Balanced task allocation by partitioning the multiple traveling salesperson problem

Task assignment and routing are tightly coupled problems for teams of mobile agents. To fairly balance the workload, each agent should be assigned a set of tasks which take a similar amount of time to complete. The completion time depends on the time needed to travel between tasks which depends on the order of tasks. We formulate the task assignment problem as the minimum Hamiltonian partition problem (MHPP) form agents, which is equivalent to the minmax multiple traveling salesperson problem (m-TSP). While the MHPP’s cost function depends on the order of tasks, its solutions are similar to solutions of the average Hamiltonian partition problem (AHPP) whose cost function is order-invariant. We prove that the AHPP is NP-hard and present an effective heuristic, AHP, for solving it. AHP improves a partitions of a graph using a series of transfer and swap operations which are guaranteed to improve the solution’s quality. The solution generated by AHP is used as an initial partition for an algorithm, AHP-mTSP, which solves the combined task assignment and routing problems to near optimality. For n tasks and m agents, each iteration of AHP is O(n2) and AHP-mTSP has an average run-time that scales with n2.11m0.33. Compared to state-of-the-art approaches, our approach found approximately 10% better solutions for large problems in a similar run-time

Re-establishing communication in teams of mobile robots

As communication is important for cooperation, teams of mobile robots need a way to re-establish a wireless connection if they get separated. We develop a method for mobile robots to maintain a belief of each other's positions using locally available information. They can use their belief to plan paths with high probabilities of reconnection. This approach also works for subteams cooperatively searching for a robot or group of robots that they would like to reconnect with. The problem is formulated as a constrained optimization problem which is solved using a branch-and-bound approach. We present simulation results showing the effectiveness of this strategy at reconnecting teams of up to five robots and compare the results to two other strategies

Mittelfristige Ergebnisse der Vastus-medialis-obliquus-Plastik bei lateraler Patellaluxation

Zusammenfassung: In Langzeitergebnissen nach traditionellen Operationsverfahren distalen Realignements für Patellaluxationen wie z.B. der Tuberositasosteotomie wird eine hohe Rate an Femoropatellararthrosen gefunden, sodass ein operatives Vorgehen noch heute kontrovers diskutiert wird. In der Literatur scheinen die Verfahren mit dynamischem proximalem Realignement eine geringere Arthroserate, aber bisweilen höhere Reluxationsrate aufzuweisen. Unlängst wurde der M.vastus medialis obliquus (VMO) in anatomischen und biomechanischen Studien als eine der entscheidenden proximalen stabilisierenden Strukturen bei lateralen Patellaluxationen identifiziert. Zwischen 1994 und 2003 wurden 28Patienten (Durchschnittsalter 21,5Jahre) mit einer VMO-Plastik bei lateraler Patellaluxation operativ versorgt. Die Technik wurde für die meisten Ätiologien einer femoropatellären Instabilität angewandt. Bei dieser proximalen Weichteilkorrektur wird die sehnige Einstrahlung des VMO von der Patella abgelöst. Anschließend wird die Sehne 10-15mm distalisierend an der Patella über MITEK-Anker reinseriert. Postoperativ ist eine Vollbelastung in Streckung möglich. Ein aktives Auftrainieren des Vastus medialis beginnt 6Wochen nach der Operation. 27Patienten wurden klinisch und radiologisch im Jahre 2004 nachkontrolliert, durchschnittlich 5Jahre nach der Operation. 83% gaben ein exzellentes oder gutes Resultat an, 10% waren zufrieden und 7% unzufrieden. Der durchschnittliche Lysholm-Knie-Score betrug 83,1Punkte. Zwei Patienten erlitten eine Reluxation (7%). Die postoperativen Röntgenbilder zeigten eine signifikante Verbesserung des Kongruenzwinkels auch noch nach vielen Jahren. In 89% der Fälle wurde keine oder eine nur geringe Femoropatellararthrose beobachtet. Die präsentierten Fünfjahresergebnisse sind bezüglich Patientenzufriedenheit mit anderen Verfahren proximalen und distalen Realignements vergleichbar. Die Reluxationsrate ist unterdurchschnittlich. Die bisherige niedrige Rate an Femoropatellararthrose nach durchschnittlich 5Jahren erscheint im Vergleich mit den Arthroseraten des rigiden, distalen Realignements hinsichtlich zukünftiger Langzeitergebnisse vielversprechend und wird auf den minimalen Eingriff in das physiologische Gelenkspiel und auf die Wiederherstellung der verletzten Anatomie zurückgeführt. Die Idee der proximalen dynamischen Stabilisierung und das Angreifen am Ursprung der Pathologie wird in den Erkenntnissen aktueller anatomischer und biomechanischer Untersuchungen bestätigt, was diese relativ guten Ergebnisse erklären mag. Über- und Unterkorrekturen der Weichteile können zurzeit kompensiert werden. Die oben genannten traditionellen und rigideren Operationsmethoden erlauben eine solche Kompensation nicht in diesem Ausmaß und können so zu präarthrotischer Überbelastung des medialen Femoropatellar- und Femorotibialgelenks führe

OpenSwarm: an event-driven embedded operating system for miniature robots

This paper presents OpenSwarm, a lightweight easy-to-use open-source operating system. To our knowledge, it is the first operating system designed for and deployed on miniature robots. OpenSwarm operates directly on a robot’s microcontroller. It has a memory footprint of 1 kB RAM and 12 kB ROM. OpenSwarm enables a robot to execute multiple processes simultaneously. It provides a hybrid kernel that natively supports preemptive and cooperative scheduling, making it suitable for both computationally intensive and swiftly responsive robotics tasks. OpenSwarm provides hardware abstractions to rapidly develop and test platformindependent code. We show how OpenSwarm can be used to solve a canonical problem in swarm robotics—clustering a collection of dispersed objects. We report experiments, conducted with five e-puck mobile robots, that show that an OpenSwarm implementation performs as good as a hardware-near implementation. The primary goal of OpenSwarm is to make robots with severely constrained hardware more accessible, which may help such systems to be deployed in real-world applications

Occlusion-based cooperative transport with a swarm of miniature mobile robots

This paper proposes a strategy for transporting a large object to a goal using a large number of mobile robots that are significantly smaller than the object. The robots only push the object at positions where the direct line of sight to the goal is occluded by the object. This strategy is fully decentralized and requires neither explicit communication nor specific manipulation mechanisms. We prove that it can transport any convex object in a planar environment. We implement this strategy on the e-puck robotic platform and present systematic experiments with a group of 20 e-pucks transporting three objects of different shapes. The objects were successfully transported to the goal in 43 out of 45 trials. When using a mobile goal, teleoperated by a human, the object could be navigated through an environment with obstacles. We also tested the strategy in a 3-D environment using physics-based computer simulation. Due to its simplicity, the transport strategy is particularly suited for implementation on microscale robotic systems

Turn-minimizing multirobot coverage

Document Sections I. Introduction II. Partitioning the Environment III. Combining Ranks Into Paths IV. Results V. Conclusions Authors Figures References Keywords Metrics Abstract: Multirobot coverage is the problem of planning paths for several identical robots such that the combined regions traced out by the robots completely cover their environment. We consider the problem of multirobot coverage with the objective of minimizing the mission time, which depends on the number of turns taken by the robots. To solve this problem, we first partition the environment into ranks which are long thin rectangles the width of the robot's coverage tool. Our novel partitioning heuristic produces a set of ranks which minimizes the number of turns. Next, we solve a variant of the multiple travelling salesperson problem (m-TSP) on the set of ranks to minimize the robots' mission time. The resulting coverage plan is guaranteed to cover the entire environment. We present coverage plans for a robotic vacuum using real maps of 25 indoor environments and compare the solutions to paths planned without the objective of minimizing turns. Turn minimization reduced the number of turns by 6.7% and coverage time by 3.8% on average for teams of 1-5 robots