During bent bone grinding, the robot often needs to adjust its grinding depth and angle in real time to ensure surgical quality. Optical- or vision-based navigation methods are no longer applicable due to the interference caused by splashing cooling water in the operating field. Inspired by orthopedic surgeons, in this article, we present a tactile perception-based method to solve this problem. The effect of grinding parameters on the cutter's current and vibration is analyzed, and the current and vibration signals are further processed to predict depth and angle. Depth and angle estimation models were calibrated on homogeneous artificial bone. Grinding control experiments were carried out on femur models, and the statistical mean and standard deviation results of the measured depth show that the accuracy of depth control can be further improved by adding angle estimation and proper adjustment when depth is indirectly estimated based on sensing signals. The proposed approach relies solely on the estimation of the relative position and posture between the tool and the bone surface and is expected to improve the safety of automatic grinding with orthopedic robots.

We present a MAC-layer, soft real-time packet scheduling algorithm called UPA. UPA considers a message model where message packets have end-to-end timeliness requirements that are specified using Jensen's time-utility functions (TUFs). The algorithm seeks to maximize system-wide, aggregate packet utility. Since this scheduling problem is NP-hard, UPA heuristically computes schedules with a quadratic worst-case cost, faster than the previously best CMA algorithm. Our simulation studies show that UPA performs the same as or significantly better than CMA for a broad set of TUFs. Furthermore, we implement UPA and prototype a TUF-driven switched Ethernet system. The performance measurements of UPA from the implementation reveal its strong effectiveness. Finally, we derive timeliness feasibility conditions of TUF-driven switched Ethernet systems that use the UPA algorithm.

Asynchronous real-time distributed systems are inherently non-deterministic. To deal with such non-determinism's, we have developed a family of proactive resource management algorithms that support benefit-function based, end-to-end QoS management. This paper describes a middleware implementation of these algorithms, called Choir. The Choir middleware allows the user express the task end-to-end timeliness requirements using Jensen's benefit functions. Furthermore, the middleware system can transparently replicate, and possibly migrate the computational subtasks to conquer uncertainties such as workload fluctuations, changes of system resources, so that the system aggregate benefit is maximized. Initial experimental results suggest the effectiveness the Choir middleware.

In this paper, we present a systems engineering methodology for constructing certifiable realtime distributed systems. In the proposed approach, an architectural and algorithmic solution to an application problem is designed by considering the "weakest" models including the weakest asynchronous computational model and multimodal arrival model. Furthermore, timeliness properties are described using Jensen's benefit accrual predicates. Once a system solution is designed, timeliness properties are established by constructing necessary feasibility conditions that are expressed as non-valued predicates. The predicates are quantified and verified to produce the specification of a certified solution. We illustrate the approach by considering a packet transmission problem that desire soft timeliness. We present a certifiable solution to this problem that consists of switched Ethernet, a soft real-time packet scheduling algorithm (that was previously developed), and feasibility conditions.

In this paper, we present a MAC-layer packet scheduling algorithm, called BPA, for real-time switched Ethernet networks. BPA considers a message model where trans-node application-level messages have end-to-end timeliness requirements that are specified using Jensen's benefit functions. The objective of BPA is to maximize the aggregate message-level benefit. The algorithm reasons that this objective can be achieved by maximizing aggregate packet-level benefit, where packets of messages are allowed to inherit benefit functions of their parent messages. BPA thus solves a non-preemptive packet scheduling problem. Since this problem is NP-hard, BPA heuristically computes packet schedules to maximize aggregate benefit, incurring a worst-case computational complexity of O(n/sup 2/). This is better than the O(n/sup 3/) complexity of the previously known best algorithm (called CMA) for the same problem. Further, our experimental studies show that BPA performs as good as CMA for a broad set of benefit functions, and significantly outperforms CMA for some benefit functions. Furthermore, we observe that BPA yields lower missed-deadline ratio than CMA when message arrival density increases.