Multi-robot Task Allocation

There is difficulty associated with transferring the DAI approaches into multi- robot systems, as the multi-robot systems are subject to real world constraints [41].

There are many approaches to multi-robot task allocation, including ALLIANCE [33], ASyMTRe-D [39], MURDOCH [20], Dynamic Role Assignment [10], Broadcast of Local Eligibility [43], and First-price auctions [46]. These systems all make an as- sumption that each task can be completed by a single robot, or the number of robots for a task required is known a priori.

ALLIANCE [33] uses impatience and acquiescence to define a robot allocation to a task in a decentralized manner. The tasks are assigned to the entire collective, and one robot is selected to execute it. If it fails to execute the task in a certain time, another robot becomes impatient and attempts to take over. If the original robot is sufficiently acquiescent, it allows the new robot to attempt the task. This provides the ability for dynamic allocation of robots to a task, but each task is inherently a single robot task. In essence, ALLIANCE allows robots to decide among themselves which robot attempts a task. No provisions are made for multi-robot tasks (those tasks which require multiple robots to execute), though it is a straightforward variant to limit the acquiescence of an executing robot yet allow a new robot to join. The biggest limitation of ALLIANCE is that there is no self-regulation which prevents allocation of the entire collective to a task. The collective can then be stuck trying to execute an impossible task. Preallocating multiple robots to an inherently multirobot task, as in coalition formation, constrains the extent to which the robots dedicate resources to an impossible task.

ASyMTRe-D [39] uses a distributed version of the “schema” abstraction [3] to generate coalitions. The approach applies a distributed negotiation process based on the Contract Net Protocol [37]. ASyMTRe-D creates motor schemas and defines them as resources available to other robots. This allows the robots to solve for the resources required for a task and gather those resources. This naturally creates a coalition for the task’s execution. Provided each robot in the collective understands the schemas available and the contributions of those schemas for task execution, the robots can build successful and robust coalitions. However, ASyMTRe-D is built assuming that no new robot types join the collective, and those in the collective understand a priori how to represent and use every schema required for a task. ASyMTRe-D does allow robots to join or leave the collective frequently, but any robots joining must be well understood (i.e., the schemas are available to every other robot and every other robot understands the information needed to activate the schemas) ahead of time. This

prior knowledge about other robots precludes ASyMTRe-D’s applicability to more dynamic collectives.

MURDOCH [20] allocates tasks to robots with a first-price auction method [31]. It announces a task with defined metrics, then the robots issue bids. The task issuer (auctioneer) awards the task to the highest bid then monitors task completion. This requires that the tasks are inherently single robot tasks, assigned to a collective of multiple robots. Under this method, only one robot is assigned to a task and no provisions are made to allow multiple assignment. This approach is limited to tasks that require a single robot unless a multirobot task is explicitly decomposed into distinct single-robot roles. This requires extensive knowledge on behalf of the auctioneer to decompose the task to single robot roles. Without significant user intervention, this requires a static collective.

Dynamic Role Assignment [10] provides a method for task execution where the robots can dynamically change roles. The roles are defined ahead of time, but the robots assigned to each role may change dynamically. This enables a multi-robot collective to readjust the allocation of tasks during execution, making for a more efficient execution. One drawback of this system is that the roles must be defined by a user. Thus, each task has a representation that describes the specific steps and roles to fill for successful execution. This approach is not well suited to an automated role generation, since the robots would require full knowledge of the other robots in the collective to determine the plan and the roles. Furthermore, the task is already allocated when Dynamic Role Assignment takes over, so coalition formation and task allocation discussions are impertinent.

Broadcast of Local Eligibility [43] uses the behavior-based architecture paradigm [9] in a multi-robot setting to allocate tasks to the collective. It allows identical robots with identical behaviors (called peer behaviors) the ability to cross-inhibit each other. The robot assigned to the task is the most eligible robot for that task. A notable drawback of this representation includes the underlying constraint that the robots are

identical. One way to reduce this constraint is to allow heterogeneous hardware with identical behaviors, but the issue then is in matching the functionality, parameters, and feedback such that the behavior output is identical. Another issue is that the tasks are inherently single robot tasks, assigned to a collective of multiple robots. Each task assigned to the collective is executed by a single robot, and the only contributors to comparative eligibility (quality for task execution) are physical location in the environment and current tasking.

The focus of auction methods is to address efficiency and optimality [17, 20, 21] in multirobot task allocation. They operate by allowing robots to bid on a task, and, much like an auction, awarding the highest bidder with the task. They operate well in the face of uncertainty, but make a number of assumptions which limit their applicability. First, they assume that the robots are generally not selfish. Since there are times when a robot must be selfish (for example, when a robot’s battery charge level is critically low), this assumption does not hold for all cases. Secondly, the existence of an auctioneer centralizes the approaches. The centralization can be mitigated by allowing any robot to become an auctioneer, but the centralization is present for each task nevertheless. The third, and most critical, assumption that auction methods make is that the number of robots required for a task are known a

priori. In circumstances where the procedural aspects of tasks are not well defined,

this assumption may prevent successful execution. For example, a task requiring box pushing (such as in [42]) may assume one agent is required. When that agent is incapable of pushing the box alone, the task must be reassigned appropriately. Therefore, though the utility generation and evaluation aspects of the procedure to be presented in Chapter III are similar to aspects of auction methods, the procedure followed uses agent preferences instead of a predefined coalition size for determining the size of the optimal coalition.