Search And Tracking (SAT) is the problem of searching for a mobile target and tracking it after it is found. As this problem has important applications in search-and-rescue and surveillance operations, recently there has been increasing interest in equipping unmanned aerial vehicles (UAVs) with autonomous SAT capabilities. State-of-the-art approaches to SAT rely on estimating the probability density function of the target's state and solving the search control problem in a greedy fashion over a short planning horizon (typically, a one-step lookahead). These techniques suffer high computational cost, making them unsuitable for complex problems. In this paper, we propose a novel approach to SAT, which allows us to handle big geographical areas, complex target motion models and long-term operations. Our solution is to track the target reactively while it is in view and to plan a recovery strategy that relocates the target every time it is lost, using a high-performing automated planning tool. The planning problem consists of deciding where to search and which search patterns to use in order to maximise the likelihood of recovering the target. We show experimental results demonstrating the potential of our approach.
In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal, as they may take too long to cover a large area. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles are non-holonomic, but can be modeled using Dubins vehicle kinematics. This paper focuses on environmental monitoring of aquatic environments using Autonomous Surface Vehicles (ASVs). In particular, we propose a novel approach for solving the problem of complete coverage of a known environment by a multi-robot team consisting of Dubins vehicles. It is worth noting that both multi-robot coverage and Dubins vehicle coverage are NP-complete problems. As such, we present two heuristics methods based on a variant of the traveling salesman problem -- k-TSP -- formulation and clustering algorithms that efficiently solve the problem. The proposed methods are tested both in simulations to assess their scalability and with a team of ASVs operating on a lake to ensure their applicability in real world.
Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircraft to maximize forest fire coverage is a complex problem. The state space is high dimensional, the fire propagates stochastically, the sensor information is imperfect, and the aircraft must coordinate with each other to accomplish their mission. This work presents two deep reinforcement learning approaches for training decentralized controllers that accommodate the high dimensionality and uncertainty inherent in the problem. The first approach controls the aircraft using immediate observations of the individual aircraft. The second approach allows aircraft to collaborate on a map of the wildfire's state and maintain a time history of locations visited, which are used as inputs to the controller. Simulation results show that both approaches allow the aircraft to accurately track wildfire expansions and outperform an online receding horizon controller. Additional simulations demonstrate that the approach scales with different numbers of aircraft and generalizes to different wildfire shapes.
A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other agents in dynamic environments can lead to risky or over-conservative plans. In this work, we model the motion planning problem as a partially observable Markov decision process (POMDP) and propose an online system that combines an intent recognition algorithm and a POMDP solver to generate risk-bounded plans for the ego vehicle navigating with a number of dynamic agent vehicles. The intent recognition algorithm predicts the probabilistic hybrid motion states of each agent vehicle over a finite horizon using Bayesian filtering and a library of pre-learned maneuver motion models. We update the POMDP model with the intent recognition results in real time and solve it using a heuristic search algorithm which produces policies with upper-bound guarantees on the probability of near colliding with other dynamic agents. We demonstrate that our system is able to generate better motion plans in terms of efficiency and safety in a number of challenging environments including unprotected intersection left turns and lane changes as compared to the baseline methods.
There has been much interest recently in problems that com-bine high-level task planning with low-level motion planning.In this paper, we present a problem of this kind that arises inmulti-vehicle mission planning. It tightly integrates task al-location and scheduling, who will do what when, with pathplanning, how each task will actually be performed. It ex-tends classical vehicle routing in that the cost of executing aset of high-level tasks can vary significantly in time and costaccording to the low-level paths selected. It extends classi-cal motion planning in that each path must minimize costwhile also respecting temporal constraints, including thoseimposed by the agent’s other tasks and the tasks assigned toother agents. Furthermore, the problem is a subtask withinan interactive system and therefore must operate within se-vere time constraints. We present an approach to the problembased on a combination of tabu search, linear programming,and heuristic search. We evaluate our planner on represen-tative problem instances and find that its performance meetsthe demanding requirements of our application. These resultsdemonstrate how integrating multiple diverse techniques cansuccessfully solve challenging real-world planning problemsthat are beyond the reach of any single method.