Comparison of non-coplanar optimization of static beams and arc trajectories for intensity-modulated treatments of meningioma cases

Two methods for non-coplanar beam direction optimization, one for static beams and another for arc trajectories, were proposed for intracranial tumours. The results of the beam angle optimizations were compared with the beam directions used in the clinical plans. Ten meningioma cases already treated were selected for this retrospective planning study. Algorithms for non-coplanar beam angle optimization (BAO) and arc trajectory optimization (ATO) were used to generate the corresponding plans. A plan quality score, calculated by a graphical method for plan assessment and comparison, was used to guide the beam angle optimization process. For each patient, the clinical plans (CLIN), created with the static beam orientations used for treatment, and coplanar VMAT approximated plans (VMAT) were also generated. To make fair plan comparisons, all plan optimizations were performed in an automated multicriteria calculation engine and the dosimetric plan quality was assessed. BAO and ATO plans presented, on average, moderate global plan score improvements over VMAT and CLIN plans. Nevertheless, while BAO and CLIN plans assured a more efficient OARs sparing, the ATO and VMAT plans presented a higher coverage and conformity of the PTV. Globally, all plans presented high-quality dose distributions. No statistically significant quality differences were found, on average, between BAO, ATO and CLIN plans. However, automated plan solution optimizations (BAO or ATO) may improve plan generation efficiency and standardization. In some individual patients, plan quality improvements were achieved with ATO plans, demonstrating the possible benefits of this automated optimized delivery technique.

Tool workspace consideration for assembly plan generation

Purpose During the design of a new product, the generation of assembly sequences plans (ASPs) has become one of the most important problems taken into account by researchers. In fact, a good mounting order allows the time decrease of the assembly process which leads to the reduction of production costs. In this context, researchers developed several methods to generate and optimize ASP based on various criteria. Although this paper aims to improve the quality of ASP it is necessary to increase the number of criteria which must be taken into account when generating ASPs. Design/methodology/approach In this paper, an ASP generation approach, which is based on three main algorithms, is proposed. The first one generates a set of assembly sequences based on stability criteria. The obtained results are treated by the second algorithm which is based on assembly tools (ATs) workspace criterion. An illustrative example is used to explain the different steps of this proposed approach. Moreover, a comparative study is done to highlight its advantages. Findings The proposed algorithm verifies, for each assembly sequence, the minimal required workspace of used AT and eliminates the ASPs non-respecting this criterion. Finally, the remaining assembly sequences are treated by the third algorithm to reduce the AT change during the mounting operation. Originality/value The proposed approach introduces the concept of AT workspace to simulate and select ASPs that respect this criterion. The dynamic interference process allows the eventual collision detection between tool and component and avoids it. The proposed approach reduces the AT change during the mounting operations.

A Comprehensive Approach to On-board Autonomy Verification and Validation

Deep space missions are characterized by severely constrained communication links. To meet the needs of future missions and increase their scientific return, future space systems will require an increased level of autonomy on-board. In this work, we propose a comprehensive approach to on-board autonomy. We rely on model-based reasoning, and we consider many important (on-line and off-line) reasoning capabilities such as plan generation, validation, execution and monitoring, runtime diagnosis, and fault detection, identification, and recovery. The controlled platform is represented symbolically, and the reasoning capabilities are seen as symbolic manipulation of such formal model. We have developed a prototype of our framework, and we have integrated it within an on-board Autonomous Reasoning Engine. Finally, we have evaluated our approach on three case-studies inspired by real-world projects and characterized it in terms of reliability, availability, and performance.