Dynamic Task Allocation in Partially Defined Environments Using A* with Bounded Costs

The sector of maritime robotics has seen a boom in operations in areas such as surveying and mapping, clean-up, inspections, search and rescue, law enforcement, and national defense. As this sector has continued to grow, there has been an increased need for single unmanned systems to be able to undertake more complex and greater numbers of tasks. As the maritime domain can be particularly difficult for autonomous vehicles to operate in due to the partially defined nature of the environment, it is crucial that a method exists which is capable of dynamically accomplishing tasks within this operational domain. By considering the task allocation problem as a graph search problem, Minion Task, is not only capable of finding and executing tasks, but is also capable of optimizing costs across a range of parameters and of considering constraints on the order that tasks may be completed in. Minion task consists of four key phases that allow it to accomplish dynamic tasking in partially defined environments. These phases are a search space updater that is capable of evaluating the regions the vehicle has effectively perceived, a task evaluator that is capable of ascertaining which tasks in the mission set need to be searched for and which can be executed, a task allocation process that utilizes a modified version of the A* with Bounded Costs (ABC) algorithm to select the best ordering of task for execution based on an optimization routing, and, finally, a task executor that handles transiting to and executing tasks orders received from the task allocator. To evaluate Minion Task’s performance, the modified ABC algorithm used by the task allocator was compared to a greedy and a random allocation scheme. Additionally, to show the full capabilities of the system, a partial simulation of the 2018 Maritime RobotX competition was utilized to evaluate the performance of the Minion Task algorithm. Comparing the modified ABC algorithm to the greedy and random allocation algorithms, the ABC method was found to always achieve a score that was as good, if not better than the scores of the greedy and random allocation schemes. At best, ABC could achieve an up to 2 times improvement in the score achieved compared to the other two methods when the ranges for the score and execution times for each tasks in the task set as well as the space where these tasks could exists was sufficiently large. Finally, using two scenarios, it was shown that Minion Task was capable of completing missions in a dynamic environment. The first scenario showed that Minion Task was capable of handling dynamic switching between searching for and executing tasks. The second scenario showed the algorithm was capable of handling constraints on the ordering of the tasks despite the environment and arrangement of tasks not changing otherwise. This paper succeeded in proving a method, Minion Task, that is capable of performing missions in dynamic maritime environments

Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

The Viability of Domain Constrained Coalition Formation for Robotic Collectives

Applications, such as military and disaster response, can benefit from robotic collectives' ability to perform multiple cooperative tasks (e.g., surveillance, damage assessments) efficiently across a large spatial area. Coalition formation algorithms can potentially facilitate collective robots' assignment to appropriate task teams; however, most coalition formation algorithms were designed for smaller multiple robot systems (i.e., 2-50 robots). Collectives' scale and domain-relevant constraints (i.e., distribution, near real-time, minimal communication) make coalition formation more challenging. This manuscript identifies the challenges inherent to designing coalition formation algorithms for very large collectives (e.g., 1000 robots). A survey of multiple robot coalition formation algorithms finds that most are unable to transfer directly to collectives, due to the identified system differences; however, auctions and hedonic games may be the most transferable. A simulation-based evaluation of three auction and hedonic game algorithms, applied to homogeneous and heterogeneous collectives, demonstrates that there are collective compositions for which no existing algorithm is viable; however, the experimental results and literature survey suggest paths forward.Comment: 46 pages, 9 figures, Swarm Intelligence (under review