Mobile robots are being used more and more in various fields of agriculture, industry, military as well as disaster relief. However, the robot's limited battery energy power has resulted in many failed missions. To utilize this limited energy of the robot, this paper focuses on solving the problem of multi-objective path planning for a robot that satisfies a free- collision constraint and tri-optimization objectives: path length, path safety, and smoothness of the path. An original environment decomposition method is proposed, which is inspired by wave propagation in physics. The working environment is divided into arcs with the same center. Then, an Adaptive Tri-Objective Particle Swarm Optimization algorithm (denoted, ATOPSO) that combines the advantages between the multi-objective Particle Swarm Optimization and the Differential Evolution, is also introduced to solve the multi-objective problem above. The performance of the proposed decomposition method and the path planning algorithm ATOPSO is compared to existing methods and experimented with various scenarios and data. Obtained results show that our proposed method is more effective, it can solve the multi-objective problem in many complex environments including obstacles of different shapes.