous mobile robot to navigation within a known environment such as a building where measurements have been
taken and a predetermined route planned based upon which doors (or areas) the robot would have enough force to enter.
Discussed here is the design of a DR navigation system in Altera's DSP Builder graphical design process. The wheel
circumference to the step size of stepper motor used to drive the robot are related and so this ratio can be easily changed
to easily accommodate changes to the physical design of a robot with minimal changes to the software. The robot
calculates its position in relation to the DR map by means of the number of revolutions of the wheels via odometry, in
this situation there is no assumed wheel slippage that would induce an accumulative error in the system overtime. The
navigation works by using a series of counters, each corresponding to a measurement taken from the environment, and
are controlled by a master counter to trigger the correct counter at the appropriate time given the position of robot in the
DR map. Each counter has extra safeguards built into them on their enables and outputs to ensure they only count at the
correct time and to avoid clashes within the system. The accuracy of the navigation is discussed after the virtual route is
plotted in MATLAB as a visual record in addition to how feedback loops, identification of known objects (such as fire
safety doors that it would navigate through), and visual object avoidance could later be added to augment the system.
The advantages of such a system are that it has the potential to upload different DR maps so that the end robot for can be
used in new environments easily.
The coordination of a multi-robot system searching for multi targets is challenging under dynamic environment since the
multi-robot system demands group coherence (agents need to have the incentive to work together faithfully) and group
competence (agents need to know how to work together well). In our previous proposed bio-inspired coordination
method, Local Interaction through Virtual Stigmergy (LIVS), one problem is the considerable randomness of the robot
movement during coordination, which may lead to more power consumption and longer searching time. To address
these issues, an adaptive LIVS (ALIVS) method is proposed in this paper, which not only considers the travel cost and
target weight, but also predicting the target/robot ratio and potential robot redundancy with respect to the detected
targets. Furthermore, a dynamic weight adjustment is also applied to improve the searching performance. This new
method a truly distributed method where each robot makes its own decision based on its local sensing information and
the information from its neighbors. Basically, each robot only communicates with its neighbors through a virtual
stigmergy mechanism and makes its local movement decision based on a Particle Swarm Optimization (PSO) algorithm.
The proposed ALIVS algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching
target. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world
constraints.
Miniature robots have many advantages over their larger counterparts, such as low cost, low power, and easy to build a
large scale team for complex tasks. Heterogeneous multi miniature robots could provide powerful situation awareness
capability due to different locomotion capabilities and sensor information. However, it would be expensive and time
consuming to develop specific embedded system for different type of robots. In this paper, we propose a generic
modular embedded system architecture called SMARbot (Stevens Modular Autonomous Robot), which consists of a set
of hardware and software modules that can be configured to construct various types of robot systems. These modules
include a high performance microprocessor, a reconfigurable hardware component, wireless communication, and diverse
sensor and actuator interfaces. The design of all the modules in electrical subsystem, the selection criteria for module
components, and the real-time operating system are described. Some proofs of concept experimental results are also
presented.
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