Toward Safety-Aware Informative Motion Planning for Legged Robots

TL;DR

This paper introduces SAFE-IIG, an integrated motion planning framework for legged robots that combines safety constraints, information gathering, and dynamic feasibility using CBFs, MPC, and stochastic maps.

Contribution

It presents SAFE-IIG, a novel algorithm that unifies safety, information gathering, and kinodynamic planning for legged robots in complex environments.

Findings

SAFE-IIG plans safe, feasible paths in dense environments.

The framework effectively balances exploration and safety constraints.

Simulation results demonstrate improved exploration efficiency.

Abstract

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.