Optimal Movement Strategies Research Articles

Research on the Multi-Robot Cooperative Pursuit Strategy Based on the Zero-Sum Game and Surrounding Points Adjustment

Making full use of the cooperation of multi-robots can improve the success rate of apursuit task. Therefore, this paper proposes a multi-robot cooperative pursuit strategy based on the zero-sum game and surrounding points adjustment. First, a mathematical description of the multi-robot pursuit problem is constructed, and the zero-sum game model is established considering the cooperation of the pursuit robots and the confrontation between the pursuit robots and the escape robot. By solving the game model, the optimal movement strategies of the pursuit robots and the escape robot are obtained. Then, the position adjustment method of the pursuit robots is studied based on the Hungarian algorithm, and the pursuit robots are controlled to surround the escape robot. Based on this, a multi-robot cooperative pursuit strategy is proposed that divides the pursuit process into two stages: pursuit robot position adjustment and game pursuit. Finally, the correctness and effectiveness of the multi-robot cooperative pursuit strategy are verified with simulation experiments. The multi-robot cooperative pursuit strategy allows the pursuit robots to capture the escape robot successfully without conflicts among the pursuit robots. It can be seen from the documented simulation experiments that the success rate of the pursuit task using the strategy proposed in this paper is 100%.

Potential Mediators of Load-Related Changes in Movement Complexity in Young, Healthy Adults.

Movement screening has become increasingly popular among tactical professionals. This popularity has motivated the design of interventions that cater to improving outcomes on the screens themselves, which are often scored in reference to an objective norm. In contrast to the assumptions underlying this approach, dynamical systems theory suggests that movements arise as a function of continuously evolving constraints and that optimal movement strategies may not exist. To date, few data address behavioral complexity in the fundamental movement tasks commonly used in clinical screenings. To provide evidence of complex variability during movement screens and test the role of modifiable-that is, trainable-constraints in mediating loss of complexity during experimental-task manipulations. Crossover study. Research laboratory. Twenty-five male (age = 23.96 ± 3.74 years, height = 178.82 ± 7.51 cm, mass = 79.66 ± 12.66 kg) and 25 female (age = 22.00 ± 2.02 years, height = 165.40 ± 10.24 cm, mass = 63.98 ± 11.07 kg) recreationally active adults. Participants performed tests of balance, range of motion, and strength. Additionally, they performed cyclical movement tasks under a control (C) condition and while wearing an 18.10-kg weight vest (W). Ground reaction forces were sampled at 1000 Hz and used to calculate center of pressure during cyclical movement tests. Multivariate multiscale entropy (MMSE) for the center-of-pressure signal was then calculated. Condition effects (C versus W) were analyzed using paired t tests, and penalized varying-coefficients regression was used to identify models predicting entropy outcomes from balance, range of motion, and strength. The MMSE decreased during the W condition (MMSEC > MMSEW; t49 range = 3.17-5.21; all P values < .01). Moderate evidence supported an association between modifiable constraints and behavioral complexity, but a role in mediating load-related loss of complexity was not demonstrated.

Cryoem-cloud-tools: A software platform to deploy and manage cryo-EM jobs in the cloud.

Access to streamlined computational resources remains a significant bottleneck for new users of cryo-electron microscopy (cryo-EM). To address this, we have developed tools that will submit cryo-EM analysis routines and atomic model building jobs directly to Amazon Web Services (AWS) from a local computer or laptop. These new software tools ("cryoem-cloud-tools") have incorporated optimal data movement, security, and cost-saving strategies, giving novice users access to complex cryo-EM data processing pipelines. Integrating these tools into the RELION processing pipeline and graphical user interface we determined a 2.2 Å structure of ß-galactosidase in ∼55 h on AWS. We implemented a similar strategy to submit Rosetta atomic model building and refinement to AWS. These software tools dramatically reduce the barrier for entry of new users to cloud computing for cryo-EM and are freely available at cryoem-tools.cloud.

Optimal eye movement strategies: a comparison of neurosurgeons gaze patterns when using a surgical microscope.

Previous studies have consistently demonstrated gaze behaviour differences related to expertise during various surgical procedures. In micro-neurosurgery, however, there is a lack of evidence of empirically demonstrated individual differences associated with visual attention. It is unknown exactly how neurosurgeons see a stereoscopic magnified view in the context of micro-neurosurgery and what this implies for medical training. We report on an investigation of the eye movement patterns in micro-neurosurgery using a state-of-the-art eye tracker. We studied the eye movements of nine neurosurgeons while performing cutting and suturing tasks under a surgical microscope. Eye-movement characteristics, such as fixation (focus level) and saccade (visual search pattern), were analysed. The results show a strong relationship between the level of microsurgical skill and the gaze pattern, whereas more expertise is associated with greater eye control, stability, and focusing in eye behaviour. For example, in the cutting task, well-trained surgeons increased their fixation durations on the operating field twice as much as the novices (expert, 848ms; novice, 402ms). Maintaining steady visual attention on the target (fixation), as well as being able to quickly make eye jumps from one target to another (saccades) are two important elements for the success of neurosurgery. The captured gaze patterns can be used to improve medical education, as part of an assessment system or in a gaze-training application.

The Energetic Benefit of Robotic Gait Selection—A Case Study on the Robot RAM&lt;italic&gt;one&lt;/italic&gt;

Should a legged robot use different gaits at different desired speeds? If so, what constitutes these gaits? This work examines these questions through a case study on the planar bipedal robot RAM one . Using a realistic model of the robot, this paper presents the outcome of a series of trajectory optimizations which minimize the electrical cost of transport and find the associated optimal motion strategies. These optimizations show that at low speeds it is most economical to perform a ballistic walking gait with an instantaneous transfer of support. At higher speeds, spring-mass running with an extended air-phase becomes the optimal gait. Additionally, it is illustrated that the optimal running gait is conducted with the knee joints pointing away from rather than towards the direction of travel. The transition between ballistic walking and spring-mass running happens at a speed of 1.04 m/s, and switching can reduce energy expenditure by up to 259%.

Using the Newly Developed Floor-Sitting Movement Analysis Proforma to Study the Effect of Age and Activity on Floor-Sitting in Indian Adults.

Floor-sitting is culturally relevant to the Indian context. The present study aimed to examine the effect of age and activity on the movement patterns used and time taken to perform floor-sitting in Indian adults. Video-recordings of 30 young (23.30±2.53years) and 30 older (69.67±6.45years) adults performing floor-sitting without and with an activity (simulated feeding) were analyzed using the Floor-sitting Movement Analysis Proforma (FMAP) developed for the study. For inter-rater reliability of the FMAP, two raters analyzed the performance of a random sample of 20 participants. An almost perfect inter-rater agreeability (κ≥.8) was obtained for the FMAP. Cross-legged sitting was the most preferred (95%) floor-sitting position. Older adults used more number of movement components, asymmetrical patterns, more support, and more time (p<.001) as compared to the young adults. The activity facilitated the use of optimal movement strategies in young and older adults. The activity significantly increased time taken to rise from floor-sitting (p=.004). The study establishes the influence of age and activity on performance of floor-sitting. Older adults use lower developmental movement patterns that maybe a "normal" adaptation to age-related sensorimotor changes. Retraining of floor-sitting isa "culturally" desiredgoal among Indian adults and should involvethe practice of age-appropriate movement patterns in the context of meaningful activities.

Can virtual reality trainers improve the compliance discrimination abilities of trainee surgeons?

The assessment of tissue compliance using a handheld tool is an important skill in medical areas such as laparoscopic and dental surgery. The increasing prevalence of virtual reality devices raises the question of whether we can exploit these systems to accelerate the training of compliance discrimination in trainee surgeons. We used a haptic feedback device and stylus to assess the abilities of naïve participants to detect compliance differences with and without knowledge of results (KR) (groups 1 and 2), as well as the abilities of participants who had undergone repetitive training over several days (group 3). Kinematic analyses were carried out to objectively measure the probing action. Untrained participants had poor detection thresholds (mean just noticeable difference, JND = 33%), and we found no effect of KR (provided after each trial) on performance (mean JND = 35%). Intensive training dramatically improved group performance (mean JND = 12%). Probing action (in particular, slower movement execution) was associated with better detection thresholds, but training did not lead to systematic changes in probing behaviour. These findings set a benchmark for training systems that act to increase perceptual sensitivity and guide the learner toward optimal movement strategies to improve discrimination.

Optimization of the anterior cruciate ligament injury prevention paradigm: novel feedback techniques to enhance motor learning and reduce injury risk.

Primary anterior cruciate ligament (ACL) injury prevention programs effectively reduce ACL injury risk in the short term. Despite these programs, ACL injury incidence is still high, making it imperative to continue to improve current prevention strategies. A potential limitation of current ACL injury prevention training may be a deficit in the transfer of conscious, optimal movement strategies rehearsed during training sessions to automatic movements required for athletic activities and unanticipated events on the field. Instructional strategies with an internal focus of attention have traditionally been utilized, but may not be optimal for the acquisition of the control of complex motor skills required for sports. Conversely, external-focus instructional strategies may enhance skill acquisition more efficiently and increase the transfer of improved motor skills to sports activities. The current article will present insights gained from the motor-learning domain that may enhance neuromuscular training programs via improved skill development and increased retention and transfer to sports activities, which may reduce ACL injury incidence in the long term.

Effect of arm swing on single-step balance recovery

Balance recovery techniques are useful not only in preventing falls but also in many sports activities. The step strategy plays an important role especially under intense perturbations. However, relatively little is known about the effect of arm swing on stepping balance recovery although considerable arm motions have been observed. The purpose of this study was to examine how the arms influence kinematic and kinetic characteristics in single-step balance recovery. Twelve young male adults were released from three forward-lean angles and asked to regain balance by taking a single step under arm swing (AS) and arm constrained (AC) conditions. It was found that unconstrained arms had initial forward motion and later upward motion causing increased moment of inertia of the body, which decreased falling angular velocity and allowed more time for stepping. The lengthened total balance time included weight transfer and stepping time, although duration increase in the latter was significant only at the largest lean angle. In contrast, step length, step velocity, and vertical ground reaction forces on the stepping foot were unaffected by arm swing. Future studies are required to investigate optimal movement strategies for the arms to coordinate with other body segments in balance recovery and injury reduction.

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

Optimal migratory behavior in spatially-explicit seasonal environments

Mass migrations of vertebrate and arthropod species have long been perceived as some of the most mystical phenomena in nature. And for eons, we have been asking ourselves why animals migrate. Ecologically, migration provides benefits in currencies of survival, growth, and reproduction, allowing animals to exploit environmental heterogeneities in space and time. Yet for a given environment, different species respond with different behaviors -- some travelling large distances, while others shelter in place. Part of the explanation of this distinction is the physiological differences between species and their ability to move. But is physiological difference a necessary pre-condition? Or can environmental heterogeneity itself be sufficient for bifurcations in movement behavior? &nbsp In this paper, we address this last question using a model for the evolution of migration in a density-independent, spatially-explicit setting when movement is costly based on the harvesting a single resource that varies in space and time. We use optimal control methods to calculate the optimal movement patterns in several different situations. In this framework, optimal movement strategies can be classified into six different regimes, based on the cost of movement, the strength and scale of seasonal resource variation, and the degree of trade-off between short-term and long-term benefits. We show that a migratory niche emerges in response to inseparable spatio-temporal environmental heterogeneity, and that this niche can bifurcate from changes to the resource distribution without need for physiological divergence.

Optimal within-patch movement strategies for optimising patch residence time: an agent-based modelling approach

Several optimisation models, like the marginal value theorem (MVT), have been proposed to predict the optimal time foraging animals should remain on patches of resources. These models do not clearly indicate, however, how animals can follow the corresponding predictions. Hence, several proximate patch-leaving decision rules have been proposed. Most if not all of these are based on the animals’ motivation to remain on the patches, but the real behaviours involved in such motivation actually still remain to be identified. Since animals are usually exploiting patches of resources by walking, we developed a model simulating the intra-patch movement decisions of time-limited animals exploiting resources distributed in delimited patches in environments with different resource abundances and distributions. The values of the model parameters were optimised in the different environments by means of a genetic algorithm. Results indicate that simple modifications of the walking pattern of the foraging animals when resources are discovered can lead to patch residence times that appear consistent with the predictions of the MVT. These results provide a more concrete understanding of the optimal patch-leaving decision rules animals should adopt in different environments.

The ideal free strategy with weak Allee effect

This paper examines the interplay between optimal movement strategies and the weak Allee effect within the context of two competing species in a spatially heterogenous environment. When both species have the same populations dynamics, previous studies identified an ‘ideal free’ strategy which is able to exclude any other competitor playing a ‘non-ideal free’ strategy. We find that if the ideal free disperser is subject to a weak Allee effect, a competing species utilizing very weak or very strong advection will still be excluded despite having superior population dynamics. However, for intermediate advection rates, such a competitor can invade the ideal free disperser and even drive it to extinction. Not only do these results enhance ecological understanding of competing species, but they provide insight into the theory of reaction–advection–diffusion models when the usual linearization techniques offer no information.

Very Slow Search and Reach: Failure to Maximize Expected Gain in an Eye-Hand Coordination Task

We examined an eye-hand coordination task where optimal visual search and hand movement strategies were inter-related. Observers were asked to find and touch a target among five distractors on a touch screen. Their reward for touching the target was reduced by an amount proportional to how long they took to locate and reach to it. Coordinating the eye and the hand appropriately would markedly reduce the search-reach time. Using statistical decision theory we derived the sequence of interrelated eye and hand movements that would maximize expected gain and we predicted how hand movements should change as the eye gathered further information about target location. We recorded human observers' eye movements and hand movements and compared them with the optimal strategy that would have maximized expected gain. We found that most observers failed to adopt the optimal search-reach strategy. We analyze and describe the strategies they did adopt.

Tracking an omnidirectional evader with a differential drive robot

In this paper we consider the surveillance problem of tracking a moving evader by a nonholonomic mobile pursuer. We deal specifically with the situation in which the only constraint on the evader's velocity is a bound on speed (i.e., the evader is able to move omnidirectionally), and the pursuer is a nonholonomic, differential drive system having bounded speed. We formulate our problem as a game. Given the evader's maximum speed, we determine a lower bound for the required pursuer speed to track the evader. This bound allows us to determine at the beginning of the game whether or not the pursuer can follow the evader based on the initial system configuration. We then develop the system model, and obtain optimal motion strategies for both players, which allow us to establish the long term solution for the game. We present an implementation of the system model, and motion strategies, and also present simulation results of the pursuit-evasion game.

Simple summation rule for optimal fixation selection in visual search

When searching for a known target in a natural texture, practiced humans achieve near-optimal performance compared to a Bayesian ideal searcher constrained with the human map of target detectability across the visual field [Najemnik, J., & Geisler, W. S. (2005). Optimal eye movement strategies in visual search. Nature, 434, 387–391]. To do so, humans must be good at choosing where to fixate during the search [Najemnik, J., & Geisler, W.S. (2008). Eye movement statistics in humans are consistent with an optimal strategy. Journal of Vision, 8(3), 1–14. 4]; however, it seems unlikely that a biological nervous system would implement the computations for the Bayesian ideal fixation selection because of their complexity. Here we derive and test a simple heuristic for optimal fixation selection that appears to be a much better candidate for implementation within a biological nervous system. Specifically, we show that the near-optimal fixation location is the maximum of the current posterior probability distribution for target location after the distribution is filtered by (convolved with) the square of the retinotopic target detectability map. We term the model that uses this strategy the entropy limit minimization (ELM) searcher. We show that when constrained with human-like retinotopic map of target detectability and human search error rates, the ELM searcher performs as well as the Bayesian ideal searcher, and produces fixation statistics similar to human.

Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Mobile sensors cover more area over a fixed period of time than do the same number of stationary sensors. However, the quality of coverage (QoC) achieved by mobile sensors depends on the velocity, mobility pattern, number of mobile sensors deployed, and the dynamics of the phenomenon being sensed. The gains attained by mobile sensors over static sensors and the optimal motion strategies for mobile sensors are not well understood. In this paper, we consider the following event capture problem: the events of interest arrive at certain points in the sensor field and disappear according to known arrival and departure time distributions. An event is said to be captured if it is sensed by one of the mobile sensors before it fades away. We analyze how the QoC scales with velocity, path, and number of mobile sensors. We characterize cases where the deployment of mobile sensors has no advantage over static sensors, and find the optimal velocity pattern that a mobile sensor should adopt. We also present algorithms for two motion planning problems: 1) for a single sensor, what is the sensor trajectory and the <emphasis emphasistype="italic">minimum speed</emphasis> required to satisfy a bound on the event loss probability and 2) for sensors with fixed speed, what is the <emphasis emphasistype="italic">minimum number of sensors</emphasis> required to satisfy a bound on the event loss probability. When the robots are restricted to move along a line or a closed curve, our algorithms return the optimal velocity for the minimum velocity problem. For the minimum sensor problem, the number of sensors used is within a factor of 2 of the optimal solution. For the case where the events occur at arbitrary points on a plane, we present heuristic algorithms for the aforementioned motion planning problems and bound their performance with respect to the optimal. </para>

Optimal eye movement strategies in visual search

To perform visual search, humans, like many mammals, encode a large field of view with retinas having variable spatial resolution, and then use high-speed eye movements to direct the highest-resolution region, the fovea, towards potential target locations. Good search performance is essential for survival, and hence mammals may have evolved efficient strategies for selecting fixation locations. Here we address two questions: what are the optimal eye movement strategies for a foveated visual system faced with the problem of finding a target in a cluttered environment, and do humans employ optimal eye movement strategies during a search? We derive the ideal bayesian observer for search tasks in which a target is embedded at an unknown location within a random background that has the spectral characteristics of natural scenes. Our ideal searcher uses precise knowledge about the statistics of the scenes in which the target is embedded, and about its own visual system, to make eye movements that gain the most information about target location. We find that humans achieve nearly optimal search performance, even though humans integrate information poorly across fixations. Analysis of the ideal searcher reveals that there is little benefit from perfect integration across fixations--much more important is efficient processing of information on each fixation. Apparently, evolution has exploited this fact to achieve efficient eye movement strategies with minimal neural resources devoted to memory.

Optimal eye movement strategies in visual search

To perform visual search, humans, like many mammals, encode a large field of view with retinas having variable spatial resolution, and then use high-speed eye movements to direct the highest-resolution region, the fovea, towards potential target locations. Good search performance is essential for survival, and hence mammals may have evolved efficient strategies for selecting fixation locations. Here we address two questions: what are the optimal eye movement strategies for a foveated visual system faced with the problem of finding a target in a cluttered environment, and do humans employ optimal eye movement strategies during a search? We derive the ideal bayesian observer for search tasks in which a target is embedded at an unknown location within a random background that has the spectral characteristics of natural scenes. Our ideal searcher uses precise knowledge about the statistics of the scenes in which the target is embedded, and about its own visual system, to make eye movements that gain the most information about target location. We find that humans achieve nearly optimal search performance, even though humans integrate information poorly across fixations. Analysis of the ideal searcher reveals that there is little benefit from perfect integration across fixations--much more important is efficient processing of information on each fixation. Apparently, evolution has exploited this fact to achieve efficient eye movement strategies with minimal neural resources devoted to memory.

An integer linear programming-based tool for wireless sensor networks

Wireless sensor networks will be widely deployed in the future for monitoring important environmental conditions, security, and health care. One of the most important challenges in the implementation of such networks is minimizing energy dissipation. Given that many of the energy optimization problems defined for sensor networks are very hard, heuristics are commonly employed. Evaluating the effectiveness of these heuristics, i.e., how close do they come to the optimal solutions, is a challenge. While algorithms that give optimal solutions cannot be on-line (since they are expensive), if they are used off-line, they can provide invaluable insight to improve existing heuristics and to derive new ones. In this paper, we present an integer linear programming (ILP)-based tool that can be used to evaluate optimal solutions for communication energy optimization in sensor networks under specific constraints. This tool, which is based on the required sensing and communication schedules, determines optimal sensor movement and communication strategies to minimize energy consumption due to inter-sensor communication. The tool can also accommodate several constraints related to movement capabilities of sensor nodes, their battery capacities, and their communication ranges since all these can be expressed in a linear form. In addition, it can also work with objective functions other than minimizing communication energy. Our experience with the tool indicates that it is very useful for studying different scenarios under which an objective function needs to be optimized.