COAD: Constant-Time Planning for Continuous Goal Manipulation with Compressed Library and Online Adaptation

In many robotic manipulation tasks, the robot repeatedly solves motion-planning problems that differ mainly in the location of the goal object and its associated obstacle, while the surrounding workspace remains fixed. Prior works have shown that leveraging experience and offline computation can accelerate repeated planning queries, but they lack guarantees of covering the continuous task space and require storing large libraries of solutions. In this work, we present COAD, a framework that provides constant-time planning over a continuous goal-parameterized task space. COAD discretizes the continuous task space into finitely many Task Coverage Regions. Instead of planning and storing solutions for every region offline, it constructs a compressed library by only solving representative root problems. Other problems are handled through fast adaptation from these root solutions. At query time, the system retrieves a root motion in constant time and adapts it to the desired goal using lightweight adaptation modules such as linear interpolation, Dynamic Movement Primitives, or simple trajectory optimization. We evaluate the framework on various manipulators and environments in simulation and the real world, showing that COAD achieves substantial compression of the motion library while maintaining high success rates and sub-millisecond-level queries, outperforming baseline methods in both efficiency and path quality. The source code is available at https://github.com/elpis-lab/CoAd.

Design of Planar Collision-free Trochoidal Paths for a Multi-robot Swarm

In the literature, a distributed consensus protocol by which a connected swarm of agents can generate artistic patterns in 2-dimensional space is proposed. Motivated by this protocol, in this paper, we design the parameters of this protocol for a 3-agent swarm of non-holonomic robots of finite size that results in the generation of periodic trochoidal trajectories that satisfy a set of geometric and speed constraints; this design also includes selecting the initial positions of the robots. This problem finds applications in persistent surveillance and coverage, guarding a region of interest, and target detection. While the trajectories may be self-intersecting, imposing geometric constraints i. eliminates collisions between robots; ii. ensures minimum and maximum separation distance between any robot and a fixed point, thus ensuring the robots are in communication range. Imposing speed constraints ensure that tracking these trajectories becomes feasible. It is also shown that robots can be injected to these paths at specific locations, in order to increase the refresh rate, without violating any of the geometric constraints. The designs are implemented in an indoor mobile robot platform.