Power Consumption and Conservation of Industrial Robots Through Embedded System
INTRODUCTION:
Robot was coined by Czech playwright Karl Capek in his play R.U.R (Rossum’s Universal Robots), which opened in Prague in 1921. Robot is the Czech word for forced labor.
The term robotics was introduced by writer Isaac Asimov. In his science fiction book I, Robot, published in 1950, he presented three laws of robotics:
1. A robot may not injure a human being, or, through inaction, allow a human being to come to harm
2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law
INDUSTRIAL ROBOTS:
Robots usually have multiple components, such as motors, sensors, microcontrollers and embedded computers. DC motors transform direct current into mechanical energy and are often used to drive the robots. Sensors collect data from environment and provide information to robots. Most often used sensors are vision, infrared, sonar and laser rangers. Many robots use embedded computers for high-level computation and microcontrollers for low-level controls.
EPSON INDUSTRIAL ROBOT
The microcontroller directly controls motors, sensors, and polls the sensor readings. It hides the hardware details from the embedded computer, and provides an application programming interface (API) for the embedded computer. The embedded computer handles high-level computation, including motion planning, image processing, and scheduling. The separation of the microcontroller and embedded computer makes the designs more flexible. However, other components like sensing, control, communication and computation also consume significant amounts of power. It is important to consider all components to achieve better energy efficiency. This study has two major contributions. Firstly, we study power consumption of a robot
USES OF INDUSTRIAL ROBOTS:
One of the most common uses for industrial robots is welding. Robot welded car bodies for example enhances safety, a robot never miss a welding spot and performs equally all through the day.
In assembling of parts many of these robots can be found in the automotive and electronics industries Packaging/palletizing, is still a minor application area for industrial robots, this application area is expected to grow as robots become easier to handle.
The food industry is an area where robots are expected to play a major role in the future. The process involves harvesting each arriving plant, cutting its steam into segments near each node, and then replanting the segments so that they can grow into new plants etc.
MICROCONTROLLER AND EMBEDDED COMPUTER
The microcontroller periodically sends commands to motors and sensors, polls sensors’ readings, and communicates with the embedded computer. The microcontroller’s tasks are usually fixed so the power consumption of the microcontroller can be modeled by a constant. The embedded computer is more complex than the microcontroller. Many studies have been devoted into simulation-based methods to estimate its power consumption [6] [5] [8]. The power consumption of the embedded computer may vary significantly across different programs.
Micro controller
Motor
Sensor
Embedded computer
PREVIOUS WORK:
[1] Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines
ENERGY-CONSERVATION TECHNIQUES:
This section explains three promising techniques for power reduction of mobile robots.
A. Dynamic Power Management
Dynamic power management (DPM) dynamically adjusts power states of components adaptive to the task’s need. The purpose is to reduce the power consumption without compromising system performance. Many electronic components have multiple power states; their power consumption is different at different power states. For example, processors can run on different frequencies. To save power, the processors can enter lower frequencies when the workloads are light. Another example is to shut off the power supply to the disk in an embedded computer to save the static power when there is no disk access.
A simple DPM method shuts down a component when it is idle. It is essentially a prediction problem. If we predict there is no access on this component for a reasonably long period of time, the component can be shut down to save static power. Turning on and off the component takes time and energy. If the idle period is too short, the components may actually consume more energy for turning on and off. One of the widely used prediction methods is timeout: if the component has been idle for a time period longer than the timeout, the component will be shut down. The rationale behind timeout is that the component is likely to keep idle in the near future since it has been idle for a while. Another widely used DPM technique is dynamic voltage scaling (DVS) by reducing both supply voltage and clock frequency to reduce the power consumption of processors. CMOS circuit is its dynamic power, which can be expressed by c Vdd, f, where c is the effective switched capacitance, vdd is the supply voltage and f is the clock frequency.
B. Real-Time Scheduling
Real-time systems handle tasks with deadlines. Real-time scheduling (RTS) schedules multiple tasks and meet the deadlines. If the tasks can be scheduled without missing the deadlines, we say they are schedulable. Mobile robots are real-time systems. When a robot detects an obstacle, it has to timely slow down and decides the next motion. For multiple robots coordinating to accomplish a task, timely information communicating is critical. Two often used scheduling algorithms are rate monotonic (RM) and earliest deadline first (EDF). Many other algorithms are based on these two. RM is a fixed-priority algorithm, assigning a higher priority to a task with a shorter period. EDF executes the task with the earliest deadline among all ready tasks. It has been proved that EDF is optimal with respect to minimizing the maximum lateness. Besides scheduling tasks to meet their deadlines, RTS can also schedule the tasks such that DPM can save more energy. For example, when the idle periods of a component are too short due to frequent accesses, power cannot be saved by shutting down the component. However, if we can reschedule the tasks and make the component have more long idle periods, the component may be shut down to save power.
C. Examples
In this section, we show some potential applications of DPM and RTS into energy-efficient robot designs using several examples.
1) Shutdown of Unused Components: Electric components consume static power in idle states. Shutting down the power supply when a component is idle can save the static power. When the robot stops, the sensors may be turned off. If half of the time the sensors can be shut down, the average sensing power can be reduced.
2) Sensing Frequency Scaling: It is intuitive that the sensing frequency should be different when robots move at different speeds. The sensing frequency needs to be higher when the speed is higher. Instead of keeping the sensing frequency that satisfies the highest speed’s need, we can reduce the sensing frequency when the robot moves slowly. If the robot moves slowly and the sensing frequency can be reduced.
3) Dynamic Voltage Scaling: DVS is very effective in reducing processors’ power. The processor inside the Hitachi-8s microcontroller can work at two different frequencies: 20MHz and 10MHz. The current operating system inside the microcontroller doesn’t support the frequency scaling. Therefore, we can not measure the power savings. However, if we can dynamically change the working frequency according to the workload, we can reduce the control power. This technique also applies to the embedded computer.
4) Trade-off between Motion and Communication:
A Team of robots may move and cooperatively execute a task. Robots need to send sensing data through wireless communication. Consider one robot needs to transfer data to another robot, but the robot is far away. If the robots can move closer, the communication power can be saved. The cost here is the motion power for moving closer. If the volume of the data is large enough, more communication power can be saved than the motion power cost.
5) Energy-Efficient Real-Time Scheduling for Robots:
A mobile robot is a real-time system. The robot can have many periodic tasks, such as motor and sensor control, sensing data reading, motion planning, and data processing. The robot may also have some aperiodic tasks, such as obstacle avoidance and communication. RTS can work with DPM to more effectively reduce the power consumption. For example, if a scheduler can cluster tasks closer in time and create longer idle periods, shutdown techniques can be more effective. RTS also can work with DVS to reduce processor energy consumption, as we discussed in the related work. For mobile robots, the tasks’ deadlines are different at different traveling speeds. At a higher speed, the periodic tasks have shorter periods. Therefore, we should consider both motion planning and RTS together.
FORTIFICATION:
Many fails to consider the third quadrant called fortification. This paper put some idea about that quadrant. Some shrinks the use of robots by Fleet Size Problem [3]: A fundamental question for multi-robot applications is to decide the number of robots needed (i.e., the “fleet-size problem”) to accomplish tasks. We provide a probabilistic method to decide the fleet size necessary to serve requests with random arrival times and locations. We consider five factors on which the fleet size depends: available energy, power consumption, service field, request rate, and timing constraints.
Though we know that many of the industrial robots uses stepper motors, servo motors, relays etc., on AC or DC. In AC supply it is must to keep the power factor under control it should not be low. If it so, heavy power loss will be occurred and the company will be liable to meet the surcharge of their electricity department.. The following are the some of the fortification technique
1. To improve the power factor capacitor has to be used. Though robot uses electrical motors, transformers etc., under starting state it needs high capacitance to maintain pf; on the other hand under running condition it needs minimum capacitance value. In other words it can be explained as the capacitance value changes according to the load of the robots. These will be an extra burden to cutoff the capacitor under load. To overcome this dynamic control capacitor may be used.
2. The one of the factor that decides the life of the robot is wear and tear of the electrical equipments. This falls in sparking of the relays contacts and motor brushes due to the in rush current. These can be eliminated by close circuit transient.
3. Due to energy conservation law, energy will be dissipated through heat. This can be reduced by silver windings. Silver has the lower resistance of current then copper and aluminum. Due to lower resistance of electric current it reduces major electrical losses
4. Over load detector for example, robot with hardware platform of a Pioneer 2-DX robot [2] augmented with custom hardware for watering. To deliver water to the plant, the robot has been fitted with a water line, dispensing spout, and pump. To deliver power to wireless sensors an inductive charging coil has been positioned near the watering spout. Similarly, another paddle shaped inductive charge coil has been added to the robot to allow it to recharge itself at its “maintenance bay”. In order to support calibration, the robot includes a sensor node that was human-calibrated lastly, the robot has a maintenance bay it uses to automatically charge its own batteries and refill its water reservoir. The reliability of this approach has been demonstrated during the deployment of the robots Rhino and Minerva as autonomous museum tour guide robots [4, 7]. The high-level task ordering and dispatching software was custom-built for the Plant Care project.
There may be the chance to water to direct contact with the power pack. This leads to short circuit, and draws more current that the rated (PU) per unit.
FUTURE ENHANCEMENTS:
For future work, I plan to extend the current study in two directions. First, we will measure power consumption of more components, such as laser rangers, cameras, servo motors, stepper motors, and relays. Second, I plan to implement the proposed energy conservation techniques into the Pioneer robots, and conduct experiments in real applications
CONCLUSION:
In this study, I presented some of the power consumption technique of different components of an industrial robot. In this paper, I introduce one technique called fortification technique than two exiting techniques DPM and RTS for energy-efficient designs of robots. These techniques together with motion planning provide greater opportunities for reducing the power consumption and prolonging the operation time of mobile robots.
REFERENCE:
[1] Yongguo Mei, Student Member, IEEE, Yung-Hsiang Lu, Member, IEEE, Y. Charlie Hu, Member, IEEE, and C. S. George Lee, Member, IEEE
[2].ActivMedia Robotics, http://www.activrobots.com, visited Feb. 2002.
[3] Y. Mei, Y.-H. Lu, C. S. G. Lee, and Y. C. Hu. Determining the Fleet Size of Mobile Robots with Energy Cons traints. In IEEE /RSJ International Conference on Intelligent Robots and Systems, pages 1420–1425, 2004.
[4]. Burgard, W., A. Cremers, D. Fox, D. Haehnel, G. Lakemeyer, D. Schulz, W. Steiner and S. Thrun. 1999. Experiences with an interactive museum tour-guide robot. Artificial Intelligence.
[5] J. R. Lorch and A. J. Smith. Apple Macintosh’s Energy Consumption. IEEE Micro, 18(6):54–63, November 1998
[6] D. Brooks, V. Tiwari, and M. Martonosi. Wattch: A Framework for Architectural-level Power Analysis and Optimizations. In International Symposium on Computer Architecture, pages 83– 94, 2000.
[7]. S. Thrun, M. Bennewitz, W. Burgard, A. Cremers, F. Dellaert, D. Fox, D. Haehnel, C. Rosenberg, N. Roy, J. Schulte and D. Schulz. 1999. MINERVA: A second generation mobile tour-guide robot. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
[8] T. Simunic, L. Benini, and G. D. Micheli. Cycle-accurate Simulation of Energy Consumption in Embedded Systems. In Design Automation Conference, pages 867–872, 1999
Assistant professor in lord venkateswara engineering college.I am doing phd in sathyabama university, Tamil Nadu,India.
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