Deployment of teams of aerial robots could enable large-scale filming of dynamic groups of people (actors) in complex environments for applications in areas such as team sports and cinematography. Toward this end, methods for submodular maximization via sequential greedy planning can enable scalable optimization of camera views across teams of robots but face challenges with efficient coordination in cluttered environments. Obstacles can produce occlusions and increase chances of inter-robot collision which can violate requirements for near-optimality guarantees. To coordinate teams of aerial robots in filming groups of people in dense environments, a more general view-planning approach is required. We explore how collision and occlusion impact performance in filming applications through the development of a multi-robot multi-actor view planner with an occlusion-aware objective for filming groups of people and compare with a formation planner and a greedy planner that ignores inter-robot collisions. We evaluate our approach based on five test environments and complex multi-actor behaviors. Compared with a formation planner, our sequential planner generates 14% greater view reward for filming the actors in three scenarios and comparable performance to formation planning on two others. We also observe near identical view rewards for sequential planning both with and without inter-robot collision constraints which indicates that robots are able to avoid collisions without impairing performance in the perception task. Overall, we demonstrate effective coordination of teams of aerial robots in environments cluttered with obstacles that may cause collisions or occlusions and for filming groups that may split, merge, or spread apart. Our implementation and the data used to produce results for this paper are available via the companion website: https://greedyperspectives.github.io/

Volumetric objectives for exploration and perception tasks seek to capture a sense of value (or reward) for hypothetical observations at one or more camera views for robots operating in unknown environments. For example, a volumetric objective may reward robots proportionally to the expected volume of unknown space to be observed. We identify connections between existing information-theoretic and coverage objectives in terms of expected coverage, particularly that mutual information without noise is a special case of expected coverage. Likewise, we provide the first comparison, of which we are aware, between information-based approximations and coverage objectives for exploration, and we find, perhaps surprisingly, that coverage objectives can significantly outperform information-based objectives in practice. Additionally, the analysis for information and coverage objectives demonstrates that Randomized Sequential Partitions—a method for efficient distributed sensor planning—applies for both classes of objectives, and we provide simulation results in a variety of environments for as many as 32 robots.

In multi-robot multi-target tracking, robots coordinate to monitor groups of targets moving about an environment. We approach planning for such scenarios by formulating a receding-horizon, multi-robot sensing problem with a mutual information objective. Such problems are NP-Hard in general. Yet, our objective is submodular which enables certain greedy planners to guarantee constant-factor suboptimality. However, these greedy planners require robots to plan their actions in sequence, one robot at a time, so planning time is at least proportional to the number of robots. Solving these problems becomes intractable for large teams, even for distributed implementations. Our prior work proposed a distributed planner (RSP) which reduces this number of sequential steps to a constant, even for large numbers of robots, by allowing robots to plan in parallel while ignoring some of each others’ decisions. Although that analysis is not applicable to target tracking, we prove a similar guarantee, that RSP planning approaches performance guarantees for fully sequential planners, by employing a novel bound which takes advantage of the independence of target motions to quantify effective redundancy between robots’ observations and actions. Further, we present analysis that explicitly accounts for features of practical implementations including approximations to the objective and anytime planning. Simulation results—available via open source release—for target tracking with ranging sensors demonstrate that our planners consistently approach the performance of sequential planning (in terms of position uncertainty) given only 2–8 planning steps and for as many as 96 robots with a 24x reduction in the number of sequential steps in planning. Thus, this work makes planning for multi-robot target tracking tractable at much larger scales than before, for practical planners and general tracking problems.

This work considers estimation of mass parameters for multi-robot coordinated lifting in the context of coordinated aerial manipulation, and develops strategies for active parameter estimation for cooperative manipulation tasks through an information-theoretic framework. The active sensing problem is formulated based on application of increasing forces to the object and detection of small motions that occur when the center of pressure exits the convex hull formed by existing contacts. In order to enable identification of informative actions, we develop and employ a closed-form solution of Cauchy-Schwarz quadratic mutual information (Ics) for non-parametric filters. The evaluation considers iterative selection from a finite set of measurements and demonstrates that choosing measurements to maximize Icssignificantly improves the convergence rate of the parameter estimates compared to random and cyclic selection methods. This approach is extended to consider actuator constraints and feasible lifting configurations and achieves an 80% success rate in formation of feasible lifting configurations compared to a 53% baseline performance.

This paper presents a real-time, kinodynamic planning and information-theoretic exploration framework that enables high-resolution mapping of three-dimensional environments featuring complex concavities and disjoint objects. The proposed approach targets planetary exploration applications and seeks to achieve real-time operation on computationally constrained systems while ensuring energy-efficient information acquisition. Trajectories are selected by maximizing a measure of information gain per an expected execution cost (e.g., time or energy). The proposed trajectory generation formulation is based on state-lattice motion primitives and evaluation of the Cauchy-Schwarz quadratic mutual information (CSQMI) at each lattice state. An expanded search structure is proposed that extends the state-lattice to a finite horizon to enable expansive space coverage while remaining real-time viable. Additionally, compression techniques are employed to reduce the computational burden associated with the CSQMI calculation over expansive environments while preserving fidelity. The performance of the proposed methodology is evaluated through simulated exploration of a three-dimensional terrestrial pit environment by a quadrotor aerial robot which acts as a surrogate for a propulsive vehicle when operating on an airless body.

In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by refueling at depots. We also present a heuristic method to allow us to compute bounded suboptimal results in real time. The results produced by our algorithm will allow a team of robots to efficiently cover a given set of targets or tasks persistently over long periods of time, even when the cost to transition between tasks is dynamic.