Toward Safety-Aware Informative Motion Planning for Legged Robots
This work addresses safety-aware exploration for legged robots, which is an incremental improvement in robotic motion planning for specific applications.
The paper tackled the problem of motion planning for legged robots that balances information gathering with safety, proposing SAFE-IIG, an integrated framework that uses Control Barrier Functions and Model Predictive Control to ensure safety and dynamic feasibility while exploring dense stochastic maps, with simulation results demonstrating its ability to plan safe and feasible paths.
This paper reports on developing an integrated framework for safety-aware informative motion planning suitable for legged robots. The information-gathering planner takes a dense stochastic map of the environment into account, while safety constraints are enforced via Control Barrier Functions (CBFs). The planner is based on the Incrementally-exploring Information Gathering (IIG) algorithm and allows closed-loop kinodynamic node expansion using a Model Predictive Control (MPC) formalism. Robotic exploration and information gathering problems are inherently path-dependent problems. That is, the information collected along a path depends on the state and observation history. As such, motion planning solely based on a modular cost does not lead to suitable plans for exploration. We propose SAFE-IIG, an integrated informative motion planning algorithm that takes into account: 1) a robot's perceptual field of view via a submodular information function computed over a stochastic map of the environment, 2) a robot's dynamics and safety constraints via discrete-time CBFs and MPC for closed-loop multi-horizon node expansions, and 3) an automatic stopping criterion via setting an information-theoretic planning horizon. The simulation results show that SAFE-IIG can plan a safe and dynamically feasible path while exploring a dense map.