Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment

The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.

Dynamic Task Allocation based on Individual Abilities - Experiences from Developing and Operating an Inclusive Assembly Line for Workers With and Without Disabilities

Digital assistive systems, enable workers with disabilities to perform complex industrial work. However, the previously presented systems considered only a single workplace and a single user. This paper presents an assembly line that enables a joint processing of complex tasks by multiple workers with and without disabilities. The aim was to investigate the use of interaction technologies such as in-situ projections and hand-tracking to enable the processing of complex assembly tasks by work teams with highly heterogeneous abilities. The developed assembly line assists users and coordinates the joint work by distributing single assembly steps to workers based on the individual workers' abilities. Besides presenting the concept and implementation of the assembly line, we report our findings after six months of operation. Our results indicate that using the assistive assembly line has positive impacts, such as increased satisfaction and independence of the workers combined with a higher productivity.

An Adaptive Epidemiology-Based Approach to Swarm Foraging with Dynamic Deadlines

Swarm robotics is an emerging field that can offer efficient solutions to real-world problems with minimal cost. Despite recent developments in the field, however, it is still not sufficiently mature, and challenges clearly remain. The dynamic deadline problem is neglected in the literature, and thus, time-sensitive foraging tasks are still an open research problem. This paper proposes a novel approach—ED_Foraging—that allows simple robots with limited sensing and communication abilities to perform complex foraging tasks that are dynamic and time constrained. A new mathematical model is developed in this paper to utilize epidemiological modeling and predict the dynamics of resource deadlines. Moreover, an improved dynamic task allocation (DTA) method is proposed to assign robots to the most critical region, where a deadline is represented by a state and time. The main goal is to reduce the number of expired resources and collect them as quickly as possible by giving priority to those that are more likely to expire if not collected. The deadlines are unknown and change dynamically. Thus, the robots continuously collect local information throughout their journeys and allocate themselves dynamically to the predicted hotspots. In the experiments, the proposed approach is adapted to four DTA methods and tested with different setups using simulated foot-bot robots. The flexibility, scalability, and robustness of this approach are measured in terms of the foraging and expiration rates. The empirical results support the hypothesis that epidemiological modeling can be utilized to handle foraging tasks that are constrained by dynamic deadlines. It is also confirmed that the proposed DTA method improves the results, which were found to be flexible, scalable, and robust to changes in the number of robots and the map size.