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This paper describes our research and experiments with autonomous robots, in which were used genetic algorithms to evolve stable gaits of simulated legged robots in a physically based simulation environment. In our approach, the gait is defined using a finite state machine based on the joint angles of the robot legs, and the parameters are optimize...
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... to Figure 3, the results obtained by the morphology and control evolution are clearly superior to those obtained using just the control evolution, since the confidence intervals are not superposed. Figure 4 shows a walking accomplished by Figure 2 robot, and Figure 5 shows a walking accomplished by an evolved robot 4 . Figure 6 shows the morphologies evolved in Table 2 experiments (the numbers in the top-left corner refers to the experiment number in Table 2). Observing Figure 6, it is noticed that the large state space allows the evolution of different solutions, even so efficient, in a similar manner that was oc- curred in the natural evolution. The main goal of this paper was to describe our research and experiments with autonomous robots, in which were used genetic algorithms to evolve stable gaits of simulated legged robots in a physically based simulation environment. The GA evolves parameters used to control the robot actu- ators and also the robot morphology, and this evolution was tested into a virtual environment using the ODE rigid body dynamics simulation tool. The accomplished experiments demonstrate that the morphology evolution is superior to the evolution of the control parameters only. Some future work includes improving the robot gait in order to walk on irregular surfaces and to go upstairs or downstairs, as well as, to implement in hardware the simulated robot, once we have now acquired sufficient experi- ence in order to design, implement and fine tune the control of the legged ...
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This paper describes the LegGen System, used to automatically create and control stable gaits for legged robots into a physically based simulation environ-ment. In our approach, the gait is defined using three different methods: a finite state machine based on robot's leg joint angles sequences; a locus based gait defining the endpoint (paw) trajec...
Citations
... [11] describes a search algorithm for finding the most efficient gait over uneven terrain. Other papers using GA or other simulation and optimization techniques include [1], [5], [12], [13], [14], [15] and [16]. None of the papers found during the literature review focused their research on the energy efficiency of dynamic leg length hexapod systems. ...
Walking robots consume considerable amounts of power, which leads to short mission times. Many of the tasks that require the use of walking robots, rather than wheeled, often require extended periods of time between the possibility of charging. Therefore, it is extremely important that, whenever possible, the gait a walking robot uses is as efficient as possible in order to extend overall mission time. Many approaches have been used in order to optimize the gait of a hexapod robot; however, little research has been done on how enabling the leg segments of a hexapod to extend will impact the efficiency of its gait. In this thesis, a joint space model is defined that includes both rotational joints as well as prismatic joints for expanding and contracting individual leg segments. A genetic algorithm (GA) is used to optimize the efficiency of a gait using the joint space based on a tripod gait. Other considerations for the gait include stability and dragging, which affects overall efficiency of a gait. The results of preliminary runs of the GA show the impacts of changing the weights of a multi-objective function, the number of generations, the number of parents retained between generations and the mutation rate. Further experiments show the impact of dynamic leg lengths on the overall efficiency of a hexapod tripod gait.
... In that context research has been done on Central Pattern Generators (CPG) -related to nerve cells in animal's spinal cord dedicated for generating periodic leg movement in animals and their application for robot control [7] [8] [9]. Additionally to this research various combinations have been also experimented on using artificial neural networks and genetic algorithms for robot gait pattern generation and gait control [10] [11] [12] [13]. However, just predefining some robot leg movements or generating gait patterns is sometimes not adequate for robust and fault-tolerant robot operation and it is usually an exhausting task on analyzing all the situations the robot may operate in. ...
In this paper we introduce and elaborate a biologically inspired methodology for robot walking gait pattern self-synchronization
using ORCA (Organic Robot Control Architecture). The firefly based pulse coupled biological oscillator concept has been successfully
applied for achieving self-organized synchronization of walking robot gait patterns by dynamically prolonging and shortening
of robot’s legs stance and swing phases. The results from the experiments done on our hexapod robot demonstrator show the
practical usefulness of this biologically inspired approach for run-time self-synchronizing of walking robot gait pattern
parameters.
This article presents a solution to the problem of how to coordinate the actuators of a legged robot such that its frontal speed is maximized. It is assumed that the position of each leg actuator is described by a periodic function that has to be determined using a reinforcement learning technique called Learning Automata. Analysis of the robot morphology is used to group similar legs and decrease the number of actuator functions that must be determined. MATLAB/Simulink and the SimMechanics Toolbox are used to simulate the robot walking on a flat surface. The simulated robot response is evaluated by the reinforcement learning technique considering: 1) the robot frontal speed, 2) the smoothness of the robot movements, 3) the largest torque required by all actuators, and 4) the energy consumption. After the reinforcement learning algorithm converges to a solution, the actuators functions are applied to the real robot that was built using the Bioloid Comprehensive Kit, an educational robot kit manufactured by Robotis. The response of the real robot is then evaluated and compared with the simulated robot response. This article presents two case studies: a quadrupedal robot and a tripedal robot. In both cases, each leg has three actuators. The solutions obtained by the proposed methodology are presented and shown to be satisfactory.
Resource management is one of the important issues in the efficient use of grid computing, in general, and poses specific
challenges in the context of ad hoc grids due to the heterogeneity, dynamism, and intermittent participation of participating
nodes in the ad hoc grid. In this paper, we consider three different kinds of organizations in an ad hoc grid ranging from
completely centralized to completely decentralized (P2P). On the basis of self organization mechanisms, we study the effect
of the neighborhood degree of a node for finding resources on the efficiency of resource allocation. We investigate the message
complexity of each organization and its corresponding efficiency in terms of task/resource matching and the response time.
We show that the intermediate state of the ad hoc grid with multiple adaptive matchmakers outperforms both a completely centralized
and a completely decentralized (P2P) infrastructure.
Performance comparison among various architectures is generally attained by using standard benchmark tools. This paper presents
JetBench, an Open Source OpenMP based multicore benchmark application that could be used to analyse real time performance
of a specific target platform. The application is designed to be platform independent by avoiding target specific libraries
and hardware counters and timers. JetBench uses jet engine parameters and thermodynamic equations presented in the NASA’s
EngineSim program, and emulates a real-time jet engine performance calculator. The user is allowed to determine a flight profile
with timing constraints, and adjust the number of threads. This paper discusses the structure of the application, thread distribution
and its scalability on a custom symmetric multicore platform based on a cycle accurate full system simulator.
In this paper, we focus on a real world scenario of managing electrical demand sets of a smart-home. External signals, reflecting the low voltage grid's state, are used to support the challenge of balancing energy demand and generation. To manage the smart-home's appliances and to integrate electric vehicles as energy storages decentralized control systems are investigated.
Power and resource management are key goals for the success of modern battery-supplied multimedia devices. This kind of devices
are usually based on SoCs with a wide range of subsystems, that compete in the usage of shared resources, and offer several
power saving capabilities, but need an adequate software support to exploit such capabilities.
In this paper we present Constrained Power Management (CPM), a cross-layer formal model and framework for power and resource management, targeted to MPSoC-based devices. CPM allows
coordination and communication, among applications and device drivers, to reduce energy consumption without compromising QoS.
A dynamic and multi-objective optimization strategy is supported, which has been designed to have a negligible overhead on
the development process and at run-time.
Register renaming is a widely used technique to remove false data dependencies in contemporary superscalar microprocessors.
The register rename logic includes a mapping table that holds the physical register identifiers assigned to each architectural
register. This mapping table needs to be recovered to its correct state when a branch prediction occurs. In this paper we
propose a scalable rename table design that allows fast recovery on branch predictions. A FIFO scheme is applied with a distributed
rename table structure that holds a variable number of checkpoints specific to each architectural register. Our results show
that although the area of the rename table is increased, it is possible to recover from a branch misprediction in at worst
2 cycles.
During the last decade, performance prediction for industrial and scientific workloads on massively parallel high-performance
computing systems has been and still is an active research area. Due to the complexity of applications, the approach to deriving
an analytical performance model from current workloads becomes increasingly challenging: automatically generated models often
suffer from inaccurate performance prediction; manually constructed analytical models show better prediction, but are very
labor-intensive. Our approach aims at closing the gap between compiler-supported automatic model construction and the manual
analytical modeling of workloads. Commonly, performance-counter values are used to validate the model, so that prediction
errors can be determined and quantified. Instead of manually instrumenting the executable for accessing performance counters,
we modified the GCC compiler to insert calls to run-time system functions. Added compiler options enable the user to control
the instrumentation process. Subsequently, the instrumentation focuses on frequently executed code parts. Similar to established
frameworks, a run-time system is used to track the application behavior: traces are generated at run-time, enabling the construction
of architecture independent models (using quadratic programming) and, thus, the prediction of larger workloads. In this paper,
we introduce our framework and demonstrate its applicability to benchmarks as well as real world numerical workloads. The
experiments reveal an average error rate of 9% for the prediction of larger workloads.
With the increasing number of nodes in high performance clusters as well as the high frequency of the processors in these
systems, energy consumption has become a key factor which limits their large-scale deployment. In response, this paper presents
a software module that, taking into consideration past and future users’ requests, implements energy saving policies, powering
on and shutting down the system nodes. This tool employs Roll for clusters with Rocks® as operating system and Sun® Grid Engine
as queue system.
