Sanjeev Ramkumar Sudha, Lars Ivar Hatledal, Erlend M. Coates
原文摘要
Safe navigation for mobile robots in dynamic and uncertain environments requires planning paths that avoid collisions with other moving targets. Typically this involves forward propagating the robot motion and checking for collisions at sampled points in space and time. Such methods can miss collisions between sampled points and are not robust to uncertainty in the state estimates. We propose ColNav, a robust collision check formulation with geometric volumes. The key aspect of our approach is encoding the uncertainty in the targets' and the ego robot's positions and velocities into spacetime tubes, in two variants that consume bounded-set and Gaussian uncertainty descriptions, respectively. The tubes are organised in bounding volume hierarchies, so that a single tree traversal covers the entire horizon, rather than testing sampled time instants, and returns a robust geometric certificate together with an estimate of the time to collision. Static and dynamic obstacles share the same representation. Our approach reaches the goal with a 100% success rate under prediction model mismatch, where the best baseline reaches 65%, and maintains up to three times the clearance in dense traffic. We also validate real-time operation on a physical robot.
Sanjeev Ramkumar Sudha, Marija Popović, Erlend M. Coates
原文摘要
Autonomous surface vehicles (ASVs) offer an efficient solution for environmental cleanup as well as search and rescue operations in open waters. Targets in these settings drift continuously, so efficient search must balance exploration of unobserved regions with tracking of known targets. However, most target tracking and pursuit scenarios consider simple guidance behaviours and short-term predictions for decision-making. In this letter, we address the problem of search and capture of multiple drifting targets, such as litter, in marine environments, using an adaptive informative planning approach. A key aspect of our strategy is a spatiotemporal informative planning method based on model predictive path integral (MPPI) control, a sampling-based model predictive control method. Our planner balances search and tracking objectives with a cost design that couples spatiotemporal target drift predictions and exploration objectives. The planner directly generates kinematic-level commands by optimising continuous trajectories over long horizons. In the interception stage, we use pure pursuit guidance for physical capture of the targets. Simulations show that our planner captures up to 18% more targets than the strongest baseline, proving its efficiency for environmental cleanup. Finally, we validate our approach in field trials with an ASV.