Online collision avoidance trajectory planning for spacecraft proximity operations with uncertain obstacle

The spacecraft relative motion trajectory planning is one of the enabling techniques for autonomous proximity operations, especially in the increasingly complicated mission environments. Most traditional trajectory planning methods focus on improving the performance criteria in the deterministic conditions, whereas various uncertain elements in practice would significantly degrade the trajectory performance. Considering the uncertainties underlying the collision avoidance constraints, this paper suggests a model predictive control based online trajectory planning framework in which the obstacle information in higher-precision would be consistently updated by the onboard sensor. To improve the computational efficiency of the online planning framework, the rotating hyperplane (RH) technique is utilized to transform the nonlinear ellipsoidal keep-out zone constraints into convex formulations. And the concept of rotation window is introduced to eliminate the unexpected mismatch between the spacecraft motion and hyperplane rotation in the conventional RH method, which in sequence improves the RH method’s capability for multiple obstacle avoidance problem. Moreover, a three-dimensional (3-D) extension strategy is proposed to simplify the computation procedure when applying the RH method for a 3-D collision avoidance problem. Numerical simulations are carried out to validate the performance of the proposed online trajectory planning framework in addressing the uncertain collision avoidance constraints.

Planning and optimization of robotic pick-and-place operations in highly constrained industrial environments

Purpose The purpose of this paper is to give a comprehensive solution method for the manipulation of parts with complex geometries arriving in bulk into a robotic assembly cell. As bin-picking applications are still not reliable in intricate workcells, first, the problem is transformed to a semi-structured pick-and-place application, then by collecting and organizing the required process planning steps, a methodology is formed to achieve reliable factory applications even in crowded assembly cell environments. Design/methodology/approach The process planning steps are separated into offline precomputation and online planning. The offline phase focuses on preparing the operation and reducing the online computational burdens. During the online phase, the parts laying in a semi-structured arrangement are first recognized and localized based on their stable equilibrium using two-dimensional vision. Then, the picking sequence and corresponding collision-free robot trajectories are planned and optimized. Findings The proposed method was evaluated in a geometrically complex experimental workcell, where it ensured precise, collision-free operation. Moreover, the applied planning processes could significantly reduce the execution time compared to heuristic approaches. Research limitations/implications The methodology can be further generalized by considering multiple part types and grasping modes. Additionally, the automation of grasp planning and the enhancement of part localization, sequence planning and path smoothing with more advanced solutions are further research directions. Originality/value The paper proposes a novel methodology that combines geometrical computations, image processing and combinatorial optimization, adapted to the requirements of flexible pick-and-place applications. The methodology covers each required planning step to reach reliable and more efficient operation.

Enhancing Planning Behavior during Retirement: Effects of a Time Perspective Based Training Intervention

Time perspective is a psychological construct that reflects the way people view time. Two schools of thought exist that theorize how this temporal mindset affects behavior—dominant and balanced. We applied dominant and balanced time perspective frameworks separately to two versions of an online intervention that aimed to promote goal-setting and accumulation of essential retirement resources (health, physical, social, cognitive and emotional) and compared effects with a control group. The effectiveness of the intervention was tested with 109 US retirees using a 4-wave design over a 6-month period. Linear mixed models showed an increase in health goal striving for the balanced group at posttraining and gains were maintained at the 3-month time point. Both training groups demonstrated an increase in the number and specificity of goals at posttraining and 3-months. Applying a time perspective framework to an online planning intervention for retirees shows promise in promoting planning for retirement resources. Practical implications, limitations, and suggestions for developing future interventions are discussed.

Hamiltonian coordination primitives for decentralized multiagent navigation

We focus on decentralized navigation among multiple non-communicating agents in continuous domains without explicit traffic rules, such as sidewalks, hallways, or squares. Following collision-free motion in such domains requires effective mechanisms of multiagent behavior prediction. Although this prediction problem can be shown to be NP-hard, humans are often capable of solving it efficiently by leveraging sophisticated mechanisms of implicit coordination. Inspired by the human paradigm, we propose a novel topological formalism that explicitly models multiagent coordination. Our formalism features both geometric and algebraic descriptions enabling the use of standard gradient-based optimization techniques for trajectory generation but also symbolic inference over coordination strategies. In this article, we contribute (a) HCP (Hamiltonian Coordination Primitives), a novel multiagent trajectory-generation pipeline that accommodates spatiotemporal constraints formulated as symbolic topological specifications corresponding to a desired coordination strategy; (b) HCPnav, an online planning framework for decentralized collision avoidance that generates motion by following multiagent trajectory primitives corresponding to high-likelihood, low-cost coordination strategies. Through a series of challenging trajectory-generation experiments, we show that HCP outperforms a trajectory-optimization baseline in generating trajectories of desired topological specifications in terms of success rate and computational efficiency. Finally, through a variety of navigation experiments, we illustrate the efficacy of HCPnav in handling challenging multiagent navigation scenarios under homogeneous or heterogeneous agents across a series of environments of different geometry.

Incorporating Queueing Dynamics into Schedule-Driven Traffic Control

Key to the effectiveness of schedule-driven approaches to real-time traffic control is an ability to accurately predict when sensed vehicles will arrive at and pass through the intersection. Prior work in schedule-driven traffic control has assumed a static vehicle arrival model. However, this static predictive model ignores the fact that the queue count and the incurred delay should vary as different partial signal timing schedules (i.e., different possible futures) are explored during the online planning process. In this paper, we propose an alternative arrival time model that incorporates queueing dynamics into this forward search process for a signal timing schedule, to more accurately capture how the intersection’s queues vary over time. As each search state is generated, an incremental queueing delay is dynamically projected for each vehicle. The resulting total queueing delay is then considered in addition to the cumulative delay caused by signal operations. We demonstrate the potential of this approach through microscopic traffic simulation of a real-world road network, showing a 10-15% reduction in average wait times over the schedule-driven traffic signal control system in heavy traffic scenarios.

EFFECTS OF STRATEGIC AND UNPRESSURED WITHIN-TASK PLANNING ON IRANIAN INTERMEDIATE EFL LEARNERS’ ORAL PRODUCTION

Background/Purpose: This study is an attempt to investigate the effect of strategic planning and unpressured within-task planning on Iranian EFL learners’ oral performance. Methodology: Following a quasi-experimental research design, 60 intermediate EFL Iranian learners were recruited as the participants of the study and they were assigned into two groups of 30. In group one, the participants were asked to perform the task under unpressured online planning condition. In group two, the learners were asked to do the task under strategic planning condition. The pre and post-tests included story-telling narratives tasks followed with a 15-minute silent movie under two planning conditions for the two groups. Findings: The results of data analysis showed that pre-task strategic planning and within-task planning (online planning) had a positive effect on learners’ oral production; hence, planning time provided opportunity for learners to be able to produce more fluent, accurate, and complex language than no-planners. The results indicated that the participants in the strategic planning group (STPG) significantly outperformed the participants in unpressured within-task-planning (UWPG), leading us to claim that strategic planning was more effective than unpressured within-task planning in improving oral production. Contributions: This study has numerous benefits for language teachers and specialists in content production. Teachers should incorporate strategic planning in their regular teaching programs to encourage learners to balance their level of speech. Moreover, providing learners with the ability to plan the success of a task allows them to create a more fluent and nuanced language. Keywords: Accuracy, complexity, fluency, strategic planning, unpressured within-task planning. Cite as: Bakhtiary, M. R., Rezvani, E., & Namaziandost, E. (2021). Effects of strategic and unpressured within-task planning on Iranian intermediate EFL learners’ oral production. Journal of Nusantara Studies, 6(2), 97-115. http://dx.doi.org/10.24200/jonus.vol6iss2pp97-115

The Effect of Different Planning Conditions versus Explicit Grammar Instruction on Teenage EFL Learners’ Oral Production of a Picture Promoted Task and Grammar Gain

This study examined the effects of three task conditions on teenage EFL learners’ oral performance of a picture prompted task and their grammatical knowledge gain. To this end, 34 EFL learners were randomly assigned into three experimental groups, namely online planning, pre-planning, and explicit instruction, and one control group. Pictures were employed as prompts to implement the tasks in all groups. A Grammatical Judgment Test was used to compare students’ knowledge of simple present passive structure before and after the treatment. Learners’ oral task performance was measured in terms of Complexity, Accuracy, and Fluency (CAF). The findings demonstrated significant differences among groups as for CAF measures. However, no significant differences were spotted in GJT posttest scores. The paper calls for the incorporation of diverse prompt-based planning conditions in task-oriented teaching practices in order to target learners' higher levels of oral competence.

The Effects of Pre-Task Planning on Second Language Writing: A Systematic Review of Experimental Studies

There are both pedagogical and theoretical grounds for asking second language writers to plan before they start writing. The question then arises whether pre-task planning (PTP) improves written output. To address this question, this article reviewed 32 studies by comparing the effect of PTP either with no planning or with unpressured online planning (OLP). These studies also investigated the moderating effect of variables relating to the writer participants, the nature of the planning, and the writing tasks. The main findings are: (1) There is no clear evidence that PTP leads to better overall writing quality when this is measured using rating rubrics, (2) PTP generally results in more fluent writing, (3) its impact on syntactical and lexical complexity is inconsistent and negligible, (4) OLP does sometimes result in increased linguistic accuracy, and (5) there is insufficient evidence to reach clear conclusions about the role that moderating variables have on the impact of PTP, but the results suggest that collaborative (as opposed to individual planning) can lead to increased accuracy and that PTP tends to lead to more complex language when the writing task is a complex one. The article concludes with a set of principles to ensure better quality research and three general proposals for the kind of future research needed.