Look-ahead Technique Research Articles

A Look-Ahead Technique for Search-Based HTN Planning: Reducing the Branching Factor by Identifying Inevitable Task Refinements

In HTN planning the choice of decomposition methods used to refine compound tasks is key to finding a valid plan. Based on inferred preconditions and effects of compound tasks, we propose a look-ahead technique for search-based total-order HTN planning that can identify inevitable refinement choices and in some cases dead-ends. The former occurs when all but one decomposition method for some task are proven infeasible for turning a task network into a solution, whereas the latter occurs when all methods are proven infeasible. We show how it can be used for pruning, as well as to strengthen heuristics and to reduce the search branching factor. An empirical evaluation proves its potential as incorporating it improves an existing HTN planner such that it is the currently best performing one in terms of coverage and IPC score.

A Novel 3D Path Following Scheme for a Class of Underactuated Underwater Vehicles with Unconventional Actuation

A 3D path following algorithm involving a novel motion control methodology is investigated for use on a new class of autonomous underwater vehicle (AUV). Unlike standard path following AUV control algorithms for underactuated vehicles, where roll perturbations are avoided, this unconventional algorithm utilizes active roll control to achieve a desired heading. The desired heading is calculated using traditional look-ahead techniques, and is achieved by performing a roll rotation to align the body-fixed yaw plane with the desired heading, and then a yaw rotation is performed to achieve that desired heading. It is demonstrated that this new path following algorithm achieves good tracking results, but experiences complications for increased forward velocities. It is also demonstrated that the complications at higher velocities can be overcome with adaptive adjustment of the look-ahead algorithm parameters.

A look-ahead approach to maximizing present value of genetic gains in genomic selection.

Look-ahead selection is a sophisticated yet effective algorithm for genomic selection, which optimizes not only the selection of breeding parents but also mating strategy and resource allocation by anticipating the implications of crosses in a prespecified future target generation. Simulation results using maize datasets have suggested that look-ahead selection is able to significantly accelerate genetic gain in the target generation while maintaining genetic diversity. In this paper, we propose a new algorithm to address the limitations of look-ahead selection, including the difficulty in specifying a meaningful deadline in a continuous breeding process and slow growth of genetic gain in early generations. This new algorithm uses the present value of genetic gains as the breeding objective, converting genetic gains realized in different generations to the current generation using a discount rate, similar to using the interest rate to measure the time value of cash flows incurred at different time points. By using the look-ahead techniques to anticipate the future gametes and thus present value of future genetic gains, this algorithm yields a better trade-off between short-term and long-term benefits. Results from simulation experiments showed that the new algorithm can achieve higher genetic gains in early generations and a continuously growing trajectory as opposed to the look-ahead selection algorithm, which features a slow progress in early generations and a growth spike right before the deadline.

Improving utilization rate of semi-parallel successive cancellation architecture for polar codes using 2-bit decoding

Polar codes are the capacity-achieving error-correcting code proved to be a significant invention in coding theory. It can achieve channel capacity at infinite code length N due to its explicit code construction. However, the processing complexity along with the higher latency due to successive cancellation (SC) decoding is being a major design issue, which reduces the utilization rate in the decoder architectures. This paper presents a modified semi-parallel architecture for decoding polar code with a better decoding latency. Precomputation and look-ahead techniques are used to generate two bits in the final stage. Pipelined partial-sum unit with a less critical path reduces hardware complexity independent of code length. Hence, the fact that the proposed architecture reduces the latency by 2.7 times leads to increase in utilization rate than prior semi-parallel architecture. For a code length of $N=2^{10}$, the proposed architecture shows $ 62.7\% $ and $ 94\% $ improved utilization rate compared to the conventional semi-parallel architecture and 2-bit SC decoder, respectively. Compared to the conventional semi-parallel decoder for $N=2^{17}$, hardware resource such as look-up-tables (LUT) and flip-flops (FF) usage are reduced by 98\% in field programmable gate array (FPGA) leads to reduction in processing complexity. Hence, very large efficient polar decoders with a high utilization rate can be implemented in FPGA.

Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices.

Recent advances in electronics and microelectronics have aided the development of low-cost devices that are widely used as well-being or preventive monitoring devices by many people. Remote health monitoring, which includes wearable sensors, actuators, and modern communication and information systems, offers effective programs that allow people to live peacefully in their own homes while also being protected in some way. High-frequency noise, power-line interface, and baseline drift are prevalent during the data-acquisition system of an ECG signal, and they can limit signal understanding. They (noises) must be isolated in order to provide an appropriate diagnostic of the patient. When removing high-frequency components (noise) from an ECG signal with an FIR filter, the critical path delay increases considerably as the filter's duration increases. To reduce high-frequency noise, simple moving average filters with pipelining and look-ahead transformation techniques are extensively used in this study. With the use of pipelining and look-ahead techniques, the only objective is to increase the clock speed of the designs. The moving average filters (conventional and proposed) were created on an Altera Cyclone IV FPGA EP4CE115F29C7 chip using the Quartus II software v13.1 tool. Finally, performance metrics such logic elements, clock speed, and power consumption were compared and studied thoroughly. The recursive pipelined 8-tap MA filter with look-ahead approach outperforms the other designs (685.48 MHz) in this investigation.

Anytime and Efficient Multi-agent Coordination for Disaster Response

The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. To maximise the number of completed tasks, the agents may have to schedule coalitions. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define a variant, called Coalition Formation with Improved Look-Ahead (text {CFLA}2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in text {CFLA}2, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. text {CFLA}2) while being up to 4 orders of magnitude faster. We also propose S-CTS, a simplified but parallel variant of CTS with even lower time complexity. Using scenarios generated by the RoboCup Rescue Simulation, we show that S-CTS is at most 10% less performing than high-performance algorithms such as Binary Max-Sum and DSA, but up to 2 orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.

Coupling Adjacent Microgrids and Cluster Formation Under a Look-Ahead Approach Reassuring Optimal Operation and Satisfactory Voltage and Frequency

Microgrids (MGs) are promising approaches to proliferate distributed energy resources for electrification in remote areas. However, to deal with the uncertainties of renewables and energy demand, special measures are required. Energy storages, controllable loads and reconfigurable networks are some of those measures that improve MGs' flexibility. Another alternative is temporarily coupling the adjacent MGs to support each other and form coupled MGs (CMGs). This paper proposes a look-ahead technique to form CMGs while reassuring the optimal performance of all MGs. Preserving the voltage and frequency of each MG is also another key objective. The proposed optimization approach tries to solve the voltage/frequency problem by forming the CMGs when the local actions (such as energy storages) are inadequate or cost-ineffective. The proposed technique considers the operational cost, technical and environmental aspects, reliability and losses in the CMG formation. This technique is general and can be used for complex topologies and also to form multiple CMGs if deemed more suitable. The performance of the developed technique is validated through extensive numerical analyses in MATLAB.

CLA Formula and its Acceleration of Architecture Design for Clustered Look-Ahead Pipelined Recursive Digital Filter

With the rapid development of 5G network and Internet of Things, Clustered Look-Ahead (CLA) technique becomes a promising approach to further accelerate recursive digital filters for real-time applications, such as smart robots and automatic driving. However, it is not easy to quickly obtain a stable CLA pipelined VLSI (Very Large Scale Integration) architecture using the existing methods. This paper proposes a CLA Formula to accelerate the VLSI architecture design procedure through rigorous and complete mathematical derivation. Comparing the proposed method with the symbolic approach, experiments reveal that more than half of the software coding time can be saved for the designers, and the execution time of the programs can also be reduced by 168 to 30243 times with the number of pipelined stages ranging from 6 to 96.

A real-time trajectory planning method for enhanced path-tracking performance of serial manipulators

Robotized assembly and manufacturing often require to modify the robot motion at runtime. When the primary constraint is to preserve the geometrical path as much as possible, it is convenient to scale the nominal trajectory in time to meet the robot constraints. Look-ahead techniques are computationally heavy, while non-look-ahead ones usually show poor performance in critical circumstances. This paper proposes a novel technique that can be embedded in non-look-ahead scaling algorithms to improve their performance. The proposed method takes into account the robot velocity, acceleration, and torque limits and modifies the velocity profile based on an approximated look-ahead criterion. To do this, it considers only the last point of a look-ahead window and, by linearizing the problem, it computes the maximum admissible robot velocity. The technique can be applied to existing trajectory scaling algorithms to confer look-ahead properties on them. Simulation and experimental results on a 6-degree-of-freedom manipulator show that the proposed method significantly reduces path-following errors.

Look-ahead bidding strategy for concentrating solar power plants with wind farms

The concentrating solar power (CSP) plant with the thermal energy storage (TES) is one of the most effective methods to solve the intermittent characteristics of solar energy. CSP plants combined with wind farms could provide continuous, stable power generation and reduce the uncertainty of the wind power. In this paper, a look-ahead technique is proposed to optimize the bidding strategy for a joint system of CSP plants with wind farms considering both the day-ahead and the following day. It is assumed that the CSP plant participates in the ancillary service (AS) market bidding while providing the reserve capacity for the wind farm to counteract the output fluctuations. As the price-taker, the joint system has different levels of attention to the market price and renewable energy output. Therefore, the scenario set and the chance-constrained programming (CCP) is used to describe its uncertainty, respectively. Besides, in order to further enhance the flexibility and maximize the economic revenues of the system, an electric heater (EH) is added to the model. The joint operator can adjust the final heat storage level to balance profit opportunities during the current market window against potential opportunities in the subsequent market window. Finally, we compare the results of different bidding models to demonstrate the effectiveness of the proposed model and the advantages of the joint system.

QoE-Aware Computation Offloading to Capture Energy-Latency-Pricing Tradeoff in Mobile Clouds

Computation offloading in mobile clouds helps mobile users save energy and enhance performance via mobile-to-cloud migration of processing. Although there exist many approaches to computation offloading, they have not explicitly considered the energy-latency-pricing tradeoff from the viewpoint of mobile users’ context, e.g., user tendency, the remaining battery level. This paper tries to capture the user-centric perspective via quality-of-experience (QoE), and formulates two important problems: mobile-to-cloud transmission scheduling of the offloaded task's data and offloading service class selection. Regarding transmission scheduling, we introduce a database-assisted optimal dynamic programming (DP) algorithm and then propose two suboptimal but computationally-efficient approximate DP algorithms, ADP and ADPe, based on the limited lookahead technique. Regarding service class selection, we consider multiple service classes with different computing power and service charge, and formulate an optimization problem to minimize the overall cost incurred during offloading. An extensive numerical analysis has revealed that ADP and ADPe achieve near-optimal performance incurring only 0.35 and 2.1 percent extra cost than the optimum on average, and enhances the QoE-aware cost by up to 2.38 times compared to the energy-only scheduling. In addition, our service class selection algorithm is shown to choose the best class according to user tendency and the remaining battery level.

Utilization Management and Pricing of Parking Facilities Under Uncertain Demand and User Decisions

Excessive search for parking spots in congested areas contributes to additional travel delays and negative socio-economic impacts. While managing parking utilization to address the agency’s objectives, it is often very beneficial to reflect the diversity of users’ behaviors and their travel choices. This paper develops a stochastic dynamic parking management model, under competitive user-agency perceptions and uncertain user demand and parking occupancy, to simultaneously minimize the total travelers’ costs and maximize the parking agency’s revenue. The problem is formulated into a dynamic programming model and solved using a stochastic look-ahead technique based on the Monte Carlo tree search algorithm to determine optimal actions on parking price assignment and spot utilization over time. The numerical experiments on a hypothetical and empirical case study are conducted to show the performance of the proposed algorithm and to draw managerial insights. The results are compared with those of benchmark algorithms, which indicate that the proposed methodology can determine near-optimal solutions efficiently.

Programming parallel dense matrix factorizations with look-ahead and OpenMP

We investigate a parallelization strategy for dense matrix factorization (DMF) algorithms, using OpenMP, that departs from the legacy (or conventional) solution, which simply extracts concurrency from a multi-threaded version of basic linear algebra subroutines (BLAS). The proposed approach is also different from the more sophisticated runtime-based implementations, which decompose the operation into tasks and identify dependencies via directives and runtime support. Instead, our strategy attains high performance by explicitly embedding a static look-ahead technique into the DMF code, in order to overcome the performance bottleneck of the panel factorization, and realizing the trailing update via a cache-aware multi-threaded implementation of the BLAS. Although the parallel algorithms are specified with a high level of abstraction, the actual implementation can be easily derived from them, paving the road to deriving a high performance implementation of a considerable fraction of linear algebra package (LAPACK) functionality on any multicore platform with an OpenMP-like runtime.

A simplified radix-4 successive cancellation decoder with partial sum lookahead

Polar codes recently received high attention by researchers as proven to approach channel capacity at higher codeword length. However, the decoding latency grows significantly with codeword length, rendering implementation for latency constrained applications impossible. To tackle this problem, this paper proposes a polar decoder architecture based on radix-4 processing units with a special last stage processing unit to decode up to 16 bits in the same clock. In addition, it proposes decoding extended special subcodes to reduce latency. Moreover, it uses partial sum look-ahead technique, resulting in a high throughput low latency decoding architecture.

Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment

Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-head-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 59.4% and 83.7% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.

Variable Latency approach in VLSI adder Implemented to Reduce Area and Power

Objective: The Ultimate aim of the VLSI Design is to improve the efficiency, Reduction of Delay and Power Consumption and to minimize the area. In our proposed approach we had implemented the analysis had been done on the field of Speed, Power consumption, Area and Power delay product (PDP) for a carry skip adder with other adders listed as the parallel prefix adders and others. Methods/Statistical Analysis: The Carry-Skip Adder planned here reduces the time required to propagate the carry by skipping over teams of consecutive adder stages, is understood to be comparable in speed to the carry look-ahead technique whereas it uses less logic space and fewer power. Findings: The adders are basic building blocks of the digital circuits for the Signal processing, Integrating and other process of operation. There are various types of adders are proposed in Literature which are commonly used in VLSI Design. The Simulation results also shows that the proposed adder Architecture is Faster and Area efficient compared to other existing adder architecture. Application/ Improvements: They estimate the performance of proposed design will be better in terms of Logic and route delay by experimental results. Keywords: Adders, Carry Bypass Adder (CBA), Carry Increment Adder (CIA), Carry Look-Ahead Adder (CLA), Carry Skip Adder (CSkA), Han Carlson Adder (HCA), Ripple Carry Adder (RCA)

Look-Ahead Bidding Strategy for Energy Storage

As the cost of battery energy storage continues to decline, we are likely to see the emergence of merchant energy storage operators. These entities will seek to maximize their operating profits through strategic bidding in the day-ahead electricity market. One important parameter in any storage bidding strategy is the state-of-charge at the end of the trading day. Because this final state-of-charge is the initial state-of-charge for the next trading day, it has a strong impact on the profitability of storage for this next day. This paper proposes a look-ahead technique to optimize a merchant energy storage operator's bidding strategy considering both the day-ahead and the following day. Taking into account the discounted profit opportunities that could be achieved during the following day allows us to optimize the state-of-charge at the end of the first day. We formulate this problem as a bilevel optimization. The lower-level problem clears a ramp-constrained multiperiod market and passes the results to the upper-level problem that optimizes the storage bids. Linearization techniques and Karush–Kuhn–Tucker conditions are used to transform the original problem into an equivalent single-level mixed-integer linear program. Numerical results obtained with the IEEE Reliability Test System demonstrate the benefits of the proposed look-ahead bidding strategy and the importance of considering ramping and network constraints.

Dynamics modeling and robotic-assist, leader-follower control of tractor convoys

This paper proposes a generalized dynamics model and a leader-follower control architecture for skid-steered tracked vehicles towing polar sleds. The model couples existing formulations in the literature for the powertrain components with the vehicle-terrain interaction to capture the salient features of terrain trafficability and predict the vehicles response. This coupling is essential for making realistic predictions of the vehicles traversing capabilities due to the power-load relationship at the engine output. The objective of the model is to capture adequate fidelity of the powertrain and off-road vehicle dynamics while minimizing the computational cost for model based design of leader-follower control algorithms. The leader-follower control architecture presented proposes maintaining a flexible formation by using a look-ahead technique along with a way point following strategy. Results simulate one leader-follower tractor pair where the leader is forced to take an abrupt turn and experiences large oscillations of its drawbar arm indicating potential payload instability. However, the follower tractor maintains the flexible formation but keeps its payload stable. This highlights the robustness of the proposed approach where the follower vehicle can reject errors in human leader driving.

A Novel Architecture to Eliminate Bottlenecks in a Parallel Tiled QRD Algorithm for Future MIMO Systems

QR decomposition (QRD) is one of the performance bottlenecks in a lot of high performance wireless communication algorithms and should have the flexibility property for future multiple-input multiple-output systems. However, the existing QRD architectures only focus on several fixed dimension matrices. The parallel tiled QRD algorithm is a perfect choice to implement QRD for its flexibility and modularity property. The size of the tile is set to 2 $\times$ 2 instead of the traditional 200 $\times$ 200 or more to support flexible antenna configurations in this brief. Using a look-ahead technique and the property of unitary matrix, a novel algorithm based on a modified Gram–Schmidt (MGS) algorithm is proposed for the bottleneck operations (GEQRT and TTQRT) of the parallel tiled QRD algorithm. A corresponding hardware architecture is also designed with the proposed algorithm. The implementation results show that the hardware architecture based on the proposed algorithm achieves a 2.7× reduction in normalized processing latency, compared with the one based on the traditional MGS algorithm.

Exact Algorithm to Solve the Minimum Cost Multi-Constrained Multicast Routing Problem

The optimal solution of the multi-constrained QoS multicast routing problem is a tree-like hierarchical structure in the topology graph. This multicast route contains a feasible path from the source node to each of the destinations with respect to a set of QoS constraints while minimizing a cost function. Often, it is a tree. In other cases, the hierarchies can return several times to nodes and links of the topology graph. Similarly to Steiner problem, finding such a structure is an NP-hard problem. The usual tree and topology enumeration algorithms applied for the Steiner problem cannot be used to solve the addressed problem. In this paper, we propose an exact algorithm based on the Branch and Bound principle and improved by the Lookahead technique. We show relevant properties of the optimum hierarchy permitting efficient pruning of the search space. To our knowledge, our paper is the first to propose an exact algorithm for this non-trivial multi-constrained optimal multicast route computation. Simulations illustrate the efficiency of the proposed pruning operations. The analysis of the execution time shows that in simple topologies and with tight QoS constraints the exact algorithm requires relatively little execution time. With loose constraints the computation time cannot be tolerated even for off-line route computation. In these cases, the solution is close to a Steiner tree and heuristics can be applied. These results can serve as basis for the design of efficient, polynomial-time routing algorithms.