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This study investigates the coordinated control problem of Euler&#8722;Lagrange systems with model uncertainties in environments containing obstacles when escorting a target. Using an outer&#8722;inner loop control structure, a null-space-based behavioral (NSB) control architecture was proposed in the outer loop considering obstacles. This architecture generates the desired velocity for the inner loop. The adaptive proportional derivative sliding mode control (APD-SMC) law was applied to the inner loop to ensure fast convergence and robustness. All the robots were distributed around the target evenly and escorted the target at a specified distance while avoiding obstacles in a <inline-formula> <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>&#8722;</mo> </mrow> </semantics> </math> </inline-formula> dimensional space (where <inline-formula> <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>&#8805;</mo> <mn>2</mn> </mrow> </semantics> </math> </inline-formula> is a positive integer). Stability and convergence analyses were conducted rigorously using a Lyapunov-based approach. The simulation results of three scenarios verified the effectiveness and high-precision performance of the proposed control algorithm compared to that of the adaptive sliding mode control (ASMC) in both two-dimensional and three-dimensional space. It is shown that all the robots can move into appropriate positions on the surface of a sphere/circle during an escort mission and reconfigure the formation automatically when an obstacle avoidance mission is active.