A memory-based model for planning target reach postures in the presence of obstructions

Existing posture prediction and motion simulation models generally lack the capability of simulating human obstruction avoidance during target reach. This compromises the utility of digital human models for ergonomics, as many design problems involve interactions between humans and obstructions. To address this problem, this paper presents a novel memory-based posture planning (MBPP) model, which plans reach postures that avoid obstructions. In this model, the task space is partitioned into small regions called cells. For a given human figure, each cell is linked to a memory that stores various alternative postures for reaching the cell. When a posture planning problem is given in terms of a target and an obstruction configuration, the model examines postures belonging to the relevant cell, selects collision-free ones and modifies them to exactly meet the hand target acquisition constraint. Simulation results showed that the MBPP model is capable of rapidly and robustly planning reach postures for various scenarios.     

A long short-term memory-based model for greenhouse climate prediction

Greenhouses can grow many off-season vegetables and fruits, which improves people's quality of life. Greenhouses can also help crops resist natural disasters and ensure the stable growth of crops. However, it is highly challenging to carefully control the greenhouse climate. Therefore, the proposal of a greenhouse climate prediction model provides a way to solve this challenge. We focus on the six climatic factors that affect crops growth, including temperature, humidity, illumination, carbon dioxide concentration, soil temperature and soil humidity, and propose a GCP_lstm model for greenhouse climate prediction. The climate change in greenhouse is nonlinear, so we use long short-term memory (LSTM) model to capture the dependence between historical climate data. Moreover, the short-term climate has a greater impact on the future trend of greenhouse climate change. Therefore, we added a 5-min time sliding window through the analysis experiment. In addition, sensors sometimes collect wrong climate data. Based on the existence of abnormal data, our model still has good robustness. We experienced our method on the data sets of three vegetables: tomato, cucumber and pepper. The comparison shows that our method is better than other comparison models.     

A finger motion model for reach and grasp

This study aimed to develop a model that describes human finger motion for simulation of reach and grasp for selected objects and tasks. Finger joint angles and timing of their changes were measured for six subjects as they reached 20-40 cm and grasped cylindrical handles (1.3-10.2 cm D) of varying orientation (vertical/axial). The empirical results from multiple regression analyses served as inputs to allow a fourth order polynomial to predict motion of each finger joint. The proposed model showed good fit with observations, with high coefficients of determination from 0.54 to 1 and reasonable errors from 0.04° to 5.44° for all conditions considered. The proposed finger motion model was implemented in an existing kinematic hand model to employ a contact algorithm for refined prediction of grip posture and to illustrate its predictive power by graphically displaying the opening and closing of the hand.

Relevance to industry

Finger joint motions during reach and grasp are needed for prediction of (1) tendon excursions for study of work-related musculoskeletal disorders, (2) required space for the hand, (3) finger locations on work objects, and (4) hand grip postures and strength.     

A model of reaction for planning in dynamic environments

One shortcoming with most AI planning systems has been an inability to deal with execution-time discrepancies between actual and expected situations. Often, these exception situations jeopardize the immediate integrity and safety of the planning agent or its surroundings, with the only recourse being more time-consuming plan generation. In order to avoid such situations, potential exceptions must be predicted during plan execution. Since many application domains (particularly for autonomous systems) are inherently dynamic — in the sense that information is at best incomplete, perhaps erroneous, and changes over time independent of a planning agent's actions — managing action in the world becomes a difficult problem. Action and events in dynamic worlds must be monitored in order to coordinate an agent's actions with its surroundings. This allows the agent to predict and plan for potential future exception situations while acting in the present.This paper introduces an approach to autonomous reaction in dynamic environments. We have avoided the traditional distinction between generating and then executing plans through the use of a dynamic reaction system, which handles potential exception situations gracefully as it carries out assigned tasks. The reaction system manages constraints imposed by ongoing activity in the world, as well as those derived from long-term planning, to control observable behaviour. This approach provides the necessary stimulus/response behaviour required in dynamic situations, while using goal-directed constraints as heuristics for improved reactions.We present an overview of the salient features of dynamic worlds and their impact on traditional planning, introduce our model of dynamic reactivity, describe an implementation of the model and its performance in a dynamic simulation environment, and present an architecture incorporating long-term planning with short-term reactance suitable for autonomous systems applications.     

A Petri net model for reasoning in the presence of inconsistency

Petri nets provide a promising framework for executing queries to logic programs. Petri net models for query processing in logic programming were initially developed by T. Murata and D. Zhang (IEEE Trans. Software Eng., vol.14, no.4, p.481-97, 1988). It is shown how this framework can be extended to be applicable to reasoning in the presence of inconsistency. This yields a Petri net model of programs that be inconsistent in classical logic. This is interesting because large expert systems may often contain inconsistent information. The Petri net method proposed suggests a robust way of preventing inconsistency from infecting a system and rendering it useless     

A privacy-preserving model for multi-agent propositional planning

Abstract

Over the last years, the planning community has formalised several models and approaches to multi-agent (MA) propositional planning. One of the main motivations in MA planning is that some or all agents have private knowledge that cannot be communicated to other agents during the planning process and the plan execution. We argue that the existing models of the multi-agent planning task do not maintain the agents’ privacy when a (strict) subset of the involved agents share confidential knowledge, or when the identity/existence of at least one agent is confidential. In this paper, first we propose a model of the MA-planning tasks that preserves the privacy of the involved agents when this happens. Then we investigate an algorithm based on best first search for our model that uses some new heuristics providing a trade-off between accuracy and agents’ privacy. Finally, an experimental study compares the effectiveness of using the proposed heuristics.     

A statistical model including age to predict passenger postures in the rear seats of automobiles

Few statistical models of rear seat passenger posture have been published, and none has taken into account the effects of occupant age. This study developed new statistical models for predicting passenger postures in the rear seats of automobiles. Postures of 89 adults with a wide range of age and body size were measured in a laboratory mock-up in seven seat configurations. Posture-prediction models for female and male passengers were separately developed by stepwise regression using age, body dimensions, seat configurations and two-way interactions as potential predictors. Passenger posture was significantly associated with age and the effects of other two-way interaction variables depended on age. A set of posture-prediction models are presented for women and men, and the prediction results are compared with previously published models. This study is the first study of passenger posture to include a large cohort of older passengers and the first to report a significant effect of age for adults. The presented models can be used to position computational and physical human models for vehicle design and assessment.

Practitioner Summary:

The significant effects of age, body dimensions and seat configuration on rear seat passenger posture were identified. The models can be used to accurately position computational human models or crash test dummies for older passengers in known rear seat configurations.     

Motion planning in the presence of movable obstacles

Most motion planning algorithms have dealt with motion in a static workspace, or more recently, with motion in a workspace that changes in a known manner. We consider the problem of finding collision-free motions in a changeable workspace. That is, we wish to find a motion for an object where the object is permitted to move some of the obstacles. In such a workspace, the final positions of the movable obstacles may or may not be part of the goal. In the case where the final positions of the obstacles are specified, the general problem is shown to be PSPACE-hard. In the case where the final positions of the obstacles are unspecified, the motion planning problem is shown to be NP-hard. Algorithms that run inO(n ³) time are presented for the case where there is only one movable obstacle in a polygonal environment withn corners and the object to be moved and the obstacle are convex polygons of constant complexity.     

A new model in the energy policy planning

In this paper the application of the Analytic Hierarchy Process in Energy policy planning is introduced. The application of the process to energy policy formulation is explained in greater detail. A desired scenario for the United States' energy future is defined, desired strategies for the attainment of the scenario are formulated, constraints binding each strategy is defined, and policies which would resolve these constraints are formulated and assigned priorities.     

A mathematical model of planning in the prefrontal cortex

The prefrontal cortex (PFC) is involved in many complex cognitive functions such as problem-solving, planning, reasoning, and decision-making. However, the biological mechanisms of these computations are not clear. To understand these mechanisms, we theoretically consider the experimental result of a path-planning task by Mushiake et al. using a mathematical model referred to as the potential network model. The simulation results show that our model can take the correct path in most trials, regardless of the goal positions and the block patterns in the task. Furthermore, our model reproduces the characteristics of the neuronal activity in both the PFC and the primary motor cortex. This study reveals that although the potential network model is abstract, it can be useful in modelling higher brain functions. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

A model for integrating process planning and production planning and control in machining processes

The goal of process planning is to propose the routing of a previously designed part and results in a sequence of operations and their parameters. It concerns and requires detailed information about the process. The goal of production planning, on the other hand, is to schedule, sequence and launch the orders introduced on the routing sheet into the job-shop according to the enterprise's strategic goal and the actual conditions of the production plant. The goals, information and decisions taken in process planning and production planning and control are often very different and, because of that, it is very difficult to integrate them.

The objective of this work is to develop a model that can be applied in the future to the development of an integrated process planning and scheduling tool using an integrated definition (IDEF) methodology to design an activity model, which integrates process and production planning in metal removal processes. An activity model will be used to develop a system that allows the user to plan the process and the production at the same time in collaborative engineering work. To design the activity model, a wide range of parts were evaluated and processed in an actual job-shop factory. Several activities were developed in detail to be tested in real cases, and an example of one of them is introduced in this article.     

A hierarchical approach for planning a multisensor multizone search for a moving target

This paper deals with a well-known problem in the general area of search theory: optimize the search resources sharing so as to maximize the probability of detection of a (moving) target. However, the problem we consider here considerably differs from the classical one. First, there is a bilevel search planning and we have to consider jointly discrete and continuous optimization problems. To this perspective original methods are proposed within a common framework. Furthermore, this framework is sufficiently general and versatile so as to be easily and successfully extended to the difficult problem of the multizone multisensor search planning for a Markovian target.     

A DC programming approach for planning a multisensor multizone search for a target

In this paper, we consider a well-known problem in the general area of search theory: planning a multisensor in multizone search so as to maximize the probability of detection of a target under a given resource effort to be shared. We propose a new optimization model that is a nonlinear mixed 0–1 programming problem. This problem is then reformulated as a DC (Difference of Convex) functions program via an exact penalty technique. DC programming and DCA (DC algorithm) have been investigated for solving the resulting DC program. Numerical experiments demonstrate the efficiency and the superiority of the proposed algorithm in comparison with the existing method.     

A comparison of information functions and search strategies for sensor planning in target classification

This paper investigates the comparative performance of several information-driven search strategies and decision rules using a canonical target classification problem. Five sensor models are considered: one obtained from classical estimation theory and four obtained from Bernoulli, Poisson, binomial, and mixture-of-binomial distributions. A systematic approach is presented for deriving information functions that represent the expected utility of future sensor measurements from mutual information, Rènyi divergence, Kullback-Leibler divergence, information potential, quadratic entropy, and the Cauchy-Schwarz distance. The resulting information-driven strategies are compared to direct-search, alert-confirm, task-driven (TS), and log-likelihood-ratio (LLR) search strategies. Extensive numerical simulations show that quadratic entropy typically leads to the most effective search strategy with respect to correct-classification rates. In the presence of prior information, the quadratic-entropy-driven strategy also displays the lowest rate of false alarms. However, when prior information is absent or very noisy, TS and LLR strategies achieve the lowest false-alarm rates for the Bernoulli, mixture-of-binomial, and classical sensor models.     

A stochastic programming process model for investment planning

The usual programming formulation of an industrial process model is extended to incorporate parameters and demand uncertainties by modeling it as a stochastic linear program with simple recourse (SLPR). The SLPR is solved using the less restrictive assumptions that only the mean and variance of the stochastic entities (but not their distribution) are known. An algorithm is derived to solve the particular form of SLPR. The methodology is applied to a study of the steel industry in India with a novel way of modeling investment and economies-of-scale.     

A computerised system for analysing working postures in agriculture

This paper aims to show a computerised program for analysing work postures in agriculture. The system contains two types of routines for studies of observation, Working Posture Analysing Sytem (WOPALAS) and a simple Video film technique for Registration and Analysis of working postures and movements (VIRA). Furthermore the system consists of programs for moment calculations and routines for registration of problems in the locomotive organs. The computerised system provides several advantages compared with traditional manual systems. For the operator, it is a less demanding work and the analyses are more accurate. The system rapidly provides the results of an analysis.     

A reliable model for global planning of mobile networks

In this paper, we propose a global model for WiMAX networks planning. This model represents the network planning problem and helps to solve it entirely without dividing it into several subproblems. The objective of the model is to minimize the cost of the network while maximizing its survivability. The model has been compared to a sequential model with the same constraints, which consists in solving the subproblems sequentially, and to a global model without reliability constraints. The results show that the proposed model performs on an average 25% better than the other models.     

A biobjective model for production planning in a cement factory

This paper is an illustration of the application of biobjective linear programming to determining the optimal allocation of production materials to the major production facilities. The system under consideration is a cement factory. However, the approach and methodology are sufficiently general that the method can be applied to similar problems in other firms.Two objectives of the factory are distinguished: minimization of manufacturing cost and maximization of capacity utilization. Addressed to the needs of the biobjective case, a technique, which is referred to as ‘compromise constraint’ technique, is introduced.