BWave framework for coverage path planning in complex environment with energy constraint

As one of fundamental problems in robotics, coverage path planning (CPP) requires the robot path to cover the entire workspace which has been employed in several essential applications such as cleaning robots, land mine detector, lawnmowers and automated harvesters. Unlike most of existing studies considering the CPP problem under a unrealistic assumption of infinity energy, this paper takes the battery limitation of robots into account. This poses a significant challenge for enabling an efficient coverage path while satisfying the limited energy constraint, even in a priori known environment. Handling this challenge, we propose a BWave Framework that guides the robot to move following an improved Boustrophedon-like motion and a special area prioritization and especially, to return a charging station effectively before an exhausted energy. To that end, a weighted map is applied for recognizing the special areas, namely trap regions, and governing the robot to enter these fields in priority. Moreover, a return matrix, which forms the shortest-path tree from the charging station, is pre-computed to not only validate the energy requirement, but also speed up the calculation process of return and advance paths during the robot’s operation. We then evaluate BWave Framework extensively in various scenarios in both generated and real-life indoor maps datasets. The results show that compared to typical baseline methods, BWave Framework achieves the CPP solution at a significantly accelerated running time, namely 51.5 to 72.8 times lower for generated maps, and 44.8 to 255 times for real maps, while reducing the total path length by 2.4%–17.6% and by 2.9%–18.5%, respectively. Moreover, the proposed method also outperforms the baselines in terms of overlap rate, number of returns and accounts for a lower number of deadlocks. •This paper tackles coverage path planning with energy constraints.•It introduces BWave, a novel framework for complete coverage.•BWave has two phases: environment knowledge extracting and complete coverage.•BWave is evaluated in various scenarios and compared with state-of-the-art methods.•BWave performs well in environments with many trap areas.