A formulation for collision identification and distance calculation in motion planning using neural networks

The collision identification and object-to-object distance calculation play an important role in the motion planning for robots and manufacturing facilities. A formulation for collision identification and distance calculation in motion planning, using neural networks, is presented. The method calculates the distances between the vertices of an object and the given polyhedral obstacles using the modified Hamming net. This formulation is derived from the homogeneous geometric transformations. The method can be used to identify collision between the vertices of a moving object and the obstacles, to calculate the distance and interference between the moving object and the obstracle, and to find the optimal direction for collision removal. The parallel computation formulation is simple in form, and can be extended to line-to-object and object-to-object collision identification and distance calculation. The method can considerably decrease required computation time, and has the potential for being applied to on-line trajectory planning.     

多移动微小型机器人编队控制与协作避碰研究

针对多移动微小型机器人系统的协作避碰和队形保持,给出了一种分布式的编队控制方法。结合移动微小型机器人的运动控制模型,提出了一种路径规划方法,使其在运动中实时避免碰撞。在此基础上利用李雅普诺夫(Lyapunov)法设计了一种编队控制器。在有界误差范围内,该控制器能够保证多机器人的轨迹跟踪和协作避碰。通过将编队控制转化为跟踪整个队形质心的轨迹,降低了控制的复杂度,从而可以较好地应用到计算资源有限的多移动微小型机器人中。通过仿真、分析和对比,对以上控制方法的稳定性和可行性进行了验证,并进行了实际的编队和避碰控制实验。实验结果表明该方法可有效地应用于多移动微小型机器人的协作避碰和编队控制。     

双机器人时间优化的避碰轨迹规划研究

介绍了一种双机器人时间优化的避碰轨迹规划方法。根据单机器人的已知路径,在具有最大加减速度条件下规划其时间优化轨迹。通过几何建模,利用Euclidean函数表示双机器人间的最短距离,实现了系统的碰撞检测。采用在初始位置延迟最小时间的方法,获得了双机器人时问优化的无碰撞轨迹。最后建立了基于Motoman-UP6的双机器人仿真模型,验证了理论分析的正确性。     

Approximate life cycle assessment of product concepts using multiple regression analysis and artificial neural networks

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the design of environmentally conscious products in conceptual design phase.