Planning to fail: using reliability to improve multirobot task allocation (original) (raw)
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Planning to fail — Reliability needs to be considered a priori in multirobot task allocation
2009 IEEE International Conference on Systems, Man and Cybernetics, 2009
The reliability of individual team members has a substantial and complex influence on the success of multirobot missions. When one robot fails, other robots must be retasked to complete the tasks that were assigned to the failed robot. This in turn increases the likelihood of these other robots failing, since they have more work to do. Existing multirobot task allocation systems consider robot failures only after the fact-by replanning after a failure occurs. We hypothesize that it should be important to consider robot reliabilities when generating an initial plan. In this paper we test this hypothesis in the context of the multirobot exploration problem. We take a simple exhaustive planner and compare the plan it chooses against the optimal plan that takes into account robot failures and the backup plans that occur after failure. Our results show that for this problem domain, making an initial plan without regards to individual robot reliabilities results in choosing a suboptimal plan most of the time, and that the difference in mission performance between the chosen plan and the optimal plan is usually substantial. In brief, in order to successfully plan we must 'plan to fail'.
Lecture Notes in Computer Science, 2013
This paper presents a quantifiable method by which the behaviors of robots, as determined by their performance in a cyber-physical context, can be captured and generalized so that accurate predictions of sequentially coordinated multirobot behaviors can be made. The analysis technique abstracts sequentially coordinated multirobot missions as a frequentist inference problem. Rather than attempt to identify and put into a causal relation all the hidden and unknown cyber-physical influences that can have an impact on mission performance, we model the problem as that of predicting multirobot performance as a conditional probability. This allows us to initially limit the testing and evaluation of robot performance to evaluations of atomistic behaviors, and to experiment mathematically with the combinations of predictive features and elementary performance metrics to derive accurate predictions of higherlevel coordinated performance. Statistical tests on the goodness of the results are reported, as well.
An optimized multirobot task allocation
Emerging Trends in Engineering …, 2008
Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. One of the most important aspects in the design of MRS is the allocation of tasks among the robots in a productive and efficient manner. Optimal solutions to multirobot task allocation (MRTA) can be found through an exhaustive search. Since there are m n × ways in which m tasks can be assigned to n robots, an exhaustive search is often not possible. Task allocation methodologies must ensure that not only the global mission is achieved, but also the tasks are well distributed among the robots. This paper presents task allocation methodologies for MRS by considering their capability in terms of time and space. A two-phase solution methodology is used to solve the MRTA problem wherein the task capacity of the robots is determined during the first phase and the task allocation optimization is done during the second phase using linear programming (LP).
Planning to fail: Mission design for modular repairable robot teams
This paper presents a method using stochastic simulation to evaluate the reliability of robot teams consisting of modular robots. For an example planetary exploration mission we use this method to compare the performance of a repairable robot team with spare modules versus nonrepairable robot teams. Our results show that for this mission a repairable robot team can provide a higher probability of mission completion than a nonrepairable team, even when the nonrepairable robots are built using components with an order of magnitude greater reliability than the repairable robots.
Task Allocation Strategies in Multi-Robot Environment
2009
Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. A robot team can accomplish a given task more quickly than a single agent by executing them concurrently. A team can also make ...
Optimal Task Allocation in a Multirobot Environment Using Capability Indices
2008
systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. Multirobot coordination, however, is a complex problem. One of the most important aspects in the design of multi- robot systems is the allocation of tasks among the robots in a productive and efficient manner. An empirical study is described in the present paper for task allocation strategies. In general, optimal solutions can be found through an exhaustive search, but because there are m n× ways in which m tasks can be assigned to n robots, an exhaustive search is often not possible. Task allocation methodologies must ensure that not only the global mission is achieved, but also the tasks are well distributed among the robots. An effective task allocation approach considers the available resources, the capabilities of the deployable robots, and then it appropriately allocates the tasks the candidate robots. This paper presents such task allocation methodologies for multi-r...
Resilient Task Allocation in Heterogeneous Multi-Robot Systems
IEEE Robotics and Automation Letters
For a multi-robot system equipped with heterogeneous capabilities, this paper presents a mechanism to allocate robots to tasks in a resilient manner when anomalous environmental conditions such as weather events or adversarial attacks affect the performance of robots within the tasks. Our primary objective is to ensure that each task is assigned the requisite level of resources, measured as the aggregated capabilities of the robots allocated to the task. By keeping track of task performance deviations under external perturbations, our framework quantifies the extent to which robot capabilities (e.g., visual sensing or aerial mobility) are affected by environmental conditions. This enables an optimization-based framework to flexibly reallocate robots to tasks based on the most degraded capabilities within each task. In the face of resource limitations and adverse environmental conditions, our algorithm minimally relaxes the resource constraints corresponding to some tasks, thus exhibiting a graceful degradation of performance. Simulated experiments in a multi-robot coverage and target tracking scenario demonstrate the efficacy of the proposed approach.
Mission Reliability Estimation for Repairable Robot Teams
International Journal of Advanced Robotic Systems, 2006
NASA has expressed interest in using modular self-repairable robotic teams for the exploration and colonization of Mars. One of the reasons often given for using repairable robots is increased reliability. Analytical tools are needed for estimating the reliability of robotic missions in order to determine if this reasoning is correct, and for what types of missions. In this paper we present the first method for analytically predicting the probability of mission completion for teams of repairable mobile robots. We then apply this method to compare the reliability of repairable and nonrepairable robot teams for an example mission scenario. Our results show that for this simple mission, with reasonable assumptions regarding costs, teams of repairable robots with spare components are superior to teams with spare nonrepairable robots.
Multi-robot Task Allocation for Search and Rescue Missions
Journal of Physics: Conference Series, 2014
Many researchers from academia and industry are attracted to investigate how to design and develop robust versatile multi-robot systems by solving a number of challenging and complex problems such as task allocation, group formation, self-organization and much more. In this study, the problem of multi-robot task allocation (MRTA) is tackled. MRTA is the problem of optimally allocating a set of tasks to a group of robots to optimize the overall system performance while being subjected to a set of constraints. A generic market-based approach is proposed in this paper to solve this problem. The efficacy of the proposed approach is quantitatively evaluated through simulation and real experimentation using heterogeneous Khepera-III mobile robots. The results from both simulation and experimentation indicate the high performance of the proposed algorithms and their applicability in search and rescue missions.