See also:Hierarchical task and motion planning in the now. IEEE International Conference on Robotics and Automation (ICRA), 2011.Planning in the Know: Hierarchical belief-space task and motion planningIntegrated robot task and motion planning in belief spaceIntegrated Robot Task and Motion Planning in the NowDomain and Plan Representation for Task and Motion Planning in Uncertain DomainsIn: Brady et al., editor, Robot Motion: Planning and Control, pages 473-498, MIT Press
Autonomous robots are becoming increasingly popular and such systems has led to complex design and analysis which brings the necessity of validation and verification. In particular, symbolic robot motion planning based on formal methods is verifiably correct. It is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence in an adaptive fashion. On the other hand, extant works in human-robot interaction (HRI) often lack quantitative models and real-time analytical approaches. Here, we summarize our recent works on symbolic robot motion planning with human-in-the-loop as a step toward addressing these challenges. We specially focus on human trust in autonomous robots and embed trust analysis into the symbolic robot motion planning.
Deploying fleets of autonomous robots in real-world applications requires addressing three problems: motion planning, coordination, and control. Application-specific features of the environment and robots often narrow down the possible motion planning and control methods that can be used. This paper proposes a lightweight coordination method that implements a high-level controller for a fleet of potentially heterogeneous robots. Very few assumptions are made on robot controllers, which are required only to be able to accept set point updates and to report their current state. The approach can be used with any motion planning method for computing kinematically-feasible paths. Coordination uses heuristics to update priorities while robots are in motion, and a simple model of robot dynamics to guarantee dynamic feasibility. The approach avoids a priori discretization of the environment or of robot paths, allowing robots to "follow each other" through critical sections. We validate the method formally and experimentally with different motion planners and robot controllers, in simulation and with real robots.
If you've ever seen a live robot manipulation demo, you've almost certainly noticed that the robot probably spends a lot of time looking like it's not doing anything. It's tempting to say that the robot is "thinking" when this happens, and that might even be mostly correct: odds are that you're watching some poor motion-planning algorithm try and figure out how to get the robot's arm and gripper to do what it's supposed to do without running into anything. This motion planning process is both one of the most important skills a robot can have (since it's necessary for robots to "do stuff"), and also one of the most time and processor intensive. At the RSS 2016 conference this week, researchers from the Duke Robotics group at Duke University in Durham, N.C., are presenting a paper about "Robot Motion Planning on a Chip," in which they describe how they can speed up motion planning by three orders of magnitude while using 20 times less power. How? Rather than using general purpose CPUs and GPUs, they instead developed a custom processor that can run collision checking across an entire 3D grid all at once.