Auction-based Task Allocation for Safe and Energy Efficient UAS Parcel Transportation

In this paper, two greedy auction-based algorithms are proposed for the allocation of heterogeneous tasks to a heterogeneous fleet of UAVs. The tasks set is composed of parcel delivery tasks and charge tasks, the latter to guarantee service persistency. An optimization problem is solved by each agent to determine its bid for each task. When considering delivery tasks, the bidder aims at minimizing the energy consumption, while the minimization of the flight time is adopted for charge tasks bids. The algorithms include a path planner that computes the minimum risk path for each task-UAV bid exploiting a 2D risk map of the operational area, defined in an urban environment. Each solution approach is implemented by means of two auction strategies: single-item and multiple-item. Considerations about complexity and efficiency of the algorithms are drawn from Monte Carlo simulations

4CNet: A Confidence-Aware, Contrastive, Conditional, Consistency Model for Robot Map Prediction in Multi-Robot Environments

Mobile robots in unknown cluttered environments with irregularly shaped obstacles often face sensing, energy, and communication challenges which directly affect their ability to explore these environments. In this paper, we introduce a novel deep learning method, Confidence-Aware Contrastive Conditional Consistency Model (4CNet), for mobile robot map prediction during resource-limited exploration in multi-robot environments. 4CNet uniquely incorporates: 1) a conditional consistency model for map prediction in irregularly shaped unknown regions, 2) a contrastive map-trajectory pretraining framework for a trajectory encoder that extracts spatial information from the trajectories of nearby robots during map prediction, and 3) a confidence network to measure the uncertainty of map prediction for effective exploration under resource constraints. We incorporate 4CNet within our proposed robot exploration with map prediction architecture, 4CNet-E. We then conduct extensive comparison studies with 4CNet-E and state-of-the-art heuristic and learning methods to investigate both map prediction and exploration performance in environments consisting of uneven terrain and irregularly shaped obstacles. Results showed that 4CNet-E obtained statistically significant higher prediction accuracy and area coverage with varying environment sizes, number of robots, energy budgets, and communication limitations. Real-world mobile robot experiments were performed and validated the feasibility and generalizability of 4CNet-E for mobile robot map prediction and exploration.Comment: 14 pages, 10 figure

A Method For Improving Decentralized Task Allocation For Multiagent Systems in Low-Communication Environments.

Communication is an important aspect of task allocation, but it has a cost and low communication restricts the information exchange needed for task allocation. As a result, a lot of decentralized task allocation algorithms perform worse as communication worsens. The contribution of this thesis is a method to improve the performance of a task allocation algorithm in low-communication environments and reduce the cost of communication by restricting communication. This method, applied to the Consensus Based Auction Algorithm (CBAA), determines when an agent should communicate and estimates the information that will be received from other agents. This method is compared to other decentralized task allocation algorithms at different levels of communication in a ship protection scenario. Results show that this method when applied to CBAA performs comparably to CBAA while reducing communication

Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of three interacting components: (i) a centralized oracle planner that uses ground truth information to generate expert demonstrations, (ii) a CNN that learns to assign rewards to action sequences by imitating a team of expert robots, and (iii) MCTS for online, decentralized planning that uses the trained CNN as the reward function for the action sequences produced by the simulation phase of MCTS. The CNN also leverages structural information to adapt the planner to different environments. Our experiments in simulation, which include a real-world environment, show that performance gracefully degrades with increasing communication loss for a team of four robots. The proposed method enables a random rollout policy to be used in the simulation phase of MCTS, which saves a significant amount of computation time compared to other heuristics. Our tests further show that the proposed method is scalable to a team of sixteen robots

Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN

International audienceFour new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in wireless sensor and robot networks based on the greedy-face-greedy (GFG) routing extended with auctions are proposed in this paper. In this paper, we assume that all robots are mobile, and after the event is found (reported by sensors), the goal is to allocate the task to the most suitable robot to act upon the event, using either distance or the robots' remaining energy as metrics. The proposed algorithms consist of two phases. The first phase of algorithms is based on face routing, and we introduced the parameter called search radius (SR) at the end of this first phase. Routing is considered successful if the found robot is inside SR. After that, the second phase, based on auctions, is initiated by the robot found in SR trying to find a more suitable one. In the simulations, network lifetime and communication costs are measured and used for comparison. We compare our algorithms with similar algorithms from the literature (k-SAAP and BFS) used for the task assignment. RFTA2 and RFTA2GE feature up to a seven-times-longer network lifetime with significant communication overhead reduction compared to k-SAAP and BFS. Among our algorithms, RFTA2GE features the best robot energy utilization

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